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Journal of Intelligent Information Systems

, Volume 50, Issue 1, pp 165–193 | Cite as

Towards mining the organizational structure of a dynamic event scenario

  • Annalisa AppiceEmail author
Article

Abstract

The increasing volume and value of data is an important enabler for data science. In this study, we consider the event data, i.e. information on things that happen in organizations, machines, systems and people’s lives. Each event refers to a well-defined activity in a certain business process execution, the resource (i.e. person or device) executing or initiating the activity, the timestamp of the event, as well as to various data elements recorded with the event (e.g. the geo-location of an activity). Process mining aims to analyze event data, in order to mine knowledge that can contribute to improving a business process behavior. In particular, the focus of this study is on organizational mining, that is a sub-field of process mining that aims at understanding the life cycle of a dynamic organizational structure (i.e. a configuration of organization units) and the interactions among co-workers (resources) arising from the analysis of real-world event logs. The innovative contribution of this study is that the organizational mining goal is here achieved by combining concepts from process mining, stream mining and social network analysis. This combination is an original contribution of this study, not still explored in organizational mining field. In an assessment, benchmark event data are explored, in order to understand how the presented solution allows us to identify the life cycle a dynamic organizational structure.

Keywords

Process mining Organizational mining Internet of events Social network analysis 

Notes

Acknowledgments

This work fulfills the research objectives of the the project MAESTRA “Learning from Massive, Incompletely annotated, and Structured Data” (Grant number ICT-2013-612944) funded by the European Commission, as well as the ATENEO 2014 project “Mining of network data” funded by the University of Bari Aldo Moro. The authors wish to thank Marco Di Pietro and Claudio Greco for their support in developing the software and Lynn Rudd for her help in reading the manuscript.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Dipartimento di InformaticaUniversità degli Studi di Bari “Aldo Moro”BariItaly
  2. 2.CINI - Consorzio Interuniversitario Nazionale per l’InformaticaBariItaly
  3. 3.CILA - Centro Interdipartimentale di Logica e ApplicazioniBariItaly

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