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Publications

  1. Antonio Senese, Matteo; Rizzo, Giuseppe; Dragoni, Mauro; Morisio, Maurizio,
    Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, May 11-16, 2020,
    European Language Resources Association,
    2020
    , pp. 717-
    725
    , (12th Language Resources and Evaluation Conference,
    May 11-16, 2020)
  2. Santos Teixeira, Milene; da Costa Pereira, Celia; Dragoni, Mauro,
    PRIMA 2020: Principles and Practice of Multi-Agent Systems - 23rd International Conference, Nagoya, Japan, November 18-20, 2020, Proceedings,
    Springer,
    vol.12568,
    2020
    , pp. 281-
    298
    , (PRIMA 2020: Principles and Practice of Multi-Agent Systems - 23rd International Conference,
    November 18-20, 2020)
  3. Faron, Catherine; Ghidini, Chiara (eds.),
    Selected papers from EKAW 2018,
    2020
  4. Fahland, Dirk; Ghidini, Chiara; Becker, Jorg; Dumas, Marlon (eds.),
    Proceedings of the Business Process Management Forum (BPM2020),
    2020
  5. Fahland, Dirk; Ghidini, Chiara; Becker, Jorg; Dumas, Marlon (eds.),
    Proceedings of the 18th International Conference on Business Process Management (BPM 2020),
    2020
  6. Di Francescomarino, Chiara; Ghidini, Chiara; Maria Maggi, Fabrizio; Rizzi, Williams,
    2020,
    Predictive Process Monitoring is a branch of process mining that aims at predicting, at runtime, the future development of ongoing cases of a process. Predictions related to the future of an ongoing process execution can pertain to numeric measures of interest (e.g., the completion time), to categorical outcomes (e.g., whether a given predicate will be fulfilled or violated), or to the sequence of future activities (and related payloads). Recently, different approaches have been proposed in the literature in order to provide predictions on the outcome, the remaining time, the required resources as well as the remaining activities of an ongoing execution, by leveraging information related to the control flow, the data flow, or even unstructured text contained in event logs recording information about process executions. The approaches can be of different nature and, some of them also equipped to offer users support in tasks such as parameter selection. This tutorial aims at (i) providing an introduction on predictive process monitoring, including an overview on how to move within the large number of approaches and techniques available; (ii) introducing the current research challenges and advanced topics; (iii) providing an overview on how to use the existing instruments and tools..
  7. Senderovich, Arik; Francescomarino, Chiara Di; Maggi, Fabrizio Maria,
    in «INFORMATION SYSTEMS»,
    vol. 84,
    2019
    , pp. 255 -
    264
  8. Dragoni, Mauro,
    in «IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE»,
    vol. 14,
    n. 2,
    2019
    , pp. 18 -
    27
  9. Dragoni, Mauro; Federici, Marco; Rexha, Andi,
    in «COGNITIVE COMPUTATION»,
    vol. 11,
    n. 4,
    2019
    , pp. 469 -
    488
  10. Dragoni, Mauro; Federici, Marco; Rexha, Andi,
    in «INFORMATION PROCESSING & MANAGEMENT»,
    vol. 56,
    n. 3,
    2019
    , pp. 1103 -
    1118

Pages