IIIT-Delhi Institutional Repository

Pariket : mining business process logs for root cause analysis of anomalous incidents

Show simple item record

dc.contributor.author Gupta, Nisha
dc.contributor.author Sureka, Ashish (Advisor)
dc.date.accessioned 2015-02-16T04:43:09Z
dc.date.available 2015-02-16T04:43:09Z
dc.date.issued 2015-02-16T04:43:09Z
dc.identifier.uri https://repository.iiitd.edu.in/jspui/handle/123456789/221
dc.description.abstract Process mining consists of extracting knowledge and actionable information from event-logs recorded by Process Aware Information Systems (PAIS). PAIS are vulnerable to system failures, malfunctions, fraudulent and undesirable executions resulting in anomalous trails and traces. The flexibility in PAIS resulting in large number of trace variants and the large volume of event-logs makes it challenging to identify anomalous executions and deter- mining their root causes. We propose a framework and a multi-step process to identify root causes of anomalous traces in business process logs. We fi rst transform the event-log into a sequential dataset and apply Window- based and Markovian techniques to identify anomalies. We then integrate the basic eventlog data consisting of the Case ID, time-stamp and activity with the contextual data and prepare a dataset consisting of two classes (anomalous and normal). We apply Machine Learning techniques such as decision tree classifi ers to extract rules (explaining the root causes) describing anomalous transactions. We use advanced visualization techniques such as parallel plots to present the data in a format making it easy for a process analyst to identify the characteristics of anomalous executions. We conduct a triangulation study to gather multiple evidences to validate the effectiveness and accuracy of our approach. en_US
dc.language.iso en_US en_US
dc.subject PAIS en_US
dc.title Pariket : mining business process logs for root cause analysis of anomalous incidents en_US
dc.type Thesis en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Repository

Advanced Search


My Account