Effa: A ProM Plugin for Recovering Event Logs

被引:3
|
作者
Xia, Xiaoxu [1 ]
Song, Wei [1 ]
Chen, Fangfei [1 ]
Li, Xuansong [1 ]
Zhang, Pengcheng [2 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China
[2] Hohai Univ, Coll Comp & Informat, Nanjing, Jiangsu, Peoples R China
关键词
event logs; minimum recovery; process decomposition; trace replaying; ProM;
D O I
10.1145/2993717.2993732
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
While event logs generated by business processes play an increasingly significant role in business analysis, the quality of data remains a serious problem. Automatic recovery of dirty event logs is desirable and thus receives more attention. However, existing methods only focus on missing event recovery, or fall short of efficiency. To this end, we present Eff a, a ProM plugin, to automatically recover event logs in the light of process specifications. Based on advanced heuristics including process decomposition and trace replaying to search the minimum recovery, Eff a achieves a balance between repairing accuracy and efficiency.
引用
收藏
页码:108 / 111
页数:4
相关论文
共 50 条
  • [21] Multidimensional subgroup discovery on event logs
    Ribeiro, J.
    Fontes, T.
    Soares, C.
    Borges, J. L.
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 246
  • [22] Analysis of Event Logs: Behavioral Graphs
    Slaninova, Katerina
    Vymetal, Dominik
    Martinovic, Jan
    WEB INFORMATION SYSTEMS ENGINEERING - WISE 2014 WORKSHOPS, 2015, 9051 : 42 - 56
  • [23] Automatic Conversion of Event Data to Event Logs Using CNN and Event Density Embedding
    Sim, Sunghyun
    Sutrisnowati, Riska Asriana
    Won, Seokrae
    Lee, Sanghwa
    Bae, Hyerim
    IEEE ACCESS, 2022, 10 : 15994 - 16009
  • [24] A generic import framework for process event logs
    Gunther, Christian W.
    van der Aalst, Wil M. P.
    BUSINESS PROCESS MANAGEMENT WORKSHOPS, 2006, 4103 : 81 - 92
  • [25] Autoencoders for improving quality of process event logs
    Hoang Thi Cam Nguyen
    Lee, Suhwan
    Kim, Jongchan
    Ko, Jonghyeon
    Comuzzi, Marco
    EXPERT SYSTEMS WITH APPLICATIONS, 2019, 131 : 132 - 147
  • [26] A framework for detecting deviations in complex event logs
    Li, Guangming
    van der Aalst, Wil M. P.
    INTELLIGENT DATA ANALYSIS, 2017, 21 (04) : 759 - 779
  • [27] Discovering Signature Patterns from Event Logs
    Bose, R. P. Jagadeesh Chandra
    van der Aalst, Wil M. P.
    2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING (CIDM), 2013, : 111 - 118
  • [28] Auditing Between Event Logs and Process Trees
    Li, Hongxia
    Hou, Haixia
    Du, Yuyue
    Liu, Zhi
    DIGITAL TV AND MULTIMEDIA COMMUNICATION, 2019, 1009 : 227 - 237
  • [29] Mining invisible tasks from event logs
    Wen, Lijie
    Wang, Jianmin
    Sun, Jiaguang
    ADVANCES IN DATA AND WEB MANAGEMENT, PROCEEDINGS, 2007, 4505 : 358 - +
  • [30] Towards a better assessment of event logs quality
    Kherbouche, Mohammed Oussama
    Laga, Nassim
    Masse, Pierre-Aymeric
    PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2016,