Online incremental updating for model enhancement based on multi-perspective trusted intervals. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Fang, Na; Fang, Xianwen; Lu, Ke
• Is Part Of: Connection science. Volume 34:Issue 1(2022)
• Page Start: 1956
• Page End: 1980
• Publication Date: 2022-12-31
• Subjects: Online incremental updating -- behavioural trusted interval -- resource trusted interval -- event stream -- model enhancement; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2022.2088696
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : Model incremental updating enhances the initial model by analysing discrepancy parts of the system to improve the model's adaptability for new scenarios. These discrepancy components originate from the deviation between the increasing business process operational state and the outdated original planning model. Considerable domain-specific knowledge is required to determine the threshold points for selecting appropriate activities, but process analysts rarely know each scenario's domain knowledge. Moreover, most analytical processes only focussed on the control flow level. Aiming at these issues, this paper proposes a Hybrid Behavioural and Resource Trusted intervals Updating algorithm (BI&RI Updating) for model enhancement based on control level and resource level of online event streams. First, analyse the reference model to construct multi-perspective trusted interval constraints in the offline stage. From a control-flow perspective, the behavioural relationships between activities are researched using a deep clustering approach. Moreover, from a data-flow standpoint, resource co-occurrence relationships are analysed based on association rules. Next, the incremental update algorithm in online scenarios is proposed to update the model by filtering the event streams and iteratively optimising the trusted intervals. Finally, the proposed algorithm is implemented based on the PM4PY framework and evaluated using real logs with both outcome model quality and execution efficiency. The results show that the algorithm can execute quickly and improve the quality of the model even in a non-ideal state where the logs contain noise.