Efficient business process consolidation: combining topic features with structure matching
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Accurate and effective business process consolidation is an efficient means of overcoming the dynamics and uncertainty in business process modeling. This article presents an approach to automating business process consolidation by applying process topic clustering based on business process libraries, using a graph mining algorithm to extract process patterns, identifying frequent subgraphs under the same process topic, filling the pertinent subgraph information into a table of frequent process subgraphs, and finally merging these frequent subgraphs to obtain merged business processes using a process merging algorithm. Tests on 604 models from the SAP reference model were performed, in which we used the compression ratio to judge the capability of our merging methods; the compression ratios of integrated processes in the same topic cluster were found to be much lower than those of processes related to different topics, and our method was found to achieve compression ratios similar to those reported in previous work.
KeywordsCorrelated topic model Topic distillation Business process merging gSpan Process subgraph
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61573157, 61562038 and 61562703, the Natural Science Foundation of Jiangxi Province under Grant No. 20142BAB217028, the Science and Technology Planning Project of Guangdong Province of China under Grant No. 2015B010129015.
Compliance with ethical standards
Conflict of interest
The authors declare there is no conflict of interests regarding the publication of this paper.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Blei D, Lafferty J (2006) Correlated topic models. Adv Neural Inf Process Syst 18:147Google Scholar
- Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022. doi: 10.1162/jmlr.2003.3.4-5.993
- Chen L, Wang Y, Qi Y, Zheng Z, Wu J (2013) WT-LDA: user tagging augmented LDA for Web service clustering. In: Basu S, Pautasso C, Zhang L, Fu X (eds) Service-oriented computing (11th international conference, ICSOC 2013, Berlin, Germany, December 2–5, 2013). Springer, Berlin, Heidelberg, pp 162–176Google Scholar
- Dijkman R, Dumas M, García-Bañuelos L (2009) Graph matching algorithms for business process model similarity search. In: Dayal U, Eder J, Koehler J, Reijers H (eds) Business process management (7th international conference, BPM 2009, Ulm, Germany, September 8–10, 2009). Springer, Berlin, Heidelberg, pp 48–63Google Scholar
- Gottschalk F, van der Aalst WMP, Jansen-Vullers MH (2008) Merging event-driven process chains. In: Meersman R, Tari Z (eds) On the move to meaningful internet systems: OTM 2008 (OTM 2008 confederated international conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008, Monterrey, Mexico, November 9–14, 2008, proceedings, part I). Springer, Berlin, Heidelberg, pp 418–426. doi: 10.1007/978-3-540-88871-0_28
- Küster J, Gerth C, Förster A, Engels G (2008b) A tool for process merging in business-driven development. In: Bellahsène Z, Coletta R, Franch X, Hunt E, Woo C (eds) Proceedings of the of the forum at the 20th international conference on advanced information systems engineering (CaiSE). CEUR, pp 89–92Google Scholar
- Küster J, Ryndina K, Gall H (2007) Generation of business process models for object life cycle compliance. In: Alonso G, Dadam P, Rosemann M (eds) Business process management (5th international conference, BPM 2007, Brisbane, Australia, September 24–28, 2007). Springer, Berlin Heidelberg, pp 165–181Google Scholar
- Ma DC, Lin JYC, Orlowska ME (2007) Automatic merging of work items in business process management systems. In: Ma D, Lin J, Orlowska M (eds) Business information systems (10th international conference, BIS 2007, Poznan, Poland, April 25–27, 2007). Springer, Berlin, Heidelberg, pp 14–28Google Scholar
- Mendling J, Simon C (2006) Business process design by view integration. In: Eder J, Dustdar S (eds) Business process management workshops (BPM 2006 international workshops, BPD, BPI, ENEI, GPWW, DPM, semantics4ws, Vienna, Austria, September 4–7, 2006). Springer Berlin, Heidelberg, pp 55–64. doi: 10.1007/11837862_7
- Nejati S, Sabetzadeh M, Chechik M, Easterbrook S, Zave P (2007) Matching and merging of statecharts specifications. In: ICSE’07 proceedings of the 29th international conference on software engineering. IEE Computer Society, Washington, DC, pp 54–63Google Scholar
- Ohst D, Welle M, Kelter U (2003) Differences between versions of UML diagrams. In: ESEC/FSE-11 proceedings of the 9th European software engineering conference held jointly with 11th ACM SIGSOFT international symposium on foundations of software engineering. ACM, New York, pp 227–236Google Scholar
- Qiao M, Akkiraju R, Rembert AJ (2011) Towards efficient business process clustering and retrieval: combining language modeling and structure matching. In: Rinderle-Ma S, Toumani F, Wolf K (eds) Business process management (9th international conference, BPM 2011, Clermont-Ferrand, France, August 30–September 2, 2011). Springer, Berlin, Heidelberg, pp 199–214Google Scholar
- van Dongen B, Dijkman R, Mendling J (2008) Measuring similarity between business process models. In: Bellahsène Z, Léonard M (eds) Advanced information systems engineering (20th international conference, CAiSE 2008 Montpellier, France, June 16–20, 2008). Springer, Berlin Heidelberg, pp 450–464. doi: 10.1007/978-3-540-69534-9_34
- Weske M (2007) Business process management: concepts, languages, architectures. Springer, BerlinGoogle Scholar
- Yan X, Han J (2002) gSpan:graph-based substructure pattern mining. In: 2002 IEEE international conference on data mining, 2002. ICDM 2003. Maebashi City, Japan, pp 721–724. doi: 10.1109/ICDM.2002.1184038
- Zheng Y, Jeon B, Xu D, Wu QM, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28(2):961–973Google Scholar