NICE: The Native IoT-Centric Event Log Model for Process Mining

被引：2
|
作者：
Bertrand, Yannis [1 ]
Veneruso, Silvestro [2 ]
Leotta, Francesco [2 ]
Mecella, Massimo [2 ]
Serral, Estefania [1 ]
机构：
[1] Katholieke Univ Leuven, Res Ctr Informat Syst Engn LIRIS, Warmoesberg 26, B-1000 Brussels, Belgium
[2] Sapienza Univ Roma, Rome, Italy
来源：
PROCESS MINING WORKSHOPS, ICPM 2023 | 2024年 / 503卷
关键词：
Process Mining; Event Logs; IoT; Standard Format;
D O I：
10.1007/978-3-031-56107-8_3
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
More and more so-called IoT-enhanced business processes (BPs) are supported by IoT devices, which collect large amounts of data about the execution of such processes. While these data have the potential to reveal crucial insights into the execution of the BPs, the absence of a suitable event log format integrating IoT data to process data greatly hampers the realisation of this potential. In this paper, we present the Native Iot-Centric Event (NICE) log, a new event log format designed to incorporate IoT data into a process event log ensuring traceability, flexibility and limiting data loss. The new format was linked to a smart spaces data simulator to generate synthetic logs. We evaluate our format against requirements previously established for an IoT-enhanced event log format, showing that it meets all requirements, contrarily to other alternative formats. We then perform an analysis of a synthetic log to show how IoT data can easily be used to explain anomalies in the process.
引用
收藏
页码：32 / 44
页数：13
相关论文
共 38 条
  • [31] Clustering Analysis and Frequent Pattern Mining for Process Profile Analysis: An Exploratory Study for Object-Centric Event Logs
    Faria Junior, Elio Ribeiro
    Neubauer, Thais Rodrigues
    Fantinato, Marcelo
    Peres, Sarajane Marques
    PROCESS MINING WORKSHOPS, ICPM 2022, 2023, 468 : 269 - 281
  • [32] Process mining approach for the conformance checking of discrete-event simulation model
    Uehara, Kenji
    Hiraishi, Kunihiko
    2019 58TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE), 2019, : 615 - 620
  • [33] Behavioural Similarity Measurement of Business Process Model to Compare Process Discovery Algorithms Performance in Dealing with Noisy Event Log
    Nuritha, Ifrina
    Mahendrawathi, E. R.
    FIFTH INFORMATION SYSTEMS INTERNATIONAL CONFERENCE, 2019, 161 : 984 - 993
  • [34] Implementation of Alpha Miner Algorithm in Process Mining Application Development for Online Learning Activities Based on MOODLE Event Log Data
    Nafasa, Phyllalintang
    Waspada, Indra
    Bahtiar, Nurdin
    Wibowo, Adi
    2019 3RD INTERNATIONAL CONFERENCE ON INFORMATICS AND COMPUTATIONAL SCIENCES (ICICOS 2019), 2019,
  • [35] Bridging Signals and Human Intelligence Log Mining-Driven and Meta Model-Guided Ontology Population in Large-Scale IoT
    Graf, David
    Retschitzegger, Werner
    Schwinger, Wieland
    Kapsammer, Elisabeth
    Baumgartner, Norbert
    KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, PT II, 2022, 13369 : 571 - 585
  • [36] A Framework for the Generation of Monitor and Plant Model From Event Logs Using Process Mining for Formal Verification of Event-Driven Systems
    Xavier, Midhun
    Dubinin, Victor
    Patil, Sandeep
    Vyatkin, Valeriy
    IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY, 2024, 5 : 517 - 534
  • [37] Anvaya: An Algorithm and Case-Study on Improving the Goodness of Software Process Models generated by Mining Event-Log Data in Issue Tracking Systems
    Juneja, Prerna
    Kundra, Divya
    Sureka, Ashish
    PROCEEDINGS 2016 IEEE 40TH ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE WORKSHOPS, VOL 1, 2016, : 53 - 62
  • [38] Process Mining on the Extended Event Log to Analyse the System Usage During Healthcare Processes (Case Study: The GP Tab Usage During Chemotherapy Treatments)
    Kurniati, Angelina Prima
    Hall, Geoff
    Hogg, David
    Johnson, Owen
    PROCESS MINING WORKSHOPS, ICPM 2020 INTERNATIONAL WORKSHOPS, 2021, 406 : 330 - 342
1234