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Claudia DIAMANTINI

Pubblicazioni

Claudia DIAMANTINI

 

157 pubblicazioni classificate nel seguente modo:

Nr. doc. Classificazioni
85 4 Contributo in Atti di Convegno (Proceeding)
43 2 Contributo in Volume
27 1 Contributo su Rivista
1 3 Libro
1 7 Curatele
Anno Risorsa
2019 Defining a data-driven maintenance policy: an application to an oil refinery plant
INTERNATIONAL JOURNAL OF QUALITY AND RELIABILITY MANAGEMENT
Autore/i: Antomarioni, Sara; Bevilacqua, Maurizio; Potena, Domenico; Diamantini, Claudia
Classificazione: 1 Contributo su Rivista
Abstract: Purpose – The purpose of this paper is developing a data-driven maintenance policy through the analysis of vast amount of data and its application to an oil refinery plant. The maintenance policy, analyzing data regarding sub-plant stoppages and components breakdowns within a defined time interval, supports the decision maker in determining whether it is better to perform predictive maintenance or corrective interventions on the basis of probability measurements. Design/methodology/approach – The formalism applied to pursue this aim is association rules mining since it allows to discover the existence of relationships between sub-plant stoppages and components breakdowns. Findings – The application of the maintenance policy to a three-year case highlighted that the extracted rules depend on both the kind of stoppage and the timeframe considered, hence different maintenance strategies are suggested. Originality/value – This paper demonstrates that data mining (DM) tools, like association rules (AR), can provide a valuable support to maintenance processes. In particular, the described policy can be generalized and applied both to other refineries and to other continuous production systems.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/262526 Collegamento a IRIS

2019 Social information discovery enhanced by sentiment analysis techniques
FUTURE GENERATION COMPUTER SYSTEMS
Autore/i: Diamantini, Claudia; Mircoli, Alex; Potena, Domenico; Storti, Emanuele
Classificazione: 1 Contributo su Rivista
Abstract: In recent years, the massive diffusion of social networks has made available a large amount of user-generated content, for the most part in the form of textual data that contain people's thoughts and emotions about a great variety of topics. In order to exploit these publicly available information, in this work we introduce a social information discovery system which elaborates simultaneously over more-than-one social network in an integrated scenario. The system is designed to ensure flexibility and scalability, thus enabling for (near-)real-time analysis even in case of high rates of content's creation and large amounts of heterogeneous data. Furthermore, a noise detection technique ensures a high relevance of analyzed posts/tweets to the domain of interest. We also propose a lexicon-based sentiment analysis algorithm to extract and measure users’ opinion, in order to support collaboration and open innovation. Polysemous words and negations are typically challenging for lexicon-based approaches: for this reason, we introduce both a word sense disambiguation algorithm and a negation handling technique. Experiments on several datasets have proven that the combined use of both techniques improves the classification accuracy on 3-class sentiment analysis.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/256922 Collegamento a IRIS

2019 A predictive association rule-based maintenance policy to minimize the probability of breakages: application to an oil refinery
INTERNATIONAL JOURNAL, ADVANCED MANUFACTURING TECHNOLOGY
Autore/i: Antomarioni, Sara; Pisacane, Ornella; Potena, Domenico; Bevilacqua, Maurizio; Ciarapica, Filippo Emanuele; Diamantini, Claudia
Classificazione: 1 Contributo su Rivista
Abstract: Effective maintenance policies can support companies to deal with process interruptions and consequently, to prevent significant profit losses. Moreover, the proliferation of structured and unstructured data due to production plants validates the application of knowledge discovery in databases techniques to increase processes’ reliability. In this paper, an innovative maintenance policy is proposed. It aims at both predicting components breakages through association rule mining and determining the optimal set of components to repair in order to improve the overall plant’s reliability, under time and budget constraints. An experimental campaign is carried out on a real-life case study concerning an oil refinery plant. Finally, numerical results are discussed considering different blockage categories and number of components and by varying some significant input parameters.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266285 Collegamento a IRIS

2019 Multi-Dimensional Contexts for Querying IoT Networks
Proceedings of the 27th Italian Symposium on Advanced Database Systems
Autore/i: Diamantini, C.; Antonino, Nocera; Potena, D.; Storti, E.; Ursino, D.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/265546 Collegamento a IRIS

2019 BEyOND, A NEW TOOL FOR SAPROPEL S1 STUDIES IN THE MEDITERRANEAN SEA
ALPINE AND MEDITERRANEAN QUATERNARY
Autore/i: AMEZCUA BUENDÍA, Rubén; Diamantini, Claudia; Potena, Domenico; Negri, Alessandra
Classificazione: 1 Contributo su Rivista
Abstract: Science advances and, with it, the storage of large amounts of data. The need to use this data efficiently, quickly and safely is possible thanks to the Big Data Analytics (BDA) that allows us to store and relate data in order to obtain new knowledge. In this paper we present and explain how we constructed the new database, “BEyOND”, that provides a wide variety of organized and standardized paleoproxies relative to the past 20.000 years of Mediterranean Sea history. BEyOND makes available to all researchers the possibility to extract and analyze data. We focused on a specific interval of time corresponding to the deposition of the most recent sapropel (S1) and the potential uses offered by the tool. BEyOND contains 126 sediment cores data from 79 scientific papers and a total of 1.678 different proxy related data that have been categorized in: geochemistry, isotopes, pollen, sediment grain size, coccolithophore, dinoflagellate and foraminifera. Our work highlights the development of a new methodology to correlate data, including the cases where data regarding the pre-cise age control for each core was missing. It highlights as well the potential of using data analytics to extract hidden patterns and new knowledge also in the field of paleoceanography.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/269085 Collegamento a IRIS

2019 Find the Right Peers: Building and Querying Multi-IoT Networks Based on Contexts
Proc. of the International Conference on Flexible Query Answering Systems (FQAS'19)
Autore/i: Diamantini, C.; Nocera, A.; Potena, D.; Storti, E.; Ursino, D.
Editore: Springer, Cham
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: With the evolution of the features smart devices are equipped with, the IoT realm is becoming more and more intertwined with people daily-life activities. This has, of course, impacts in the way objects are used, causing a strong increase in both the dynamism of their contexts and the diversification of their objectives. This results in an evolution of the IoT towards a more complex environment composed of multiple overlapping networks, called Multi-IoTs (MIoT). The low applicability of classical cooperation mechanisms among objects leads to the necessity of developing more complex and refined strategies that take the peculiarity of such a new environment into consideration. In this paper, we address this problem by proposing a new model for devices and their contexts following a knowledge representation approach. It borrows ideas from OLAP systems and leverages a multidimensional perspective by defining dimension hierarchies. In this way, it enables roll-up and drill-down operations on the values of the considered dimensions. This allows for the design of more compact object networks and the definition of new strategies for the retrieval of relevant devices.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266832 Collegamento a IRIS

2018 Discovering Process Models of Activities of Daily Living from Sensors
Business Process Management Workshops. BPM 2017
Autore/i: Cameranesi, Marco; Diamantini, Claudia; Potena, Domenico
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: In recent years, more and more effort was put in the design and development of smart environments, which are aimed at improving the life quality of people, providing users with advanced services supporting them during their daily activities. In order to implement these services, smart environments are equipped with several sensors that continuously monitor the activities performed by a user. Sensor data are activation sequences and could be seen as the execution of a process representing daily user behaviors and performed activities. In this paper we propose a methodology, which exploit Process Mining techniques to discover both the daily behavior model and macro activities models. The former represents the “standard” behavior of the user in the form of a process model. The latter is a set of process models representing the flow of sensors activations when given tasks or macro activities are performed. A real-world case study is introduced to empirically show the efficacy of the proposed methodology.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/252536 Collegamento a IRIS

2018 Discovering anomalous frequent patterns from partially ordered event logs
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
Autore/i: Genga, Laura; Alizadeh, Mahdi; Potena, Domenico; Diamantini, Claudia; Zannone, Nicola
Classificazione: 1 Contributo su Rivista
Abstract: Conformance checking allows organizations to compare process executions recorded by the IT system against a process model representing the normative behavior. Most of the existing techniques, however, are only able to pinpoint where individual process executions deviate from the normative behavior, without considering neither possible correlations among occurred deviations nor their frequency. Moreover, the actual control-flow of the process is not taken into account in the analysis. Neglecting possible parallelisms among process activities can lead to inaccurate diagnostics; it also poses some challenges in interpreting the results, since deviations occurring in parallel behaviors are often instantiated in different sequential behaviors in different traces. In this work, we present an approach to extract anomalous frequent patterns from historical logging data. The extracted patterns can exhibit parallel behaviors and correlate recurrent deviations that have occurred in possibly different portions of the process, thus providing analysts with a valuable aid for investigating nonconforming behaviors. Our approach has been implemented as a plug-in of the ESub tool and evaluated using both synthetic and real-life logs.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/254377 Collegamento a IRIS

2018 An approach to extracting thematic views from highly heterogeneous sources of a data lake
Proceedings of the 26th Italian Symposium on Advanced Database Systems (SEBD 2018)
Autore/i: Diamantini, C.; Lo Giudice, P.; Musarella, L.; Potena, D.; Storti, E.; Ursino, D.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In the last years, data lakes are emerging as an effective and efficient support for information and knowledge extraction from a huge amount of highly heterogeneous and quickly changing data sources. Data lake management requires the definition of new techniques, very different from the ones adopted for data warehouses in the past. One of the main issues to address in this scenario consists in the extraction of thematic views from the (very heterogeneous and generally unstructured) data sources of a data lake. In this paper, we propose a new network-based model to uniformly represent structured, semi-structured and unstructured sources of a data lake. Then, we present a new approach to, at least partially, “structure” unstructured data. Finally, we define a technique to extract thematic views from the sources of a data lake, based on similarity and other semantic relations among the metadata of data sources
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/258751 Collegamento a IRIS

