During the last years, a number of studies have experimented with applying process mining (PM) techniques to smart spaces data. The general goal has been to automatically model human routines as if they were business processes. However, applying process-oriented techniques to smart spaces data comes with its own set of challenges. This paper surveys existing approaches that apply PM to smart spaces and analyses how they deal with the following challenges identified in the literature: choosing a modelling formalism for human behaviour; bridging the abstraction gap between sensor and event logs; and segmenting logs in traces. The added value of this article lies in providing the research community with a common ground for some important challenges that exist in this field and their respective solutions, and to assist further research efforts by outlining opportunities for future work.

A Survey on the Application of Process Mining to Smart Spaces Data / Bertrand, Y.; Van den Abbeele, B.; Veneruso, S.; Leotta, F.; Mecella, M.; Serral, E.. - 468:(2023), pp. 57-70. (Intervento presentato al convegno International Workshops on EDBA, ML4PM, RPM, PODS4H, SA4PM, PQMI, EduPM, and DQT-PM, held at the International Conference on Process Mining, ICPM 2022 tenutosi a Bolzano (Italy)) [10.1007/978-3-031-27815-0_5].

A Survey on the Application of Process Mining to Smart Spaces Data

Veneruso S.;Leotta F.;Mecella M.;
2023

Abstract

During the last years, a number of studies have experimented with applying process mining (PM) techniques to smart spaces data. The general goal has been to automatically model human routines as if they were business processes. However, applying process-oriented techniques to smart spaces data comes with its own set of challenges. This paper surveys existing approaches that apply PM to smart spaces and analyses how they deal with the following challenges identified in the literature: choosing a modelling formalism for human behaviour; bridging the abstraction gap between sensor and event logs; and segmenting logs in traces. The added value of this article lies in providing the research community with a common ground for some important challenges that exist in this field and their respective solutions, and to assist further research efforts by outlining opportunities for future work.
2023
International Workshops on EDBA, ML4PM, RPM, PODS4H, SA4PM, PQMI, EduPM, and DQT-PM, held at the International Conference on Process Mining, ICPM 2022
Process mining; smart spaces; sensor logs
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A Survey on the Application of Process Mining to Smart Spaces Data / Bertrand, Y.; Van den Abbeele, B.; Veneruso, S.; Leotta, F.; Mecella, M.; Serral, E.. - 468:(2023), pp. 57-70. (Intervento presentato al convegno International Workshops on EDBA, ML4PM, RPM, PODS4H, SA4PM, PQMI, EduPM, and DQT-PM, held at the International Conference on Process Mining, ICPM 2022 tenutosi a Bolzano (Italy)) [10.1007/978-3-031-27815-0_5].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1683465
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