Sequence Alignment Adaptation for Process Diagnostics and Delta Analysis


Esgin E., KARAGÖZ P.

8th International Conference on Hybrid Artificial Intelligent Systems (HAIS), Salamanca, Mexico, 11 - 13 September 2013, vol.8073, pp.191-201 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 8073
  • City: Salamanca
  • Country: Mexico
  • Page Numbers: pp.191-201
  • Keywords: Process Mining, Sequence Alignment, Process Diagnostics, Dominant Behavior, Needleman-Wunsch Algorithm, PROCESS MODELS, SIMILARITY, DISTANCE, LOGS

Abstract

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 as protein sequences to identify similar regions. In this study, we focus on a hybrid quantitative approach for performing process diagnostics, i.e. comparing the similarity among process models based on the established dominant behavior concept and Needleman-Wunsch algorithm.