{"title":"Extending predictive process monitoring for collaborative processes","authors":"Daniel Calegari, Andrea Delgado","doi":"arxiv-2409.09212","DOIUrl":null,"url":null,"abstract":"Process mining on business process execution data has focused primarily on\norchestration-type processes performed in a single organization\n(intra-organizational). Collaborative (inter-organizational) processes, unlike\nthose of orchestration type, expand several organizations (for example, in\ne-Government), adding complexity and various challenges both for their\nimplementation and for their discovery, prediction, and analysis of their\nexecution. Predictive process monitoring is based on exploiting execution data\nfrom past instances to predict the execution of current cases. It is possible\nto make predictions on the next activity and remaining time, among others, to\nanticipate possible deviations, violations, and delays in the processes to take\npreventive measures (e.g., re-allocation of resources). In this work, we\npropose an extension for collaborative processes of traditional process\nprediction, considering particularities of this type of process, which add\ninformation of interest in this context, for example, the next activity of\nwhich participant or the following message to be exchanged between two\nparticipants.","PeriodicalId":501123,"journal":{"name":"arXiv - CS - Databases","volume":"48 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Databases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.09212","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Process mining on business process execution data has focused primarily on
orchestration-type processes performed in a single organization
(intra-organizational). Collaborative (inter-organizational) processes, unlike
those of orchestration type, expand several organizations (for example, in
e-Government), adding complexity and various challenges both for their
implementation and for their discovery, prediction, and analysis of their
execution. Predictive process monitoring is based on exploiting execution data
from past instances to predict the execution of current cases. It is possible
to make predictions on the next activity and remaining time, among others, to
anticipate possible deviations, violations, and delays in the processes to take
preventive measures (e.g., re-allocation of resources). In this work, we
propose an extension for collaborative processes of traditional process
prediction, considering particularities of this type of process, which add
information of interest in this context, for example, the next activity of
which participant or the following message to be exchanged between two
participants.