A Hybrid Approach to Process Mining: Finding Immediate Successors of a Process by Using From-To Chart

Esgin, Eren
Karagöz, Pınar
Process mining is a branch of data mining that aims to discover process model from the event logs. In this study, we propose a hybrid approach to process mining in such a way that, "from-to chart" is used as the front-end to monitor the transitions among activities of a realistic event log. Another novelty of this study is developed evaluation metrics, which are used for finding immediate successors in order to convert these raw relations into dependency/frequency graph.


Improving Efficiency of Sequence Mining by Combining First Occurrence Forest (FOF) Strategy and Sibling Principle
Onal, Kezban Dilek; Karagöz, Pınar (2014-06-04)
Sequential pattern mining is one of the basic problems in data mining and it has many applications in web mining. The WAP-Tree (Web Access Pattern Tree) data structure provides a compact representation of single-item sequence databases. WAP-Tree based algorithms have shown notable execution time and memory consumption performance on mining single-item sequence databases. We propose a new algorithm FOF-SP, a WAP-Tree based algorithm which combines an early prunning strategy called "Sibling Principle" from th...
Generating performance improvement suggestions by using cross organizational process mining
Yılmaz, Onur; Karagöz, Pınar (null; 2015-12-10)
Process mining is a relatively young and developing research area with the main idea of discovering, monitoring and improving processes by extracting information from event logs. With the increase of cloud computing and shared infrastructures, event logs of multiple organizations are available for analysis where cross-organizational process mining stands with the opportunity for organizations learning from each other. The approach proposed in this study mines process models of organizations and calculates p...
A New WAP-tree based sequential pattern mining algorithm for faster pattern extraction
Önal, Kezban Dilek; Şenkul, Pınar; Department of Computer Engineering (2012)
Sequential pattern mining constitutes a basis for solution of problems in various domains like bio-informatics and web usage mining. Research on this field continues seeking faster algorithms. WAP-Tree based algorithms that emerged from web usage mining literature have shown a remarkable performance on single-item sequence databases. In this study, we investigated application of WAP-Tree based mining to multi-item sequential pattern mining and we designed an extension of WAP-Tree data structure for multi-it...
Extracting process hierarchies by multi-sequence alignment adaptations
Esgin, Eren; Karagöz, Pınar (2021-01-01)
Process mining is an active research area that provides a wide range of automated process discovery, conformance checking and process enhancement solutions by extracting process information from event logs. With the emerge of new shared economy models and system architectures, monolithic perspective of process mining, i.e. a single process within a single organisation, is evolved towards cross-organisational business processes. The results can be used to form new collaboration among the organisations and sh...
Process Profiling based Synthetic Event Log Generation
ESGIN, EREN; Karagöz, Pınar (2019-09-19)
The goal of process mining is to discover the process behavior from the runtime information of process executions. Having labeled process logs is crucial for process mining research. However, real life event logs at process-aware information systems are mostly partially assigned to case identifiers, known as unlabeled event log problem. As a remedy to labeled data need in process mining research, we propose an approach to generate synthetic event logs according to the provided process profile, which outline...
Citation Formats
E. Esgin and P. Karagöz, “A Hybrid Approach to Process Mining: Finding Immediate Successors of a Process by Using From-To Chart,” 2009, p. 664, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62546.