TY - GEN
T1 - Adaptive Process Log Generation and Analysis with Next(Log) and ML.Log
AU - Cartwright, Dyllan
AU - Sterie, Radu Andrei
AU - Yadegari Ghahderijani, Arash
AU - Karastoyanova, Dimka
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - In this paper we present a tool for adaptive process log generation and analysis of the correlation between KPI (Key Performance Indicator) values and changes in adaptive processes. The tool features a component called Next(Log) helping users to generate initial business process logs using any preferred method and subsequently allows them to adapt these logs based on their own defined rules while ensuring an intuitive and coherent user interface. The adapted logs are then used for log analysis with the ML.Log component, which employs machine learning techniques to find patterns of matching KPI values and adaptation injections in the logs. The tool therefore supports the research on the challenges imposed by the lack of sufficient amount of data from adaptive process logs and the open issues in identifying at what KPIs values changes are required and what kind of changes would have the best impact on the process performance at run time.
AB - In this paper we present a tool for adaptive process log generation and analysis of the correlation between KPI (Key Performance Indicator) values and changes in adaptive processes. The tool features a component called Next(Log) helping users to generate initial business process logs using any preferred method and subsequently allows them to adapt these logs based on their own defined rules while ensuring an intuitive and coherent user interface. The adapted logs are then used for log analysis with the ML.Log component, which employs machine learning techniques to find patterns of matching KPI values and adaptation injections in the logs. The tool therefore supports the research on the challenges imposed by the lack of sufficient amount of data from adaptive process logs and the open issues in identifying at what KPIs values changes are required and what kind of changes would have the best impact on the process performance at run time.
KW - Adaptive process log generation
KW - KPI-to-adaptation correlation
KW - Runtime process adaptation
KW - Synthetic process event logs
UR - http://www.scopus.com/inward/record.url?scp=85187779074&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-54712-6_21
DO - 10.1007/978-3-031-54712-6_21
M3 - Conference contribution
AN - SCOPUS:85187779074
SN - 9783031547119
T3 - Lecture Notes in Business Information Processing
SP - 331
EP - 337
BT - Enterprise Design, Operations, and Computing. EDOC 2023 Workshops - IDAMS, iRESEARCH, MIDas4CS, SoEA4EE, EDOC Forum, Demonstrations Track and Doctoral Consortium, 2023, Revised Selected Papers
A2 - Sales, Tiago Prince
A2 - de Kinderen, Sybren
A2 - Proper, Henderik A.
A2 - Pufahl, Luise
A2 - Karastoyanova, Dimka
A2 - van Sinderen, Marten
PB - Springer Science and Business Media Deutschland GmbH
T2 - several workshops, EDOC Forum and Demonstrations and Doctoral Consortium track, which were held in conjunction with 27th International Conference on Enterprise Design, Operations, and Computing, EDOC 2023
Y2 - 30 October 2023 through 3 November 2023
ER -