An Impact of the User and Time Parameters to Sequence Alignment Methods for Process Mining

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• 作者：Jakub 艩tolfa (17)
  Svatopluk 艩tolfa (17)
  Kate艡ina Slaninov谩 (17) (18)
  Jan Martinovi膷 (17) (18)
  V谩clav Sn谩拧el (17) (18)
  
• 关键词：Sequence Alignment Methods ; Process Mining
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8838
• 期：1
• 页码：580-591
• 全文大小：986 KB
• 参考文献：1. de Weerdt, J., Schupp, A., Vanderloock, A., Baesens, B.: Process mining for the multi-faceted analysis of business processes - a case study in a financial services organization. Computers in Industry聽64(1), 57鈥?7 (2013) CrossRef
  2. Esgin, E., Karagoz, P.: Sequence alignment adaptation for process diagnostics and delta analysis. In: Pan, J.-S., Polycarpou, M.M., Wo藕niak, M., de Carvalho, A.C.P.L.F., Quinti谩n, H., Corchado, E. (eds.) HAIS 2013. LNCS (LNAI), vol.聽8073, pp. 191鈥?01. Springer, Heidelberg (2013) CrossRef
  3. Guo, A., Siegelmann, H.: Time-warped longest common subsequence algorithm for music retrieval. In: Buyoli, C.L., Loureiro, R. (eds.) Proceedings of 5th International Conference on Music Information Retrieval, ISMIR 2004, pp. 258鈥?61. Universitat Pompeu Fabra (2004)
  4. Gusfield, D.: Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. Cambridge University Press (2008)
  5. Hirschberg, D.S.: Algorithms for the longest common subsequence problem. J. ACM聽24, 664鈥?75 (1977) CrossRef
  6. 艩tolfa, S.J., 艩tolfa, S., Slaninov谩, K., Martinovi膷, J.: Searching time series based on pattern extraction using dynamic time warping. In: Proceedings of the Dateso 2014 Annual International Workshop on DAtabases, TExts, Specifications and Objects. CEUR Workshop Proceedings, pp. 81鈥?0 (2014)
  7. Kocyan, T., Martinovi膷, J., Dr谩啪dilov谩, P., Slaninov谩, K.: Searching time series based on pattern extraction using dynamic time warping. In: Proceedings of the Dateso 2013 Annual International Workshop on DAtabases, TExts, Specifications and Objects. CEUR Workshop Proceedings, pp. 129鈥?38 (2013)
  8. M眉ller, M.: Information Retrieval for Music and Motion. Springer (2007)
  9. Slaninov谩, K., Kocyan, T., Martinovi膷, J., Dr谩啪dilov谩, P., Sn谩拧el, V.: Dynamic time warping in analysis of student behavioral patterns. In: Proceedings of the Dateso 2012 Annual International Workshop on DAtabases, TExts, Specifications and Objects. CEUR Workshop Proceedings, pp. 49鈥?9 (2012)
  10. Slaninov谩, K., Martinovi膷, J., Novos谩d, T., Dr谩啪dilov谩, P., Voj谩膷ek, L., Sn谩拧el, V.: Web site community analysis based on suffix tree and clustering algorithm. In: Proceedings of the 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2011, pp. 110鈥?13 (2011)
  11. Slaninov谩, K., Martinovi膷, J., 艩perka, R., Dr谩啪dilov谩, P.: Extraction of agent groups with similar behaviour based on agent profiles. In: Saeed, K., Chaki, R., Cortesi, A., Wierzcho艅, S. (eds.) CISIM 2013. LNCS, vol.聽8104, pp. 348鈥?57. Springer, Heidelberg (2013) CrossRef
  12. van der Aalst, W., Weijters, A.: Process mining: A research agenda. Computers in Industry聽53(3), 231鈥?44 (2004) CrossRef
  13. van der Aalst, W., Weijters, A., Maruster, L.: Workflow mining: Discovering process models from event logs. Transaction on Knowledge and Data Engineering聽16(9), 1128鈥?142 (2004) CrossRef
  14. van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The prom framework: A new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol.聽3536, pp. 444鈥?54. Springer, Heidelberg (2005) CrossRef
  15. 艩tolfa, J., Kopka, M., 艩tolfa, S., Kob臎rsk媒, O., Sn谩拧el, V.: An application of process mining to invoice verification process in sap. In: Abraham, A., Kr枚mer, P., Sn谩拧el, V. (eds.) Innovations in Bio-inspired Computing and Applications. AISC, vol.聽237, pp. 61鈥?4. Springer, Heidelberg (2014) CrossRef
  
• 作者单位：Jakub 艩tolfa (17)
  Svatopluk 艩tolfa (17)
  Kate艡ina Slaninov谩 (17) (18)
  Jan Martinovi膷 (17) (18)
  V谩clav Sn谩拧el (17) (18)
  
  17. Department of Computer Science, FEECS, V艩B, Technical University of Ostrava, 17. Listopadu 15, 708 33, Ostrava-Poruba, Czech Republic
  18. IT4Innovations, V艩B, Technical University of Ostrava, 17. Listopadu 15, 708 33, Ostrava-Poruba, Czech Republic
  
• ISSN：1611-3349

文摘

Process mining is relatively new domain that opens many opportunities for process control and improvement. Anyway, the basis of the process mining is the examination of bunch of data from processes. There are many methods that already had been used in this domain and many other are still waiting for the discovery of their benefits. One of the main issues is to find out whether the new method is useful or not. The main purpose of this paper is to present the usability of sequence alignment method in process mining especially from the user and time perspective.