2018 Multidimensional Query Reformulation with Measure Decomposition
INFORMATION SYSTEMS
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Classificazione: 1 Contributo su Rivista
Abstract: Measurement and comparison of performances in networked organisations is particularly critical because of heterogeneity and sparsity of data. In particular, each organization is autonomous in the definitions of which measures to use and their calculation formulas, i.e. the mathematical expressions stating how a measure is calculated from others. Hence, full integration of data marts requires a reconciliation among such heterogeneous definitions in order to support evaluation of cross-organizations performances and to produce meaningful comparisons. To address this issue, this paper proposes (1) an extension of the traditional multidimensional model by taking into account the explicit representation of the semantics for measure formulas, and, on the top of this model, (2) a novel query reformulation approach for a scenario of federated data warehouses. The approach exploits both aggregation and, unlike traditional approaches, measure decomposition through the calculation of measure formulas. This extends usual features of query rewriting based on views, allowing to overcome heterogeneities at measure level among data mart schemas and enabling meaningful comparisons among values of different autonomous data marts. A formalization of the rewriting algorithm is proposed, together with a computational analysis, proofs of correctness and termination, and an evaluation of effectiveness that shows how the approach can lead to a significant increase in the capability of integrating indicators to answer queries in a federated scenario.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/258615 Collegamento a IRIS

2018 A Big Data Framework for Analysis of Traffic Data in Italian Highways
Proceedings of 24th International Symposium on Methodologies for Intelligent Systems
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Editore: Springer
Classificazione: 2 Contributo in Volume
Abstract: The analysis of traffic data can provide decision-makers with invaluable information. Despite the availability of methodologies specifically oriented to processing this kind of data and extract knowledge from them, few tools provide a rich set of functionalities tailored to traffic analysis in large-scale, stream-like contexts. In this paper we aim to fill this gap, by introducing an exploratory framework supporting the analysis of massive stream traffic data by either OLAP-like exploration or by resorting to advanced data mining techniques.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/260767 Collegamento a IRIS

2018 Comparison of City Performances Through Statistical Linked Data Exploration
Cloud Infrastructures, Services, and IoT Systems for Smart Cities
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Editore: Springer, Cham
Classificazione: 2 Contributo in Volume
Abstract: The capability to perform comparisons of city performances can be an important guide for stakeholders to detect strengths and weaknesses and to set up strategies for future urban development. Today, the rise of the Open Data culture in public administrations is leading to a larger availability of statistical datasets in machine-readable formats, e.g. the RDF Data Cube. Although these allow easier data access and consumption, appropriate evaluation mechanisms are still needed to perform proper comparisons, together with an explicit representation of how statistical indicators are calculated. In this work, we discuss an approach for analysis and comparison of statistical Linked Data which is based on the formal and mathematical representation of performance indicators. Relying on this knowledge model, a set of logic-based services are able to support novel typologies of comparison of different resources.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/251567 Collegamento a IRIS

2018 A new metadata model to uniformly handle heterogeneous data lake sources
New Trends in Databases and Information Systems. ADBIS 2018.
Autore/i: Diamantini, C.; Lo Giudice, P.; Musarella, L.; Potena, D.; Storti, E.; Ursino, D.
Editore: Springer, Cham
Classificazione: 2 Contributo in Volume
Abstract: Metadata have always played a key role in favoring the cooperation of heterogeneous data sources. This role has become much more crucial with the advent of data lakes, in which case metadata represent the only possibility to guarantee an effective and efficient management of data source interoperability. For this reason, the necessity to define new models and paradigms for metadata representation and management appears crucial in the data lake scenario. In this paper, we aim at addressing this issue by proposing a new metadata model well suited for data lakes. Furthermore, to give an idea of its capabilities, we present an approach that leverages it to “structure” unstructured sources and to extract thematic views from heterogeneous data lake sources.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/258796 Collegamento a IRIS

2017 APD tool: Mining Anomalous Patterns from Event Logs
Proceedings of the BPM Demo Track and BPM Dissertation Award co-located with 15th International Conference on Business Process Management (BPM 2017)
Autore/i: Genga, Laura; Alizadeh, Mahdi; Potena, Domenico; Diamantini, Claudia; Zannone, Nicola
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: A main challenge of today’s organizations is the monitoring of their processes to check whether these processes comply with process models specifying the prescribed behavior. Deviations from the prescribed behavior can represent either legitimate work practices not described by the models, which highlight the need of improving it to better reflect the reality, or malicious behaviors representing, for instance, security breaches and frauds. In this paper, we present a tool designed to extract anomalous patterns representing recurrent deviations, together with their correlations, from historical logging data. The tool is targeted to researchers and practitioners in business process and security domains, with background in process mining
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/250783 Collegamento a IRIS

2017 Students' careers analysis: A process mining approach
Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics
Autore/i: Cameranesi, Marco; Diamantini, Claudia; Genga, Laura; Potena, Domenico
Editore: ACM
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: University degrees are typically organized in courses with prerequisites among them. If prerequisite are not mandatory, students are left free to attend courses and take exams in almost any order. While favoring flexible organization of the work by students, this practice can also lead to unstructured learning practices and to performance issues. In this paper we propose to take a process-oriented view of students' careers and analyze them by process mining techniques. Results provide us with some evidence of typical patterns followed by students and of the advantages of adopting structured learning practices.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/250574 Collegamento a IRIS

2017 Semantic Process Mining for Ambient Assisted Living
Proceedings of the International Workshop on Analysis of Biometric Parameters to detect relationship between stress and sleep quality
Autore/i: Genga, Laura; Potena, Domenico; Storti, Emanuele; Cameranesi, Marco; Diamantini, Claudia
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Ambient Assisted Living (AAL) systems are aimed to assist elderly people and enhance their autonomy, by monitoring their health, supporting their daily activities and so on. AAL tools are employed for several purposes, e.g. medication management, social isolation prevention, fall detection. In this work, we focus on the analysis of daily activities of monitored people and, in particular, on the detection of common patterns of daily activities. These patterns allow to understand the habitual behavior of monitored people, that is a valuable knowledge both in order to enhance the support provided to elders in performing their activities and to be able to quickly detect unexpected or dangerous situations. However, AAL tools usually return data at a very low level of detail, analyzing which too detailed patterns are inferred, which are of scarce support for the human analyst. To address this issue, in this work, we discuss the application of a combined methodology based on the combination of semantic techniques and multidimensional analysis paradigm to allow the analyst to switch to the desired level of granularity and to consider different process perspective, thus enhancing the analysis.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/249509 Collegamento a IRIS

2017 Data Semantics Meets Knowledge Discovery in Databases
A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Classificazione: 2 Contributo in Volume
Abstract: In the last 30 years two important fields were born and have developed rapidly: knowledge discovery and knowledge management based on semantics. In the present chapter we provide an overview of the interlinks between them, taking the perspective of the evolution of systems and platforms supporting knowledge discovery with the help of data semantics.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/249379 Collegamento a IRIS

2017 Workload-Driven Database Optimization for Cloud Applications
Proceeding of the 2017 Internation Conference on High Performance Computing & Simulation (HPCS)
Autore/i: Diamantini, Claudia; Mircoli, Alex; Moretti, Matteo; Potena, Domenico; Tempera, Valentina
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The performance of modern data-intensive applications is closely related to the speed of data access. However, a physical database optimization by design is often infeasible, due to the presence of large databases and time-varying workloads. In this paper we introduce a novel methodology for physical database optimization which allows for a quick and dynamic selection of indexes through the analysis of database logs. The application of the technique to cloud applications, which use a pay-per-use model, results in immediate cost savings, due to the presence of elastic resources. In order to demonstrate the effectiveness of the approach, we present the case study Nuvola, a SaaS multitenant application for schools that is characterized by heavy workloads. Experimental results show that the proposed technique leads to a 52.1% reduction of query execution time for a given workload. A comparative analysis of database performance before and after the optimization is also performed through a M/M/1 queue model and the results are discussed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/250283 Collegamento a IRIS

2017 An ontology-based framework to support performance monitoring in public transport systems
TRANSPORTATION RESEARCH. PART C, EMERGING TECHNOLOGIES
Autore/i: Benvenuti, Filippo; Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Classificazione: 1 Contributo su Rivista
Abstract: Managers of public transport systems have been facing for years the strategic challenge of maintaining high quality of transport services to improve the mobility of citizens, while reducing costs and ensuring safety and low environmental impact. A well-established way to evaluate the performance achieved by the system or by specific activities is to monitor Key Performance Indicators (KPI). However, existing management systems, which refer to flexible yet large and complex data models, provide a limited support to define and select relevant KPIs for the objectives at hand, and even the identification of whether and how the data model is capable to achieve a certain informative need is a critical and time-consuming task. This work is aimed to propose a framework to ease the development of a monitoring system in the public transport domain. The approach is based on the ontological representation of all the knowledge regarding indicators and their formulas, business objectives, dimension analysis and their relation with the Transmodel, the European reference data model for public transport information systems. On its top, a reasoning framework provides logic functionalities to interactively support designers in a set of common design tasks: the choice of the most suitable indicators for the performance monitoring needs at hand, the definition of new indicators and the identification of the minimal set of Transmodel modules needed to calculate them. A case study is included to discuss these applications, while an evaluation shows the feasibility of the approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/248761 Collegamento a IRIS

