Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/8608
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dc.contributor.authorKumar, M.V.M.
dc.contributor.authorThomas, L.
dc.contributor.authorAnnappa, B.
dc.date.accessioned2020-03-30T10:22:28Z-
dc.date.available2020-03-30T10:22:28Z-
dc.date.issued2017
dc.identifier.citationProceedings of the 2017 2nd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2017, 2017, Vol., , pp.-en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8608-
dc.description.abstractProcess mining research discipline offers a spectrum of techniques for analysing event logs. Event logs represent the history of process execution. This information can be used for monitoring, analysing and improving the operational processes. The currently available methods in process mining emphasise on constructing the static process model. These models depict various dimensions of the process under analysis. But, models can only represent the past execution history and can't be used to guide and control the prospectus execution of the process. There is a need for the methods and techniques which guide the future execution of process in the light of recorded information. This paper introduces a technique for identifying and predicting the frequent control-flow execution patterns in information systems. The proposed Position Weight Matrix proven to be efficient during experimentation and validation studies. � 2017 IEEE.en_US
dc.titleOn predicting the frequent execution patterns in information systemsen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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