R. P. Jagadeesh Chandra Bose

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Process mining refers to the extraction of process models from event logs. Real-life processes tend to be less structured and more flexible. Traditional process mining algorithms have problems dealing with such unstructured processes and generate " spaghetti-like " process models that are hard to comprehend. An approach to overcome this is to cluster(More)
Process Mining refers to the extraction of process models from event logs. Real-life processes tend to be less struc-tured and more flexible. Traditional process mining algorithms have problems dealing with such unstructured processes and generate spaghetti-like process models that are hard to comprehend. An approach to overcome this is to cluster process(More)
Process mining refers to the extraction of process models from event logs. Real-life processes tend to be less structured and more flexible. Traditional process mining algorithms have problems dealing with such unstructured processes and generate spaghetti-like process models that are hard to comprehend. One reason for such a result can be attributed to(More)
Process mining techniques attempt to extract non-trivial knowledge and interesting insights from event logs. Process models can be seen as the " maps " describing the operational processes of organizations. Unfortunately, traditional process discovery algorithms have problems dealing with less-structured processes. Furthermore, existing discovery algorithms(More)
A real-life event log, taken from a Dutch Academic Hospital, is analyzed using process mining techniques. The log contains events related to treatment and diagnosis steps for patients diagnosed with cancer. Given the heterogeneous nature of these cases, we first demonstrate that it is possible to create more homogeneous subsets of cases (e.g. patients(More)
Operational processes need to change to adapt to changing circumstances, e.g., new legislation, extreme variations in supply and demand , seasonal effects, etc. While the topic of flexibility is well-researched in the BPM domain, contemporary process mining approaches assume the process to be in steady state. When discovering a process model from event(More)
Process mining techniques often reveal that real-life processes are more variable than anticipated. Although declarative process models are more suitable for less structured processes, most discovery techniques generate conventional procedural models. In this paper, we focus on discovering Declare models based on event logs. A Declare model is composed of(More)
Business processes leave trails in a variety of data sources (e.g., audit trails, databases, transaction logs). Hence, every process instance can be described by a trace, i.e., a sequence of events. Process mining techniques are able to extract knowledge from such traces and provide a welcome extension to the repertoire of business process analysis(More)
Process mining techniques attempt to extract non-trivial knowledge and interesting insights from event logs. Process mining provides a welcome extension of the repertoire of business process analysis techniques and has been adopted in various commercial BPM systems (BPM|one, Futura Reflect, ARIS PPM, Fujitsu, etc.). Unfortunately, traditional process(More)