2017 Discovering Mobility Patterns of Instagram Users through Process Mining Techniques
Proceedings of the 2017 IEEE International Conference on Information Reuse and Integration (IRI)
Autore/i: Diamantini, Claudia; Genga, Laura; Marozzo, Fabrizio; Potena, Domenico; Trunfio, Paolo
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Every day a huge amount of data is generated by users of social media platforms, like Facebook, Twitter and so on. Analyzing data posted by people interested in a given topic or event allows inferring patterns and trends about people behaviors on a very large scale. These posts are often geotagged, this way enabling mobility pattern analysis. In this work, we investigate the use of Process Mining techniques to support the discovery and the analysis of mobility patterns of social media users. We discuss the results obtained analyzing posts of Instagram users who visited EXPO 2015, the Universal Exposition hosted in Milan, Italy, from May to October 2015.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/251880 Collegamento a IRIS

2017 Building detection in multi-source aerial data with imbalanced training samples: an approach based on the Bayesian Vector Quantizer
INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION
Autore/i: Benvenuti, Filippo; Mancini, Adriano; Potena, Domenico; Diamantini, Claudia; Frontoni, Emanuele; Zingaretti, Primo
Classificazione: 1 Contributo su Rivista
Abstract: The problem of building detection in multi-source aerial data has a large variety of applications from map updating to the detection of illegal construction. The development of a capability to integrate multispectral and LiDAR technologies has been proved to be the most effective strategy in dealing with the problem. An automated and combined approach enables such joint capabilities to process data, reducing human effort that is usually limited to the creation of training sets. This aspect plays a key role in getting accurate results and it is central to dealing with the problem of an imbalanced data set. We tailor the Bayesian Vector Quantizer algorithm (BVQ) to the problem of building detection from multi-source high-resolution aerial data like LiDAR with a focus on the imbalanced data set problem. The result is a methodology that is optimised towards the solution of strongly imbalanced problems where noise is present and where the number of training samples for buildings over classes like trees, land and grass is one order of magnitude lower. The formulation is compared with other well-adopted approaches which highlight the relative strengths of the BVQ approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/248350 Collegamento a IRIS

2017 Tell the student: evidence-based advantages of prerequisites
Proceedings of 25th Italian Symposium on Advanced Database Systems
Autore/i: Cameranesi, Marco; Diamantini, Claudia; Genga, Laura; Potena, Domenico
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: When bachelor and master degrees were introduced in the Italian system, the Engineering Faculty of Università Politecnica delle Marche dropped mandatory prerequisites between modules from its degrees. Since then, we periodically witness intense debates among professors, who think this led to unstructured learning practices with consequent performance issues, and students who are concerned of an overly constrained course of study. This paper briefly describes an ex-post analysis of the carriers of students enrolled in the bachelor degree in Computer and Automation Engineering based on process mining techniques, the resulting evidence about typical patterns followed by students, and sketches possible actions that can be put in practice to support students without constraining their course of study.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/249421 Collegamento a IRIS

2017 Big data analytics methodologies applied at energy management in industrial sector: A case study
INTERNATIONAL JOURNAL OF RF TECHNOLOGIES: RESEARCH AND APPLICATIONS
Autore/i: Bevilacqua, Maurizio; Ciarapica, Filippo Emanuele; Diamantini, Claudia; Potena, Domenico
Classificazione: 1 Contributo su Rivista
Abstract: In this work, a framework is developed to integrate IoT-based energy management and company’s existing information systems. This framework is a multi-layer model that includes three layers: 1) data collection layer, 2) data management layer and 3) data analytics layer. In order to test the proposed approach and assess its impact on improving energy efficiency, a pilot study was carried out in an Italian manufacturing company. Several smart meters have been installed at machine level to collect energy consumption data in real time, and then this data have been analyzed and provided to decision makers to improve energy efficiency by integrating them in production management decisions. When a company aims at analyzing the energy characteristics of its production system, data provided by different sources and geographically dispersed repositories must be taken into consideration. These characteristics bring several problems to develop a data analytic architecture. In this paper, we propose a data analytic model for IoT, in order to integrate the data collected from different sources and to improve energy-aware decision-making. Improving the overall equipment effectiveness of machine tools will improve resource-efficiency and productivity in manufacturing and support the development of smart factories from an energy point of view.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/251270 Collegamento a IRIS

2017 Subgraph Mining for Anomalous Pattern Discovery in Event Logs
New Frontiers in Mining Complex Patterns: 5th International Workshop, Revised Selected Papers
Autore/i: Genga, Laura; Potena, Domenico; Martino, Orazio; Alizadeh, Mahdi; Diamantini, Claudia; Zannone, Nicola
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: Conformance checking allows organizations to verify whether their IT system complies with the prescribed behavior by comparing process executions recorded by the IT system against a process model (representing the normative behavior). However, most of the existing techniques are only able to identify low-level deviations, which provide a scarce support to investigate what actually happened when a process execution deviates from the specification. In this work, we introduce an approach to extract recurrent deviations from historical logging data and generate anomalous patterns representing high-level deviations. These patterns provide analysts with a valuable aid for investigating nonconforming behaviors; moreover, they can be exploited to detect high-level deviations during conformance checking. To identify anomalous behaviors from historical logging data, we apply frequent subgraph mining techniques together with an ad-hoc conformance checking technique. Anomalous patterns are then derived by applying frequent items algorithms to determine highly-correlated deviations, among which ordering relations are inferred. The approach has been validated by means of a set of experiments.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/250052 Collegamento a IRIS

2017 Exploiting Mathematical Structures of Statistical Measures for Comparison of RDF Data Cubes
Big Data Analytics and Knowledge Discovery. DaWaK 2017
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: A growing number of public institutions all over the world has recently started to publish statistical data according to the RDF Data Cube vocabulary, as open and machine-readable Linked Data. Although this approach allows easier data access and consumption, appropriate mechanisms are still needed to perform proper comparisons of statistical data. Indeed, the lack of an explicit representation of how statistical measures are calculated still hinders their interpretation and use. In this work, we discuss an approach for the analysis and schema-level comparison of distributed data cubes, which is based on the formal and mathematical representation of measures. Relying on a knowledge model, we present and evaluate a set of logic-based functionalities able to support novel typologies of comparison of different data cubes.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/250572 Collegamento a IRIS

2017 Analytics for citizens: A linked open data model for statistical data exploration
CONCURRENCY AND COMPUTATION
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Classificazione: 1 Contributo su Rivista
Abstract: A growing number of public institutions all over the world have recently started to make government statistical data available in open formats, thus enhancing transparency and accountability, stimulating innovation, and promoting civic awareness and engagement. Integration issues related to fragmentation and heterogeneity of these datasets can be partially addressed by referring to the Linked Data approach, which also enables easier access and consumption by users. However, the lack of an explicit representation of how statistical indicators are calculated still hinders their interpretation, and hence the development of applications and services especially useful for citizens, who do not have full knowledge and control over the underlying data and analysis models. In the present work, we discuss an approach to ease the interaction of communities of citizens with statistical Linked Open Data. We define a model and a set of services allowing people to recognize the mathematical structure of statistical indicators, improving in this way user awareness of the meaning of indicators and their mutual relations. Through such services, it is possible to enable interactive browsing of indicator formulas and novel typologies of data exploration, including dynamic computation of indicators not explicitly stored and comparison of different Linked Data resources.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/248520 Collegamento a IRIS

2016 Extended drill-down operator: Digging into the structure of performance indicators
CONCURRENCY AND COMPUTATION
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Classificazione: 1 Contributo su Rivista
Abstract: Performance measurement is the subject of interdisciplinary research on information systems, organizational modeling and decision support systems. The data cube model is usually adopted to represent performance indicators (PI) and enable flexible analysis, visualization and reporting. However, the major obstacles against effective design and management of PI monitoring systems are related to the facts that PIs are complex objects with an aggregate/compound nature. This often leads to unawareness of indicator semantics as well as of dependencies among indicators. In this work, we propose to enrich the data cube model with the formal description of the structure of an indicator given in terms of its algebraic formula and aggregation function. Such a model enables the definition of a novel operator, namely indicator drill-down, which relies on formula manipulation functionalities and reasoning. Like the usual drill-down, this operator increases the detail of a measure of the data cube by expanding an indicator into its components. Thus, the two notions of drill-down are integrated, allowing a novel way of data exploration. As a proof-of-concept, an implementation of the approach is presented. The evaluation of the implementation on real and synthetic scenarios enlightens the effectiveness and the efficiency of the approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/228381 Collegamento a IRIS

2016 Behavioral process mining for unstructured processes
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico
Classificazione: 1 Contributo su Rivista
Abstract: Real world applications provide many examples of unstructured processes, where process execution is mainly driven by contingent decisions taken by the actors, with the result that the process is rarely repeated exactly in the same way. In these cases, traditional Process Discovery techniques, aimed at extracting complete process models from event logs, reveal some limits. In fact, when applied to logs of unstructured processes, Process Discovery techniques usually return complex, “spaghetti-like” models, which usually provide limited support to analysts. As a remedy, in the present work we propose Behavioral Process Mining as an alternative approach to enlighten relevant subprocesses, representing meaningful collaboration work practices. The approach is based on the application of hierarchical graph clustering to the set of instance graphs generated by a process. We also describe a technique for building instance graphs from traces. We assess advantages and limits of the approach on a set of synthetic and real world experiments.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/231388 Collegamento a IRIS

