Mining Goal Refinement Patterns: Distilling Know-How from Data

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dc.contributor.authorSantiputri, Metta
dc.contributor.authorDeb, Novarun
dc.contributor.authorKhan, Muhammad Asjad
dc.contributor.authorGhose, Aditya
dc.contributor.authorDam, Hoa Khanh
dc.contributor.authorChaki, Nabendu
dc.date.accessioned2023-06-28T05:22:57Z
dc.date.available2023-06-28T05:22:57Z
dc.date.issued2017-10-21
dc.description.abstrakGoal models play an important role by providing a hierarchic representation of stakeholder intent, and by providing a representation of lower-level subgoals that must be achieved to enable the achievement of higher-level goals. A goal model can be viewed as a composition of a number of goal refinement patterns that relate parent goals to subgoals. In this paper, we offer a means for mining these patterns from enterprise event logs and a technique to leverage vector representations of words and phrases to compose these patterns to obtain complete goal models. The resulting machinery can be quiote powerful in its ability to mine know-how or constitutive norms. We offer an empirical evaluation using both real-life and synthetic datasets.en_US
dc.identifier.isbn978-3-319-69903-5
dc.identifier.otherhttps://doi.org/10.1007/978-3-319-69904-2_6
dc.identifier.urihttps://repository.polibatam.ac.id/xmlui/handle/123456789/1716
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofseriesLecture Notes in Computer Science;volume 10650
dc.relation.ispartofseriesProceedings of the International Conference on Conceptual Modeling (ER 2017);
dc.subjectGoal model miningen_US
dc.subjectGoal refinementen_US
dc.subjectKnow-howen_US
dc.titleMining Goal Refinement Patterns: Distilling Know-How from Dataen_US
dc.typeBook chapteren_US

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