Title

Goal-aligned categorization of instance variants in knowledge-intensive processes

RIS ID

102466

Publication Details

Ponnalagu, K., Ghose, A., Narendra, N. C. & Dam, H. Khanh, "Goal-aligned categorization of instance variants in knowledge-intensive processes," Lecture Notes in Computer Science, vol. 9253, pp. 350-364, 2015.

Abstract

Discovering and reasoning about deviations of business process executions (from intended designs) enables organizations to continuously evaluate their execution/performance relative to their strategic goals. We leverage the observation that a deviating process instance can be viewed as a valid variant of the intended process design provided it achieves the same goals as the intended process design. However, organizations often find it difficult to categorize and classify process execution deviations in a goal-based fashion (necessary to decide if a deviation represents a valid variant). Given that industry-scale knowledge-intensive processes typically manifest a large number of variants, this can pose a problem. In this paper, we propose an approach to help decide whether process instances in execution logs are valid variants using the goalbased notion of validity described above. Our proposed approach also enables analysis of the impact of contextual factors in the execution of specific goal-aligned process variants.We demonstrate our approach with an Eclipse-based plugin and evaluate it using an industry-scale setting in IT Incident Management with a process log of 25000 events.

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Link to publisher version (DOI)

http://dx.doi.org/10.1007/978-3-319-23063-4_24