The Benefits of Sensor-Measurement Aggregation in Discovering IoT Process Models: A Smart-House Case Study

被引：8
作者：
de Leoni, Massimiliano [1 ]
Pellattiero, Lucia [1 ]
机构：
[1] Univ Padua, Padua, Italy
来源：
BUSINESS PROCESS MANAGEMENT WORKSHOPS, BPM 2021 | 2022年 / 436卷
关键词：
Sensor data; Event-log abstraction; Clustering; Human habits model discovery; Smart house;
D O I：
10.1007/978-3-030-94343-1_31
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
IoT systems collect and exchange data whose analysis opens up incredible opportunities to improve the human satisfaction with IoT systems. The IoT data can be indeed used to discover human habits and interaction patterns, useful to both improve human experience and further automatize the system. Process Mining can be leveraged on for this purpose, but a gap needs to be bridged between IoT-device event data and logs by aggregating events to take to the right granularity for Process Mining. This papers reports on the experience on real-life data to discover the human habits in a smart house. In particular, the benefits are reported on how to aggregate event data to the right granularity to further apply process-mining discovery techniques. The results illustrate that, when applied on the case study, the proposed technique is able to discover human-habit models that are more readable and accurate, thus providing actionable insights for a subsequent optimization of the human experience with the IoT system.
引用
收藏
页码：403 / 415
页数：13
相关论文
共 11 条
  • [1] Discovering Process Models of Activities of Daily Living from Sensors
    Cameranesi, Marco
    Diamantini, Claudia
    Potena, Domenico
    [J]. BUSINESS PROCESS MANAGEMENT WORKSHOPS (BPM 2017), 2018, 308 : 285 - 297
  • [2] Triage of IoT Attacks Through Process Mining
    Coltellese, Simone
    Maggi, Fabrizio Maria
    Marrella, Andrea
    Massarelli, Luca
    Querzoni, Leonardo
    [J]. ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2019 CONFERENCES, 2019, 11877 : 326 - 344
  • [3] Event-Log Abstraction using Batch Session Identification and Clustering
    de Leoni, Massimiliano
    Dundar, Safa
    [J]. PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20), 2020, : 36 - 44
  • [4] Dimaggio M, 2016, 2016 INT IEEE CONFERENCES ON UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING AND COMMUNICATIONS, CLOUD AND BIG DATA COMPUTING, INTERNET OF PEOPLE, AND SMART WORLD CONGRESS (UIC/ATC/SCALCOM/CBDCOM/IOP/SMARTWORLD), P145, DOI [10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0043, 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.137]
  • [5] Hemmer A., 2020, P OMS IEEE IFIP NETW
  • [6] The Internet of Things Meets Business Process Management A Manifesto
    Janiesch, Christian
    Koschmider, Agnes
    Mecella, Massimo
    Weber, Barbara
    Burattin, Andrea
    Di Ciccio, Claudio
    Fortino, Giancarlo
    Gal, Avigdor
    Kannengiesser, Udo
    Leotta, Francesco
    Mannhardt, Felix
    Marrella, Andrea
    Mendling, Jan
    Oberweis, Andreas
    Reichert, Manfred
    Rinderle-Ma, Stefanie
    Serral, Estefania
    Song, WenZhan
    Su, Jianwen
    Torres, Victoria
    Weidlich, Matthias
    Weske, Mathias
    Zhang, Liang
    [J]. IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE, 2020, 6 (04): : 34 - 44
  • [7] Mitchell TM, 1997, Machine Learning
  • [8] DECLARE: Full support for loosely-structured processes
    Pesic, Maja
    Schonenberg, Helen
    van der Aalst, Wil M. P.
    [J]. 11TH IEEE INTERNATIONAL ENTERPRISE DISTRIBUTED OBJECT COMPUTING CONFERENCE, PROCEEDINGS, 2007, : 287 - +
  • [9] Discovering more precise process models from event logs by filtering out chaotic activities
    Tax, Niek
    Sidorova, Natalia
    van der Aalst, Wil M. P.
    [J]. JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 2019, 52 (01) : 107 - 139
  • [10] van der Aalst W. M. P., 2016, PROCESS MINING DATA, DOI DOI 10.1007/978-3-662-49851-4_1
12