2016 Subgraph Mining for Anomalous Pattern Discovery in Event Logs
Proceedings of 5th International Workshop on New Frontiers in Mining Complex Patterns
Autore/i: Genga, Laura; Potena, Domenico; Martino, Orazio; Alizadeh, Mahdi; Diamantini, Claudia; Zannone, Nicola
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/249442 Collegamento a IRIS

2016 Building Instance Graphs for Highly Variable Processes
EXPERT SYSTEMS WITH APPLICATIONS
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico; van der Aalst, Wil
Classificazione: 1 Contributo su Rivista
Abstract: Organizations increasingly rely on business process analysis to improve operations performance. Process Mining can be exploited to distill models from real process executions recorded in event logs, but existing techniques show some limitations when applied in complex domains, where human actors have high degree of freedom in the execution of activities thus generating highly variable processes instances. The present paper contributes to the research on Process Mining in highly variable domains, focusing on the generation of process instance models (in the form of Instance Graphs) from simple event logs. The novelty of the approach is in the exploitation of filtering Process Discovery techniques coupled with repairing, which allows obtaining accurate models for any instance variant, even for rare ones. It is argued that this provides the analyst with a more complete and faithful knowledge of a highly variable process, where no process execution can be really targeted as “wrong” and hence overlooked. The approach can also find application in more structured domains, in order to obtain accurate models of exceptional behaviors. The quality of generated models will be assessed by suitable metrics and measured in empirical experiments enlightening the advantage of the approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/234726 Collegamento a IRIS

2016 Collaborative Building of a Shared Library of Performance Indicators
Proceedings of the 24th Italian Symposium on Advanced Database Systems
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper, which is an extended abstract of [1], we introduce a semantic framework for representing Performance Indicators, that supports the construction and maintenance of a minimal and consistent dictionary. We discuss how the logical representation of the formulas used for the calculation of PIs enables automatic reasoning capable to check equivalence of PIs and consistency of the dictionary. Upon these services, a web application has been implemented for collaborative construction and maintenance of the dictionary
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/235846 Collegamento a IRIS

2016 Understanding Knowlegde-Intensive Processes: from Traces to Instance Graphs
Proceedings of the 2016 International Conference on High Performance Computing & Simulation (HPCS 2016)
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Enterprise information systems, while support daily activities, typically collect data on executed processes in event logs. These data describe the temporal sequence in which activities were carried out, hiding possible parallelism and other control flows. Representing the structure of each process execution in the form of an Instance Graph, enables managers to discover valuable knowledge on enterprise behaviors. In this work, we describe BIG4ProM, a tool which implements the Building Instance Graph (BIG) algorithm. BIG4ProM exploits filtering Process Discovery algorithms implemented in ProM in order to return the set of instance graphs related to the given event log. The plug-in is conceived to support both expert and standard users.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/236256 Collegamento a IRIS

2016 Semantic-driven Goal-Oriented Development of AAL Environments
proceedings of the 2016 International Conference on Collaboration Technologies and Systems
Autore/i: Cameranesi, Marco; Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In recent years, a multitude of assistive technologies have been devised to enhance people’s capabilities by means of environments that are adaptive, sensitive and responsive to human needs. Following this approach, Ambient Assisted Living (AAL) technologies are developed to provide support especially to elderly people for prevention and recognition of medical threats, and improvement of well-being. Towards this direction, the aim of this paper is to introduce the principles of a goaloriented methodology devoted to support system designers in the development of AAL environments. In the methodology, AAL requirements are elicited, analysed and then formally represented in an ontology, which serves as a collaboratively built knowledge base. Here, high-level goals are described in terms of subgoals and tasks, that are then linked to corresponding measures and devices. On its top, logic-based reasoning functionalities provide means to retrieve explicit and hidden knowledge, as we show in two typical applications of the methodology, namely the development from scratch of an AAL environment starting from a set of high-level user requirements and the redesign of an existing implementation according to changed requirements.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/239030 Collegamento a IRIS

2016 A Negation Handling Technique for Sentiment Analysis
proceedings of the 2016 International Conference on Collaboration Technologies and Systems
Autore/i: Diamantini, Claudia; Mircoli, Alex; Potena, Domenico
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Traditional lexicon-based approaches for sentiment analysis are usually not able to model negation, as they do not provide proper techniques to identify the right negation window. In this work we address the problem of the automatic determination of the scope of negation and we present a negation handling algorithm based on dependency-based parse trees. The proposal is based on the use of grammatical relations among words to model a sentence, and hence to determine words that are affected by negation. The proposed algorithm has been coupled with a semantic disambiguation technique to identify the sentiment of a sentence. Experiments on different datasets have proven that our proposal improves the accuracy of the sentiment analysis. The proposed algorithm has been implemented as part of a Social Information Discovery system, which allows for an integrated near-real-time analysis of discussions from multiple social networks.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/239031 Collegamento a IRIS

2016 GoAAL: an Ontology for Goal-oriented Development of AAL Environments
Proceeedings of 2016 Symposium on Applied Computing
Autore/i: Cameranesi, Marco; Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Editore: ACM
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Ambient Assisted Living (AAL) systems are playing an important role in the modern society, by helping elderly people to live more independently and support daily activities. Knowledge models have been proposed in the past to describe AAL devices. However, models and methodologies capable to provide assistance to system designers during the development of an AAL environment are still missing. To this aim, in this work we propose an ontology to formally represents all relevant knowledge in the AAL domain ranging from goals to measures and sensors. On its top, a set of logic-based reasoning functions provides advanced support to the development process. In this way, starting from high-level goals the designer can easily retrieve which devices are needed to best meet the project specifications, leading to a cost-effective reduction of development time.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/235403 Collegamento a IRIS

2016 SemPI: A semantic framework for the collaborative construction and maintenance of a shared dictionary of performance indicators
FUTURE GENERATION COMPUTER SYSTEMS
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Classificazione: 1 Contributo su Rivista
Abstract: Collaboration at strategic level entails the sharing of Performance Indicators (PIs) to measure the achievement of common objectives and evaluate performances. PIs are synthetic measures calculated starting from transactional data. Given their compound nature, it is difficult to achieve an agreement on their definitions and heterogeneities arise that make sharing and exchange a difficult task. Semantic techniques can help to address these challenges by providing a common layer of formal definitions and automatic reasoning tools to maintain its consistency. In this paper, we present a novel semantic framework for representing Performance Indicators that supports the construction and maintenance of a minimal and consistent dictionary. The distinctive feature of the approach is the logical representation of formulas defining PIs, allowing to make algebraic relationships among indicators explicit, and to reason over these relationships to derive PI identity and equivalence and to enforce the overall consistency of the dictionary. We also present a web application implementing the framework for collaborative construction and maintenance of the dictionary. We provide experimental evidence of the efficiency and effectiveness of the approach on synthetic and real data.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/227771 Collegamento a IRIS

2016 A goal-oriented, ontology-based methodology to support the design of AAL environments
EXPERT SYSTEMS WITH APPLICATIONS
Autore/i: Diamantini, Claudia; Freddi, Alessandro; Longhi, Sauro; Potena, Domenico; Storti, Emanuele
Classificazione: 1 Contributo su Rivista
Abstract: One of the most critical issues in Ambient Assisted Living (AAL) is the design of systems that can evolve to meet the requirements of individuals as their needs and health conditions change. Although much work has been done on home and building automation systems for AAL, often referred to as assistive domotics, there is in fact still a substantial lack of solutions capable to support system designers in the early stage of development of such assistive systems. To this aim, the work contributes to the research on design of assistive domotic systems by presenting an ontology-driven methodology aimed to guide the development process. The novel contributions of the paper include the goal-oriented approach of the methodology, which involves the elicitation and analysis of AAL requirements and their formal representation in an ontology, where high-level goals are described in terms of subgoals and tasks, that are then linked to corresponding measures and devices. Moreover, logic-based reasoning enables more advanced functionalities useful at design time. We present a validation of the methodology showing typical use cases both related to the development from scratch of a domotic system with assistive capabilities starting from a set of high-level user requirements and the redesign of existing implementations according to changed requirements.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/236317 Collegamento a IRIS

2015 ESub: Mining and Exploring Substructures in Knowledge-Intensive Processes
Proceedings of the 2015 International Conference on High Performance Computing & Simulation (HPCS 2015)
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/227491 Collegamento a IRIS

2015 ESub: Exploration of Subgraphs A tool for exploring models generated by Graph Mining algorithms
Proceedings of the BPM Demo Session 2015
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico
Classificazione: 2 Contributo in Volume
Abstract: In this demo we introduce ESub, a tool aimed at visualizing the outcome provided by a frequent subgraph mining algorithm, i.e. SUBDUE. Such a tool has been developed as a supporting tool for a methodology we proposed in previous works for analyzing unstructured processes, based on the use of graphs. By exploiting graphs-based techniques, it is possible to provide the user with a different perspective on a process, where only the most relevant subprocesses (i.e., subgraphs) are displayed, rather than the complete, end-to-end process schema, which often results very chaotic in unstructured domains. Our tool allows the user to visualize and interact with such subgraphs. Furthermore, it allows for visualizing the original graphs of the set, and compress them by means of the most relevant subgraphs, in order to obtain a simplified view of the overall process.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/227574 Collegamento a IRIS

