Sequence alignment based process family extraction

Download
index.pdf
2018
Esgin, Eren
Show full item record
94
views
34
downloads
Business Process Management (BPM) gains growing attention by generic process design and execution capabilities empowered by process-aware information systems. During execution of these transactional information systems, end-users leave traces in the form of event logs, which can be used as a main data source for behavior analysis. Process mining encompasses the techniques for automatically discovering process from these event logs, checking conformance between the reference process model and process executions, as well as analyzing, predicting and enhancing the performance of business processes. With the emergence of new shared economical models and system architectures, monolithic process perspective is evolved through cross-organizational applications. While contemporary information systems provide functionality for process management within the organizations, a systematic approach to support and analyze multi-organizational processes is missing. Cross-organizational process mining supports the use of commonality and collaboration for process configuration. However, this functionality creates the challenge of dealing with variability across organizations. In this study, we propose a three phased cross-organizational process mining framework in order to extract the commonalities among different organizations serving the same business values. While dominant behavior extraction phase initially derives the sequence of tasks expressing the most typical behavior within the process instances, sequence alignment phase measures the degree of similarities between the process candidates by confidence enhanced cost functioning, and depicts the neighborhood among these alternatives in terms of process families. At process configuration phase, common regions that indicate a functional inheritance or abstractions in the process families are visualized at sequence alignment matrices and interpreted by new feature sets, namely identical and maximal identical pair. According to the experimental results, proposed approach presents a viable and robust cost function in incorporating the business context at process similarity measurement and clustering the process alternatives into process families.
Process control., System analysis., Business intelligence., Industrial engineering.
http://etd.lib.metu.edu.tr/upload/12622915/index.pdf
https://hdl.handle.net/11511/27909
Graduate School of Informatics, Thesis

Suggestions

Sequence Alignment Adaptation for Process Diagnostics and Delta Analysis
Esgin, Eren; Karagöz, Pınar (2013-09-13)
Business process management (BPM) paradigm gains growing attention by providing generic process design and execution capabilities. During execution, many business processes leave casual footprints (event logs) at these transactional information systems. Process mining aims to extract business processes by distilling event logs for knowledge. Sequence alignment is a technique that is frequently used in domains including bioinformatics, language/text processing and finance. It aims to arrange structures, such...
An investigation of the relationship between joint visual attention and product quality in collaborative business process modeling: a dual eye-tracking study
Fındık Coşkunçay, Duygu; Çakır, Murat Perit (2022-02-01)
Collaborative business process modeling is a collective activity where team members jointly discuss, design, and document business processes. During such activities, team members need to communicate with each other to coordinate the modeling activities, propose and justify changes, and negotiate common terms and definitions. Throughout this process, stakeholders should be aware of when and what kind of changes have been made by each team member on the shared space so that they can discuss design ideas and b...
Delta Analysis: A Hybrid Quantitative Approach for Measuring Discrepancies between Business Process Models
Esgin, Eren; Karagöz, Pınar (2011-05-25)
Business process management (BPM) continues to play a significant role in today's highly globalized world. In order to detect and prevent the gap between reference process model and the actual operation, process mining techniques discover operational model on the basis of the process logs. An important issue at BPM is to measure the similarity between the reference process model and discovered process model so that it can be possible to pinpoint where process participants deviate from the intended process d...
A Taxonomy for Personal Processes: Results from a Semi-structured Interview
Oruç, Sercan; Eren, Pekin Erhan; Koçyiğit, Altan; Yeralan, Sencer (2019-01-01)
There are few studies conducted on personal processes within the Business Process Management (BPM) domain. Personal processes are looser and more context- and person-dependent compared to the clearly defined business processes. This makes it more challenging to create solutions in this domain. In this study, a taxonomy is developed for personal processes. We used the data collected from semi-structured interviews that we have conducted with a diverse population. We built a taxonomy with 4 classes and 22 sub...
A hybrid methodology in process modeling: “from-to chart based process discovery”
Esgin, Eren; Karagöz, Pınar; Department of Information Systems (2009)
The managing of complex business processes, which are changed due to globalization, calls for the development of powerful information systems that offer generic process modeling and process execution capabilities. Even though contemporary information systems are more and more utilized in enterprises, their actual impact in automatizing complex business process is still limited by the difficulties encountered in design phase. Actually this design phase is time consuming, often subjective and incomplete. In t...
Citation Formats
E. Esgin, “Sequence alignment based process family extraction,” Ph.D. - Doctoral Program, Middle East Technical University, 2018.
METU IIS - Integrated Information Service open@metu.edu.tr