Sequence Alignment Adaptation for Process Diagnostics and Delta Analysis

Esgin E., KARAGÖZ P.

8th International Conference on Hybrid Artificial Intelligent Systems (HAIS), Salamanca, Meksika, 11 - 13 Eylül 2013, cilt.8073, ss.191-201 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 8073
  • Basıldığı Şehir: Salamanca
  • Basıldığı Ülke: Meksika
  • Sayfa Sayıları: ss.191-201
  • Anahtar Kelimeler: Process Mining, Sequence Alignment, Process Diagnostics, Dominant Behavior, Needleman-Wunsch Algorithm, PROCESS MODELS, SIMILARITY, DISTANCE, LOGS
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.