2015 Monitoring Innovation and Production Improvement
Enterprise Innovation: From Creativity to Engineering
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Editore: John Wiley & Sons, Inc.
Classificazione: 2 Contributo in Volume
Abstract: Monitoring provides a valid support to detect problems, prevent undesired situations, avoid repeating mistakes, as well as identifying virtuous behaviors both in daily production activities and innovation-oriented initiatives. Key performance indicators (KPIs) are metrics that provide quantifiable data to assess how organizations, business units or individuals are performing against predefined goals and target. This chapter presents some relevant works in data integration and performance measurement. The architecture of the solution is then introduced, which is followed by a discussion on semantic-based tools and services. The BIVEE project relies on a knowledge-centric approach in which the semantic layer, namely the production and innovation knowledge repository (PIKR). To support end-users in the management of KPI reference ontology (KPIOnto) and the extraction of KPI data for performance monitoring and comparison, two web applications named KPIOnto Editor and KPIExplorer are developed and describes in the chapter.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/227594 Collegamento a IRIS

2015 Towards a Customizable User-Centered Model for Data Analytics
Proceedings of the 2015 International Conference on Collaboration Technologies and Systems
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico; Storti, Emanuele
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Evidence-based governance and e-democracy both rely on the capability to analyze aggregated and statistical data. Recent studies report that existing analysis tools were never fully embraced by managers mainly because of their complexity for many analytical use cases. This is even more true for citizens, that do not have full control over underlying data and analysis models. In the present work, we propose an innovative user-centered approach for data analytics, that facilitates the interaction of users with statistical and aggregated measures, i.e. indicators. We provide an overview of the framework, discussing its main components and functionalities. In particular we focus on an ontology representing both atomic and compound indicators, that are provided with a calculation formula. We show how such a logic-based representation of indicators allows the implementation of powerful, automatic reasoning services, capable to provide a valuable support to users for performing analysis tasks.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/226193 Collegamento a IRIS

2015 Towards Process Instances Building for Spaghetti Processes
proceedings of 23rd Italian Symposium on Advanced Database Systems, SEBD 2015
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico; van der Aalst, Wil M. P.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Process Mining techniques aim at building a process model starting from an event log generated during the execution of the process. Classical process mining approaches have problems when dealing with Spaghetti Processes, i.e. processes with little or no structure, since they obtain very chaotic models. As a remedy, in previous works we proposed a methodology aimed at supporting the analysis of a spaghetti process by means of its most relevant subprocesses. Such approach exploits graph-mining techniques, thus requiring to reconstruct the set of process instances starting from the sequential traces stored in the event log. In the present work, we discuss the main problems related to process instances building in spaghetti contexts, and introduce a proposal for extending a process instance building technique to address such issues.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/226586 Collegamento a IRIS

2015 Semantic Disambiguation in a Social Information Discovery System
Proceedings of the 2015 International Conference on Collaboration Technologies and Systems
Autore/i: Diamantini, Claudia; Mircoli, Alex; Potena, Domenico; Storti, Emanuele
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Sentiment Analysis of microblog content calls for specific tools able to cope with the dynamic nature of information published in social networks, and the intrinsic complexity and ambiguity of human language. In this work we introduce a Word Sense Disambiguation (WSD) algorithm for polysemous word disambiguation which uses a dictionary-based approach to determine the most fitting meaning of a term, basing on nearby words in the sentence. The work is a part of a Business Intelligence system for the integration and discovery of social information from multiple social networks, namely Facebook and Twitter. In this work we also extend the number of sources taking into account LinkedIn, as it is typically used by professionals, and discussions thereof provide added benefits when a non-generic evaluation of the topic to be analyzed is required.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/226194 Collegamento a IRIS

2015 A geometric approach to feature ranking based upon results of effective decision boundary feature matrix
Feature Selection for Data and Pattern Recognition, Studies in Computational Inteligence
Autore/i: Diamantini, Claudia; Gemelli, Alberto; Potena, Domenico
Classificazione: 2 Contributo in Volume
Abstract: new method of Feature Ranking (FR) that calculates the relative weight of features in their original domain with an algorithmic procedure. The method supports information selection of real world features and is useful when the number of features has costs implications. The Feature Extraction (FE) techniques, although accurate, provide the weights of artificial features whereas it is important to weight the real features to have readable models. The accuracy of the ranking is also an important aspect; the heuristics methods, another major family of ranking methods based on generate-and-test procedures, are by definition approximate although they produce readable models. The ranking method proposed here combines the advantages of older methods, it has at its core a feature extraction technique based on Effective Decision Boundary Feature Matrix (EDBFM), which is extended to calculate the total weight of the real features through a procedure geometrically justified. The modular design of the new method allows to include any FE technique referable to the EDBFM model; a thorough benchmarking of the various solutions has been conducted.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/227411 Collegamento a IRIS

2015 Semantics-Based Multidimensional Query Over Sparse Data Marts
Big Data Analytics and Knowledge Discovery
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: Measurement of Performances Indicators (PIs) in highly distributed environments, especially in networked organisations, is particularly critical because of heterogeneity issues and sparsity of data. In this paper we present a semantics-based approach for dynamic calculation of PIs in the context of sparse distributed data marts. In particular, we propose to enrich the multidimensional model with the formal description of the structure of an indicator given in terms of its algebraic formula and aggregation function. Upon such a model, a set of reasoning-based functionalities are capable to mathematically manipulate formulas for dynamic aggregation of data and computation of indicators on-the-fly, by means of recursive application of rewriting rules based on logic programming.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/227593 Collegamento a IRIS

2015 Discovering Behavioural Patterns in Knowledge-Intensive Collaborative Processes
New Frontiers in Mining Complex Patterns
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico; Storti, Emanuele
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: Domains like emergency management, health care, or research and innovation development, are characterized by the execution of so-called knowledge-intensive processes. Such processes are typically highly uncertain, with little or no structure; consequently, classical process discovery techniques, aimed at extracting complete process schemas from execution logs, usually provide a limited support in analysing these processes. As a remedy, in the present work we propose a methodology aimed at extracting relevant subprocesses, representing meaningful collaboration behavioural patterns. We consider a real case study regarding the development of research activities, to test the approach and compare its results with the outcome of classical process discovery techniques.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/225960 Collegamento a IRIS

2014 Discovering Behavioural Patterns in Knowledge-Intensive Collaborative Processes
Proceedings of the 3rd Workshop on New Frontiers in Mining Complex  Patterns (NFMCP 2014)
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico; Storti, Emanuele
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/188306 Collegamento a IRIS

2014 Prototyping a Cloud Ecosystem for a Regional Public Administration
Proceedings of the International Symposium on Grids and Clouds (ISGC) 2014; Infrastructure Clouds and Virtualisation
Autore/i: Fanò, Livio; Bilei Gian, Mario; Storchi, Loriano; Valentini, Andrea; Fattibene, Enrico; Manziali, Matteo; Salomoni, Davide; Venturi, Valerio; Veronesi, Paolo; Riahi, Hassen; Spiga, Daniele; Amici, Cinzia; Carota, Serenella; Cirillo, Francesco; Maggiulli Maria, Laura; Sergiacomi, Andrea; Settimi, Donatella; Diamantini, Claudia; Potena, Domenico; Ribighini, Giuseppa; Storti, Emanuele; Falcioni, Damiano; Fanì, Daniele; Re, Barbara
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper we present the lessons learned in the deployment of a Cloud solution in the Marche Region Local Public Administration, which represents one of the pilot experiences at National level. The MarcheCloud (MCloud) pilot Project, started in mid-2012 as a joint collaboration among the Marche Region, National Institute of Nuclear Physics (INFN), University of Camerino and Polytechnic University of Marche.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/222326 Collegamento a IRIS

2014 A Methodology for Building Log of Collaboration Processes
Proceedings of the 2014 International Conference on Collaboration Technologies and Systems
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico; Ribighini, Giuseppa
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The analysis of data produced during collaborative activities allows organizations to improve collaboration management. Since people use several collaboration tools, these kind of data are difficult to obtain. Furthermore they are heterogeneous and require an important preprocessing step to be useful. In the present work we introduce a methodology aimed at obtaining a single logwith all data related to team activities. To improve process analysis, such data log is semantically enriched by means of a multidimensional taxonomy capable of describing collaboration activities at various abstraction levels. We also introduce a case study to be used throughout the paper as an illustrative example.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/168302 Collegamento a IRIS

2014 Data Mart Reconciliation in Virtual Innovation Factories
Advanced Information Systems Engineering Workshops
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: The present paper deals with the problem of collaboration at strategic level in innovation-oriented Virtual Enterprises. The problem is taken from the perspective of sharing a special kind of data, Key Performance Indicators, that are measures adopted to monitor the achievement of certain strategic goals. We discuss the main conflicts that can arise in measures coming from autonomous enterprises, adopting the conceptual multidimensional cube model. Then we propose a novel semantic model to deal with conflicts related to the structure of a measure, that arise when the “same” KPI is calculated in different ways by different enterprises. Finally, conflict reconciliation strategies enabled by the semantic model are discussed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/172504 Collegamento a IRIS

2014 A Composite Methodology for Supporting Collaboration Pattern Discovery via Semantic Enrichment and Multidimensional Analysis
6th International Conference on Soft Computing and Pattern Recognition (SoCPaR)
Autore/i: Cuzzocrea Alfredo; Claudia Diamantini; Laura Genga; Domenico Potena; Emanuele Storti
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Classical process discovery approaches usually investigate logs generated by processes in order to mine and discovery corresponding process schemas. When the collaboration processes case is addressed, such approaches turn to be poorly effective, due to the fact that: (i) logs of collaboration processes are usually stored in heterogenous data storages which also expose different data types; (ii) it is not easy and direct to derive a common analysis model from such logs. As a consequence, classical methodologies usually fail. In order to fulfill this gap, in this paper we describe a composite methodology that combines semantics-based techniques and multidimensional analysis paradigms to support effective and efficient collaboration process discovery from log data.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/188305 Collegamento a IRIS

