OBDA for Log Extraction in Process Mining

Reasoning Web: Semantic Interoperability on the Web -- 13th Int. Summer School Tutorial Lectures (RW 2017). Volume 10370 of Lecture Notes in Computer Science. 2017.

Process mining is an emerging area that synergically combines model-based and data-oriented analysis techniques to obtain useful insights on how business processes are executed within an organization. Through process mining, decision makers can discover process models from data, compare expected and actual behaviors, and enrich models with key information about their actual execution. To be applicable, process mining techniques require the input data to be explicitly structured in the form of an event log, which lists when and by whom different case objects (i.e., process instances) have been subject to the execution of tasks. Unfortunately, in many real world set-ups, such event logs are not explicitly given, but are instead implicitly represented in legacy information systems. To apply process mining in this widespread setting, there is a pressing need for techniques able to support various process stakeholders in data preparation and log extraction from legacy information systems. The purpose of this paper is to single out this challenging, open issue, and didactically introduce how techniques from intelligent data management, and in particular ontology-based data access, provide a viable solution with a solid theoretical basis.

@incollection{RW-2017,
   title = "OBDA for Log Extraction in Process Mining",
   year = "2017",
  author = "Diego Calvanese and Kalayci, Tahir Emre and Marco Montali and
Ario Santoso",
   editor = "Giovambattista Ianni and Domenico Lembo and Leopoldo Bertossi
and Wolfgang Faber and Birte Glimm and Georg Gottlob and Steffen Staab",
   booktitle = "Reasoning Web:  Semantic Interoperability on the Web -- 13th
Int. Summer School Tutorial Lectures (RW 2017)",
   pages = "292--345",
   volume = "10370",
   publisher = "Springer",
   series = "Lecture Notes in Computer Science",
   doi = "10.1007/978-3-319-61033-7_9",
}