Trace alignment of event logs is used to understand and improve business processes. A key missing component of current approaches for performing trace alignment is a methodology to measure the quality of alignment. We propose a novel approach for generating random event logs that can be used for testing and evaluating trace alignment tools. We first extracted a statistical model from 437 real-world traces from Children’s National Medical Center in Washington DC (CNMC). We then created a guide tree and prune it to a minimum spanning tree based on user defined trace number in output event log. The final step is to fill this tree. Each node in this tree contains a trace. Each leaf node represents a trace in output event log. The root node is filled by a user-defined sequence and each child node is mutate from parent node based on the statistical model. To validate our approach, we used a concept of replay fitness score. Replay fitness score is used to quantify the extent to which a model can reproduce the traces recorded in an event log. It’s between 0 and 1. The value 1 means that the model can perfectly replay the event log and 0 means that the model cannot reply the log. Comparing with process model (Petri-Net) extracted from 437 real-world traces, the output event log of our system can constantly get a score of 0.8. II Therefore, our results are relevant not for only validation of trace alignment tools but also for other process mining tools.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_5765
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (viii, 42 p. : ill.)
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Markov processes
Note (type = statement of responsibility)
by Xiao Bo
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
Rutgers University. Graduate School - New Brunswick
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License
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Author Agreement License
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