2014 Collaborative Management of a Repository of KDD Processes
INTERNATIONAL JOURNAL OF METADATA, SEMANTICS AND ONTOLOGIES
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Classificazione: 1 Contributo su Rivista
Abstract: Knowledge Discovery in Databases (KDD) is a complex and computationally intensive process that requires a repeated interaction between tools and users, often in a distributed environment. Given the complexity of the process, both naïve and expert users need some support to effectively perform knowledge discovery. In this paper, we present a user- and knowledge-centric approach to support the design of KDD projects. Semantic technologies are exploited to support sharing and (re)use of KDD computational resources and processes in a distributed collaborative environment. In particular, functionalities for tool publishing, service and process discovery, and versioning of processes greatly enhance process management, and provide a learn-by-example and trial-and-test environment for collaborative KDD design. The systematic use of semantic information, a loosely coupled and layered service-oriented architecture and a cooperative and flexible approach results in a platform natively conceived for an open, distributed and collaborative environment.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/171102 Collegamento a IRIS

2014 An Integrated System for Social Information Discovery
Proceedings of the 2014 International Conference on Collaboration Technologies and Systems
Autore/i: Diamantini, Claudia; Potena, Domenico; Sabelli, Alessandro; Scattolini, Samuele
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Everyday millions of contents are generated and shared over the most popular social network. Enterprises have already realized the usefulness of social networks to enable marketing campaigns and communicate with their customers. However, only few enterprises use social network as an active source of information, interacting with the network’s users in (near-)real time, e.g. for crowdsourcing and leveraging open- innovation. To encourage and facilitate this use of networks, we believe it is needed an information discovery system which elaborates simultaneously over more-than-one networks in an integrated scenario. Such a system has to be able to handle the speed at which the contents of social network are generated, the huge amount of available data and dynamism at which networks evolve and new kind of content are shared. Furthermore, the system has to ensure a quick response time. In this work we propose a methodology to design this kind of system and present the experience gained in the development of an information discovery system based on Exploratory Data Analysis and aimed at analyzing text contents from two social networks: Facebook and Twitter.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/168303 Collegamento a IRIS

2014 A Cloud-based solution for Public Administrations. The experience of the Regione Marche
Proceedings of the 2014 International Conference on Collaboration Technologies and Systems
Autore/i: Spiga, Daniele; Bilei Gian, Mario; Riahi, Hassen; Storchi, Loriano; Fattibene, Enrico; Manzali, Matteo; Salomoni, Davide; Venturi, Valerio; Veronesi, Paolo; Diamantini, Claudia; Potena, Domenico; Raffaeli, Laura; Ribighini, Giuseppa; Storti, Emanuele; Fanò, Livio; Valentini, Andrea; Falcioni, Damiano; Fanì, Daniele; Re, Barbara; Amici, Cinzia; Carota, Serenella; Cirillo, Francesco; Maggiulli Maria, Laura; Sergiacomi, Andrea; Settimi, Donatella
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Cloud computing is perceived as the next wave of ICT, and many real experiences are on the commercial scene. However this kind of architecture has open legal issues which makes it an endeavor for Public Administrations, despite its potential impact on the efficiency, effectiveness and transparency of administrative initiatives. In the present paper we present the experience made in the deployment of a Cloud solution in the Regione Marche Local Public Administration, which represents one of the pilot experiences at National level.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/168304 Collegamento a IRIS

2014 A semi-automatic methodology for the design of performance monitoring systems
Proceedings of 22nd Italian Symposium on Advanced Database Systems
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico; Storti, Emanuele
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In the present work, we propose a methodology for the design of a strategic support information system, aimed both at monitoring enterprise daily activities and at supporting decision making by means of Key Performance Indicators (KPIs). In particular, given a set of requested KPIs and the schemas of available data sources, our approach aims at identifying the subset of requested KPIs that can be actually computed over the sources. The KPIs are represented by means of an ontology, over which proper reasoning functionalities have been imple- mented. Both such automatic functionalities and interactions with ex- perts are required in order to map ontology concepts to schema elements.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/174902 Collegamento a IRIS

2014 Special Issue on Advances in Computer Supported Collaboration: Systems and Technologies
FUTURE GENERATION COMPUTER SYSTEMS
Autore/i: Divoli, Anna; Potena, Domenico; Diamantini, Claudia; Smari Waleed, W.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/139277 Collegamento a IRIS

2014 Extending Drill-Down through Semantic Reasoning on Indicator Formulas
Data Warehousing and Knowledge Discovery
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Classificazione: 2 Contributo in Volume
Abstract: Performance indicators are calculated by composition of more basic pieces of information, and/or aggregated along a number of different dimensions. The multidimensional model is not able to take into account the compound nature of an indicator. In this work, we propose a semantic multidimensional model in which indicators are formally described together with the mathematical formulas needed for their computation. By exploiting the formal representation of formulas an extended drill-down operator is defined, which is capable to expand an indicator into its components, enabling a novel mode of data exploration. Effectiveness and efficiency are briefly discussed on a prototype introduced as a proof-of concept.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/183302 Collegamento a IRIS

2014 Collaborative Building of an Ontology of Key Performance Indicators
On the Move to Meaningful Internet Systems: OTM 2014 Conferences (22th International Conference on Cooperative Information Systems - CoopIS)
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico; Storti, Emanuele
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In the present paper we propose a logic model for the representation of Key Performance Indicators (KPIs) that supports the construction of a valid reference model (or KPI ontology) by enabling the integration of definitions proposed by different engineers in a minimal and consistent system. In detail, the contribution of the paper is as follows: (i) we combine the descriptive semantics of KPIs with a logical representation of the formula used to calculate a KPI, allowing to make the algebraic relationships among indicators explicit; (ii) we discuss how this representation enables reasoning over KPI formulas to check equivalence of KPIs and overall consistency of the set of indicators, and present an empirical study on the efficiency of the reasoning; (iii) we present a prototype implementing the approach to collaboratively manage a shared ontology of KPI definitions.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/191302 Collegamento a IRIS

2014 An Ontology-Based Data Exploration Tool for Key Performance Indicators
On the Move to Meaningful Internet Systems: OTM 2014 Conferences
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele; Zhang, Haotian
Classificazione: 2 Contributo in Volume
Abstract: This paper describes the main functionalities of an ontology-based data explorer for Key Performance Indicators (KPI), aimed to support users in the extraction of KPI values from a shared repository. Data produced by partners of a Virtual Enterprise are semantically annotated through a domain ontology in which KPIs are described together with their mathematical formulas. Based on this model and on reasoning capabilities, the tool provides functionalities for dynamic aggregation of data and computation of KPI values through the formula. In this way, besides the usual drill-down, a novel mode of data exploration is enabled, based on the expansion of a KPI into its components.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/191303 Collegamento a IRIS

2014 Interoperability issues among smart home technological frameworks
Proc. of 2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications (MESA)
Autore/i: Rossi, Lorena; Belli, Alberto; DE SANTIS, Adelmo; Diamantini, Claudia; Frontoni, Emanuele; Gambi, Ennio; Palma, Lorenzo; Pernini, Luca; Pierleoni, Paola; Potena, Domenico; Raffaeli, Laura; Spinsante, Susanna; Zingaretti, Primo; Cacciagrano, D.; Corradini, F.; Culmone, R.; De Angelis, F.; Merelli, E.; Re, B.
Editore: IEEE / Institute of Electrical and Electronics Engineers Incorporated:445 Hoes Lane:Piscataway, NJ 08854:(800)701-4333, (732)981-0060, EMAIL: subscription-service@ieee.org, INTERNET: http://www.ieee.org, Fax: (732)981-9667
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/193504 Collegamento a IRIS

2013 Knowledge-Based Business Innovation Support
Proceedings of 21 st Italian Symposium on Advanced Database Systems
Autore/i: Diamantini, Claudia; Missikoff, Michele; Potena, Domenico; Smith, Fabrizio; Storti, Emanuele; Taglino, Francesco
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper we present a semantics-based infrastructure to support planning and monitoring of innovation related activities in a Virtual Enterprise context. We address the problem of knowledge management and interoperability in environments where information is often fragmented and heterogeneous. To this end, we propose a knowledge repository and management infrastructure, called Production and Innovation Knowledge Repository (PIKR), providing a set of reference ontologies to semantically describe enterprise knowledge resources, and semantics-based services for accessing and reasoning over such descriptions. We also give an overview of the implementation of the PIKR that is being carried on in the BIVEE European project.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/107862 Collegamento a IRIS

2013 Innovation Pattern Analysis
Proceedings of the 2013 International Conference on Collaboration Technologies and Systems
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico; Storti, Emanuele
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/101265 Collegamento a IRIS

