Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Soft Computing
Erschienen in: Soft Computing 2/2018

28.09.2016 | Methodologies and Application

Efficient business process consolidation: combining topic features with structure matching

verfasst von: Ying Huang, Wei Li, Zhengping Liang, Yu Xue, Xiuni Wang

Erschienen in: Soft Computing | Ausgabe 2/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

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.
Vorheriger Artikel Multichannel image denoising using color monogenic curvelet transform
Nächster Artikel Evolutionary programming with a simulated-conformist mutation strategy

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
Aznag M, Quafafou M, Rochd EM, Jarir Z (2013) Probabilistic topic models for web services clustering and discovery. In: Lau KK, Lamersdorf W, Pimentel E (eds) Service-oriented and cloud computing. Springer, Berlin, pp 19–33CrossRef
Blei D, Lafferty J (2006) Correlated topic models. Adv Neural Inf Process Syst 18:147
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 162–176
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 48–63
Gu B, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416MathSciNetCrossRef
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
Khan I, Huang JZ, Ivanov K (2016) Incremental density-based ensemble clustering over evolving data streams. Neurocomputing 191:34–43CrossRef
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 89–92
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 165–181
La Rosa M, Dumas M, Uba R, Dijkman R (2013) Business process model merging: an approach to business process consolidation. ACM Trans Softw Eng Methodol 22:11. doi:10.​1145/​2430545.​2430547
Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions and reversals. Sov Phys Doklady 10:707–710MathSciNetMATH
Li Y, Cao B, Xu L, Yin J, Deng S, Yin Y, Wu Z (2014) An efficient recommendation method for improving business process modeling. IEEE Trans Ind Inform 10:502–513CrossRef
Lu Y, Zhang L, Sun J (2009) Task-activity based access control for process collaboration environments. Comput Ind 60(6):403–415CrossRef
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 14–28
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 54–63
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 227–236
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 199–214
Salomon D (2006) Data compression: the complete reference, 4th edn. Springer, New YorkMATH
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, Berlin
Xia Z, Wang X, Sun X, Wang B (2014) Steganalysis of least significant bit matching using multi-order differences. Sec Commun Netw 7(8):1283–1291CrossRef
Xia Z, Wang X, Sun X, Liu Q, Xiong N (2016) Steganalysis of LSB matching using differences between nonadjacent pixels. Multimed Tools Appl 75(4):1947–1962CrossRef
Xie J, Gao H, Xie W, Liu X, Grant PW (2016) Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors. Inf Sci 354:19–40CrossRef
Xu L, Hu Q, Hung E, Chen B, Tan X, Liao C (2015) Large margin clustering on uncertain data by considering probability distribution similarity. Neurocomputing 158:81–89CrossRef
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
Yang Y, Jiang J (2016) Hybrid sampling-based clustering ensemble with global and local constitutions. IEEE Trans Neural Netw Learn Syst 27(5):952–965MathSciNetCrossRef
Zhang X, Xu C, Sun X, Baciu G (2016) Schatten-q regularizer constrained low rank subspace clustering model. Neurocomputing 182:36–47CrossRef
Zhang L, Lu Y, Xu F (2010) Unified modelling and analysis of collaboration business process based on Petri nets and Pi calculus. IET Softw 4(5):303–317CrossRef
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):961–973
Metadaten
Titel
Efficient business process consolidation: combining topic features with structure matching
verfasst von
Ying Huang
Wei Li
Zhengping Liang
Yu Xue
Xiuni Wang
Publikationsdatum
28.09.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 2/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-016-2364-y

Weitere Artikel der Ausgabe 2/2018

Soft Computing 2/2018 Zur Ausgabe

Methodologies and Application

Artificial bee colony algorithm with an adaptive greedy position update strategy

Methodologies and Application

A niching chaos optimization algorithm for multimodal optimization

Methodologies and Application

Multichannel image denoising using color monogenic curvelet transform

Focus

Toward automatic generation of linguistic advice for saving energy at home

Foundations

Particle swarm optimization algorithm: an overview

Methodologies and Application

Model-based control using interval type-2 fuzzy logic systems