Context aware Hidden Markov Model for Intention process mining

TitreContext aware Hidden Markov Model for Intention process mining
Publication TypeConference Paper
Year of Publication2021
AuthorsChoukri, I, Guermah, H, Daosabah, A, Nassar, M
Conference Name5th International Conference on Intelligent Computing in Data Sciences, ICDS 2021
Mots-clésBehavioral research, Context, Data mining, Digital devices, Event logs, Goal mining, Goal models, Hidden Markov models, Information systems, Information use, Intention-oriented process modeling, Intentional process modeling, Machine learning, Markov modeling, Oriented process, Process Discovery, Process engineering, Process mining, Process recommendation, Process-aware information systems, Process-models, Supervised learning, Unsupervised learning, User profile

Nowadays, the omnipresence of digital devices and solutions in daily life made the digital footprints of individuals and the traces of their activities widely available. Smart devices generate a tremendous amount of data that and enables tracking their users' activity. Extensive research has been conducted to produce generic process models based on the analysis of user's activity recorded during the enactment of operational processes. Unfortunately, these approaches considered only the relation between the observed activities and their sequences to infer the underlying process. Thus, ignoring the goal conditioning the user's behavior when triggering the actual process. Nevertheless, the same activity traces could serve to unhide the intention behind each process. This article will focus on presenting our approach to Contextual intention mining using Hidden Markov Model (HMM). This approach explores the close relationships between intention and context to construct the process model while ensuring consistency between observed context and actual intentions. © 2021 IEEE.




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