2013 A Logic-Based Formalization of KPIs for Virtual Enterprises
Advanced Information Systems Engineering Workshops
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Editore: Springer
Classificazione: 2 Contributo in Volume
Abstract: Open innovation is gaining increasing interest as a model to foster innovation through collaboration and knowledge sharing among organizations, especially in the context of Virtual Enterprises (VE). One of the main issues to overcome in such distributed settings is the integration of heterogeneous data, and the need to evaluate common Key Performance Indicators (KPI) capable to measure overall performances of the VE. In this paper we propose a conceptualization of KPIs into an ontology, to provide a common vocabulary to semantically annotate data belonging to different organizations. KPIs are described in terms of dimensions and a mathematical formula. In order to support reasoning services over KPIs formulas we refer to a logic-based formalization in Prolog, where formulas are translated as facts, and several predicates are included to support both mathematical functionalities for formula manipulation and highe-level functions especially suited for VE setup.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/105462 Collegamento a IRIS

2013 Pattern Discovery from Innovation Processes
Proceedings of the 2013 International Conference on Collaboration Technologies and Systems
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico; Storti, Emanuele
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Innovation management and promotion has become one of the most important topics in the Literature about business and executive decision support. In particular, the relationship between innovation and collaboration, both intra- and inter-organization, is gaining an increasing attention in many works, for example in the Open Innovation research field. Innovation activities, especially those that involve collaboration, are typically not structured; they don’t follow a predefined scheme or procedure and are influenced by multiple factors, for instance the individual behaviour, that makes it difficult to apply classical methods of process analysis. In this paper we describe a methodology to discover significant and recurrent patterns in innovation activities, that can be used to support and improve such kind of processes. To evaluate our approach we conducted a set of experiments on a synthetic dataset, which contains a set of traces of innovation activities generated from some abstract templates, drew with the aim to model the typical ways in which innovation is carried on.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/101264 Collegamento a IRIS

2013 GIS to Support Cost-effective Decisions on Renewable Sources Applications for low temperature geothermal energy
SpringerBriefs in Applied Sciences and Technology
Autore/i: Gemelli, Alberto; Mancini, Adriano; Diamantini, Claudia; Longhi, Sauro
Editore: Springer
Luogo di pubblicazione: London
Classificazione: 3 Libro
Abstract: Through the results of a developed case study of information system for low temperature geothermal energy, "GIS supporting cost-effective decisions on renewable sources: application to low temperature geothermal energy" addresses the issue of the use of Geographic Information Systems (GIS) in evaluating cost-effectiveness of renewable resource exploitation on a regional scale. Focusing on the design of a Decision Support System, a process is presented aimed to transform geographic data into knowledge useful for analysis and decision-making on the economic exploitation of geothermal energy. This detailed description includes a literature review and technical issues related to data collection, data mining, decision analysis for the informative system developed for the case study. It is presented a multi-disciplinary approach to GIS design which is also an innovative example of fusion of georeferenced data acquired from multiple sources including remote sensing, networks of sensors and socio-economic censuses. This book is a useful, practical reference for engineers, managers and researchers involved in the design of GIS, decision support systems, investment planning/strategy in renewable energy and ICT innovation in this field.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/98663 Collegamento a IRIS

2013 A Semantic Framework for Knowledge Management in Virtual Innovation Factories
INTERNATIONAL JOURNAL OF INFORMATION SYSTEM MODELING AND DESIGN
Autore/i: Diamantini, Claudia; Potena, Domenico; Proietti, Maurizio; Smith, Fabrizio; Storti, Emanuele; Taglino, Francesco
Classificazione: 1 Contributo su Rivista
Abstract: Knowledge management is a crucial aspect for enterprises that want to effectively cope with business innovation. However, the full control of the knowledge asset is often missing due to the lack of precise organizational models, policies, and proper technologies, especially in Virtual Enterprises (VEs), which are characterized by heterogeneous partners with different policies, skills and know-how. For such reasons, the need for technologies that enable knowledge sharing, efficient access to knowledge resources, and interoperability is felt as primary. This work proposes a semantics-based infrastructure aimed at supporting effective knowledge management for business innovation in VEs. Knowledge resources are formally represented and stored in a semantic layer, which is exploited by a set of semantic services for enabling efficient retrieval and reasoning capabilities to derive additional knowledge.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/142077 Collegamento a IRIS

2013 A virtual mart for knowledge discovery in databases
INFORMATION SYSTEMS FRONTIERS
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Classificazione: 1 Contributo su Rivista
Abstract: The Web has profoundly reshaped our vision of information management and processing, enlightening the power of a collaborative model of information production and consumption. This new vision influences the Knowledge Discovery in Databases domain as well. In this paper we propose a service-oriented, semantic-supported approach to the development of a platform for sharing and reuse of resources (data processing and mining techniques), enabling the management of different implementations of the same technique and characterized by a community-centered attitude, with functionalities for both resource production and consumption, facilitating end-users with different skills as well as resource providers with different technical and domain specific capabilities. We first describe the semantic framework underlying the approach, then we demonstrate how this framework is exploited to give different functionalities to users through the presentation of the platform functionalities.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/86391 Collegamento a IRIS

2013 A Preliminary Survey on Innovation Process Management Systems
Model and Data Engineering
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico
Classificazione: 2 Contributo in Volume
Abstract: In last decades innovation management has turned out to be a key factor in organizations growth, thus stimulating a strong research activity about such a topic. In the present work we analyse the current state of Literature regarding innovation modelling and management, taking into account different approaches with their main features. Elaborating upon such investigation, we identify open research issues and fruitful research directions for the development of data-driven, analysis-based innovation support and management systems. Finally we introduce the main features of our own approach, based on the analysis of traces generated by innovation activities, following the principles of Process Mining methods, of which an overview is delineated.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/118262 Collegamento a IRIS

2013 Decision environment of renewable energy: The case of geothermal energy
SpringerBriefs in Applied Sciences and Technology
Autore/i: Gemelli, Alberto; Mancini, Adriano; Diamantini, Claudia; Longhi, Sauro
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/259858 Collegamento a IRIS

2013 Regional atlas supporting the decision-making process
SpringerBriefs in Applied Sciences and Technology
Autore/i: Gemelli, Alberto; Mancini, Adriano; Diamantini, Claudia; Longhi, Sauro
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/259865 Collegamento a IRIS

2013 Feature analysis: Selecting decision criteria
SpringerBriefs in Applied Sciences and Technology
Autore/i: Gemelli, Alberto; Mancini, Adriano; Diamantini, Claudia; Longhi, Sauro
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/259861 Collegamento a IRIS

2013 GIS-supported decision making for low-temperature geothermal energy in central Italy
SpringerBriefs in Applied Sciences and Technology
Autore/i: Gemelli, Alberto; Mancini, Adriano; Diamantini, Claudia; Longhi, Sauro
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/259862 Collegamento a IRIS

2013 GIS-supported decision making
SpringerBriefs in Applied Sciences and Technology
Autore/i: Gemelli, Alberto; Mancini, Adriano; Diamantini, Claudia; Longhi, Sauro
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/259859 Collegamento a IRIS

2013 Decision analysis: Choosing the right plant
SpringerBriefs in Applied Sciences and Technology
Autore/i: Gemelli, Alberto; Mancini, Adriano; Diamantini, Claudia; Longhi, Sauro
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/259864 Collegamento a IRIS

2013 Conclusive remarks
SpringerBriefs in Applied Sciences and Technology
Autore/i: Gemelli, Alberto; Mancini, Adriano; Diamantini, Claudia; Longhi, Sauro
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/259860 Collegamento a IRIS

2013 Data collection
SpringerBriefs in Applied Sciences and Technology
Autore/i: Gemelli, Alberto; Mancini, Adriano; Diamantini, Claudia; Longhi, Sauro
Editore: Springer Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/259863 Collegamento a IRIS

2012 Statistical Pattern Recognition Techniques for Early Diagnosis of Diabetic Neuropathy by Posturographic Data
Medical Applications of Intelligent Data Analysis: Research Advancements
Autore/i: Diamantini, Claudia; Fioretti, Sandro; Potena, Domenico
Editore: IGI Global
Classificazione: 2 Contributo in Volume
Abstract: The goal of this chapter is to describe the use of statistical pattern recognition techniques in order to build a classification model for the early diagnosis of peripheral diabetic neuropathy. In particular, the authors present two experimental methodologies, based on linear discriminant analysis and Bayes vec- tor quantizer algorithms respectively. The former algorithm has demonstrated the best performance in distinguish between non-neuropathic and neuropathic patients, while the latter is able to build models that recognize the severity of the neuropathy.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/63151 Collegamento a IRIS

2012 Semantically-supported Team Building in a KDD Virtual Environment
Proceedings of the 2012 International Conference on Collaboration Technologies and Systems
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Team building plays a crucial role in many collaborative projects. The use of semantic technologies and tools, like ontologies and metadata management proved to be a powerful approach to organize people competencies and support the formation of teams. Although Knowledge Discovery in Databases (KDD) has inherent collaborative characteristics, team building in this domain has not been the subject of extensive work yet. In this paper we start filling this gap by presenting TeamOnto, an ontology for the representation of project teams. TeamOnto is part of a wide, modular and integrated Knowledge Base about KDD projects, of which we briefly illustrate some modules, showing their use to support team building in the KDD domain.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/70875 Collegamento a IRIS

2012 A Platform for Collaborative and Distributed KDD Process Design
Proceedings of the 2012 International Conference on Collaboration Technologies and Systems
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Knowledge Discovery in Databases (KDD) is a complex and computationally intensive process aimed at extracting knowledge from large amounts of data. To provide effective support to users, especially non-experts, in this work we propose a knowledge-centric platform specifically aimed at supporting collaborative design of KDD processes in distributed environments.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/70876 Collegamento a IRIS

2012 Mining Usage Patterns from a Repository of Scientific Workflows
Proceedings of the 2012 ACM Symposium on Applied Computing
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Editore: ACM
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In many experimental domains, especially e-Science, workflow management systems are gaining increasing attention to design and execute in-silico experiments involving data analysis tools. As a by-product, a repository of workflows is generated, that formally describes experimental protocols and the way different tools are combined inside experiments. In this paper we describe the use of the SUBDUE graph clustering algorithm to discover sub-workflows from a repository. Since sub-workflows represent significant usage patterns of tools, the discovered knowledge can be exploited by scientists to learn by-example about design practices, or to retrieve and reuse workflows. Such a knowledge, ultimately, leverages the potential of scientific workflow repositories to become a knowledge-asset. A set of experiments is conducted on the myExperiment repository to assess the effectiveness of the approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/68615 Collegamento a IRIS

2012 Towards an Open and Scientific Approach to Innovation Processes
Proceedings of the First Workshop on New Generation Enterprise and Business Innovation Systems
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Being the modern economy constantly changing and evolving, organizations are asked to develop a more flexible, open and collaborative mindset. In particular, an attitude towards continuous product/process innovation is seen as one of the potential solutions capable to effectively address the dynamism of the market and gain a competitive advantage. However, Business Innovation (BI) still lacks shared methodologies and best practices capable to effectively drive business users from an innovative idea to its realization and evaluation. This work investigates the possibility to adopt a pragmatic and systematic approach to support business users in the management of an innovation process, with the aim to test the effectiveness of an innovative idea, increasing the control over the process and reducing the risks of failure.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/80750 Collegamento a IRIS

2012 Monitoring innovation in virtual enterprises: An agile semantic approach
INVIT 2012 - Proceedings of the Fifth Interop-Vlab.It Workshop on Complexity of Systems, Complexity of Interoperability in conjunction with itAIS 2012
Autore/i: Diamantini, Claudia; Knoke, Benjamin; Missikoff, Michele
Classificazione: 2 Contributo in Volume
Abstract: In this paper, the key ideas adopted in the BIVEE project to monitor and support an innovation venture are illustrated, and developed in the context of a virtual enterprise (VE). The proposed approach is based on two pillars: a novel framework, further split into three parts, and the support of a semantic platform. In particular, the novel framework is split into the Virtual Enterprise Modelling Framework (VEMF), the Business Innovation Reference Framework (BIRF) and the Innovation Monitoring Framework (IMF). VEMF is aimed at providing a unique approach to the modelling of a VE, overcoming the divergences that the different real enterprises forming the VE may exhibit. The BIRF is aimed at providing guidelines for carrying out effective innovations in a VE. Finally, the IMF provides a set of methods, including Key Performance Indicators (KPIs), to monitor performances of activities and the achievement of planned goals. The second pillar is represented by a semantic platform relying on a federation of ontologies that allow the business context to be semantically enriched in a formal way.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/207523 Collegamento a IRIS

2012 Approximation of the Gradient of the Error Probability for Vector Quantizers
Proceedings of the 20th Italian Symposium on Advanced Database Systems
Autore/i: Diamantini, Claudia; Genga, Laura; Potena, Domenico
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Vector Quantizers (VQ) can be exploited for classification. In particular the gradient of the error probability performed by a VQ with respect to the position of its code vectors can be formally derived, hence the optimum VQ can be theoretically found. Unfortunately, this equation is of limited use in practice, since it relies on the knowledge of the class conditional probability distributions. In order to apply the method to real problems where distributions are unknown, a stochastic approximation has been previously proposed to derive a practical learning algorithm. In this paper we relax some of the assumptions underlying the original proposal and study the advantages of the resulting algorithm by both synthetic and real case studies.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/73646 Collegamento a IRIS

2012 Data Mart Integration at Measure Level
Information Systems: Crossroads for Organization, Management, Accounting and Engineering
Autore/i: Diamantini, Claudia; Potena, Domenico
Editore: Physica-Verlag HD
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/63309 Collegamento a IRIS

2012 Open Innovation in Virtual Enterprises: An Ontology-based Approach
Enterprise Interoperability V Shaping Enterprise Interoperability in the Future Internet
Autore/i: Diamantini, Claudia; Missikoff, Michele; Potena, Domenico
Editore: Springer London
Classificazione: 2 Contributo in Volume
Abstract: Innovation is recognised as a challenging activity, then it gets even more challenging if we address innovation in virtual enterprises, adopting an open approach based on advanced cooperation and interoperability. Innovation is usually seen as a process, i.e., as a set of activities that are able to intervene and change for better one or more business elements (e.g., products or business processes); therefore the knowledge concerning innovation is typically represented as a workflow. However, an innovation process is different from a usual business process, since it belongs to the category of creative endeavours that exhibit loosely structured worflows, difficult to be modelled and managed with the ‘usual’ methods. Furthermore, if we operate in a Virtual Enterprise, where several networked actors cooperate in a distributed way, the innovation process gets even more complex. For these reasons, the paper proposes a process reification method and develops the notion of an innovation as an evolving entity on top of which novel supporting systems can be built. Such an ‘innovation entity’, referred to as Innogotchi, is structured as a Bill-of-Material and represents the reference innovation ontology. We propose an implementation of the Innogotchi by using the Topic Maps standard that exhibit a number of convenient features for our purpose, among which the capability to simultaneously represent concepts, instances as well as information resources within a unified frame. The computational model underpinning the proposed methodology is also introduced.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/67416 Collegamento a IRIS

2011 A Semantic-Aided Designer for Knowledge Discovery
Proceedings of the 2011 International Conference on Collaboration Technologies and Systems
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/55341 Collegamento a IRIS

2011 Thinking Structurally Helps Business Intelligence Design
Information Technology and Innovation Trends in Organizations
Autore/i: Diamantini, Claudia; Potena, Domenico
Editore: Physica-Verlag HD
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/58901 Collegamento a IRIS

2011 Clustering of Process Schemas by Graph Mining Techniques
Proceedings of the 19th Italian Symposium on Advanced Database Systems
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/58900 Collegamento a IRIS

2011 An Ontology-based Approach to Open Innovation
Atti del Congresso Nazionale AICA 2011
Autore/i: Diamantini, Claudia; Missikoff, Michele; Potena, Domenico
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/63355 Collegamento a IRIS

2011 On the Nature of Innovation: Towards a Structural and Behavioural Characterization
Proceedings of the Fourth Interop-Vlab.It Workshop on Pervasive Computing for Networked Enterprises. Revised Papers
Autore/i: Diamantini, Claudia; Missikoff, Michele; Potena, Domenico
Editore: CEUR-WS
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/63461 Collegamento a IRIS

2010 Feature Ranking Based on Decision Border
Proc. International Conference on Pattern Recognition
Autore/i: Diamantini, Claudia; Gemelli, Alberto; Potena, Domenico
Editore: IEEE Press
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53986 Collegamento a IRIS

2010 Mining Opinions on the Basis of Their Affectivity
Proceedings of the International Symposium on Collaborative Technologies and Systems
Autore/i: Diamantini, Claudia; Potena, Domenico
Editore: IEEE Press
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53695 Collegamento a IRIS

2010 A Feature Ranking Component for GIS Architecture
Information Systems: People, Organizations, Institutions, and Technologies
Autore/i: Gemelli, Alberto; Diamantini, Claudia; Potena, Domenico
Editore: Physica-Verlag HD
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/51806 Collegamento a IRIS

2010 Hierarchical Clustering of Process Schemas
Proceedings of the Third Interop-Vlab.It Workshop on Enterprise Interoperability
Autore/i: Diamantini, Claudia; Potena, Domenico
Editore: CEUR-WS
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/54221 Collegamento a IRIS

2010 Exploring Strategic Indexes by Semantic OLAP Operators.
Management of the Interconnected World
Autore/i: Diamantini, Claudia; Potena, Domenico
Editore: Physica-Verlag
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/42285 Collegamento a IRIS

2010 Progetto del PortaleWeb Collaborativo per l'Osservatorio Prezzi e Tariffe della Regione Marche
Tariffe e prezzi nelle Marche. Implementazione di un sistema di monitoraggio
Autore/i: Diamantini, Claudia; Potena, Domenico; Papili, Alessandra
Editore: Franco Angeli
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/40603 Collegamento a IRIS

2010 Studio di un Sistema Informativo Geografico per l'Analisi Spaziale di Prezzi e Tariffe nei Comuni della Regione Marche
Tariffe e prezzi nelle Marche. Implementazione di un sistema di monitoraggio
Autore/i: Gemelli, Alberto; Potena, Domenico; Diamantini, Claudia
Editore: Franco Angeli
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/40604 Collegamento a IRIS

2010 Supporting Users in KDD Processes Design: a Semantic Similarity Matching Approach
Proceedings of ECAI 2010 3rd Planning to Learn Workshop (PlanLearn)
Autore/i: Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/49435 Collegamento a IRIS

2010 Application of Feature Ranking to Decision Support: Characterization of Geospatial Decisional Zones (Abstract)
Proceedings of 7th Conference of the Italian Chapter of AIS
Autore/i: Diamantini, Claudia; Gemelli, Alberto; Potena, Domenico
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/49434 Collegamento a IRIS

2010 Towards Coordination Patterns for Complex Experimentations in Data Mining
Proceedings of 18th Italian symposium on Advanced Database Systems
Autore/i: Aruba, Farhad; Diamantini, Claudia; Potena, Domenico; Storti, Emanuele
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/49436 Collegamento a IRIS


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