nach oben

Erschienen in:

2015 | OriginalPaper | Buchkapitel

Dynamic Constructs Competition Miner - Occurrence- vs. Time-Based Ageing

verfasst von : David Redlich, Thomas Molka, Wasif Gilani, Gordon Blair, Awais Rashid

Erschienen in: Data-Driven Process Discovery and Analysis

Verlag: Springer International Publishing

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Since the environment for businesses is becoming more competitive by the day, business organizations have to be more adaptive to environmental changes and are constantly in a process of optimization. Fundamental parts of these organizations are their business processes. Discovering and understanding the actual execution flow of the processes deployed in organizations is an important enabler for the management, analysis, and optimization of both, the processes and the business. This has become increasingly difficult since business processes are now often dynamically changing and may produce hundreds of events per second. The basis for this paper is the Constructs Competition Miner (CCM): A divide-and-conquer algorithm which discovers block-structured processes from event logs possibly consisting of exceptional behaviour. In this paper we propose a set of modifications for the CCM to enable dynamic business process discovery of a run-time process model from a stream of events. We describe the different modifications with a particular focus on the influence of individual events, i.e. ageing techniques. We furthermore investigate the behaviour and performance of the algorithm and the ageing techniques on event streams of dynamically changing processes.

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 "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

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

Vorheriges Kapitel History-Based Construction of Alignments for Conformance Checking: Formalization and Implementation

Nächstes Kapitel Trustworthy Cloud Certification: A Model-Based Approach

footprint is a term used in the process discovery domain, abstractly representing existent “behaviour” of a log, e.g. activity “a” is followed by activity “b”.

This stands in contrast to existing discovery solutions since in the CCM the footprint and its interpretation is not based on local relationships between activity occurrences, e.g. direct neighbours, but based on global relationships between them.

e.g. for \(A = \{a,b,c,d\}: (a,b,c,d) \rightarrow ((a,b,c),(d)) \rightarrow (((a),(b,c)),(d))\) \(\rightarrow \) (((a), ((b), (c))), (d)), seven different footprints for sets \(\{a,b,c,d\},\{a,b,c\},\{b,c\},\{a\},\) \(\{b\},\{c\},\{d\}\) need to be created - (, ) denote the nested blocks that emerge while splitting the sets recursively.

the occurrence values for activities as well as the global relations (see end of Sect. 4) are represented in the trace footprint by absolute statements \({true} \equiv 1\) if it occurred and \({false} \equiv 0\) if not.

In rare cases (if Loop and Parallel constructs dominate) this modification can have a negative effect on the execution-time since extra information needs to be extracted without the benefit of mining less sub-footprints.

Time-based ageing is technically based on an ageing rate \(a_r\) rather than trace influence factor \(t_{if}\). However, \(a_r\) can be derived from \(t_{if}\), i.e. \(a_r = 1-t_{if}\).

Note, that other changes like deletion of an activity will take longer to recognise, since their existence still “lingers” in the footprints “memory” for some time.

if \(t_{if}\) is set too high normal behaviour unintentionally becomes exceptional behaviour.

von Ammon, R., Ertlmaier, T., Etzion, O., Kofman, A., Paulus, T.: Integrating complex events for collaborating and dynamically changing business processes. In: Dan, A., Gittler, F., Toumani, F. (eds.) ICSOC/ServiceWave 2009. LNCS, vol. 6275, pp. 370–384. Springer, Heidelberg (2010)

Bose, R.P.J.C., van der Aalst, W.M.P., Žliobaitė, I., Pechenizkiy, M.: Handling concept drift in process mining. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 391–405. Springer, Heidelberg (2011)CrossRef

Cattafi, M., Lamma, E., Riguzzi, F., Storari, S.: Incremental declarative process mining. In: Szczerbicki, E., Nguyen, N.T. (eds.) Smart Information and Knowledge Management. SCI, vol. 260, pp. 103–127. Springer, Heidelberg (2010) CrossRef

Buijs, J., Van Dongen, B., Van Der Aalst, W.: A genetic algorithm for discovering process trees. In: Evolutionary Computation (CEC), pp. 1–8. IEEE (2012)

Burattin, A., Sperduti, A., Van Der Aalst, W.: Heuristics miners for streaming event data. CoRR abs/1212.6383 (2012)

Kindler, E., Rubin, V., Schäfer, W.: Incremental workflow mining based on document versioning information. In: Li, M., Boehm, B., Osterweil, L.J. (eds.) SPW 2005. LNCS, vol. 3840, pp. 287–301. Springer, Heidelberg (2006) CrossRef

Kindler, E., Rubin, V., Schäfer, W.: Activity mining for discovering software process models. In: Software Engineering 2006, Fachtagung des GI-Fachbereichs Softwaretechnik, LNI, pp. 175–180. GI (2006)

Ko, R.K.L.: A computer scientist’s introductory guide to business process management (BPM). ACM Crossroads J. 15(4), 11–18 (2009)CrossRef

Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013) CrossRef

10.

Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013 Workshops. LNBIP, vol. 171, pp. 66–78. Springer, Heidelberg (2014) CrossRef

11.

Luckham, D.: The Power of Events: An Introduction to Complex Event Processingin Distributed Enterprise Systems. Addison-Wesley Professional, Reading (2002)

12.

Maggi, F.M., Burattin, A., Cimitile, M., Sperduti, A.: Online process discovery to detect concept drifts in LTL-based declarative process models. In: Meersman, R., Panetto, H., Dillon, T., Eder, J., Bellahsene, Z., Ritter, N., Leenheer, P., Dou, D. (eds.) ODBASE 2013. LNCS, vol. 8185, pp. 94–111. Springer, Heidelberg (2013) CrossRef

13.

Manku, G.S., Motwani, R.: Approximate frequency counts over data streams. In: VLDB 2002, Proceedings of 28th International Conference on Very Large Data Bases, pp. 346–357. Morgan Kaufmann (2002)

14.

OMG Inc.: Business Process Model and Notation (BPMN) Specification 2.0. formal/2011-01-03 (2011). http://www.omg.org/spec/BPMN/2.0/PDF

15.

Redlich, D., Molka, T., Gilani, W., Blair, G., Rashid, A.: Constructs competition miner: process control-flow discovery of BP-domain constructs. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 134–150. Springer, Heidelberg (2014)

16.

Redlich, D., Gilani, W., Molka, T., Drobek, M., Rashid, A., Blair, G.: Introducing a framework for scalable dynamic process discovery. In: Aveiro, D., Tribolet, J., Gouveia, D. (eds.) EEWC 2014. LNBIP, vol. 174, pp. 151–166. Springer, Heidelberg (2014) CrossRef

17.

Redlich, D., Blair, G., Rashid, A., Molka, T., Gilani, W.: Research challenges for business process models at run-time. In: Bencomo, N., France, R., Cheng, B.H.C., Aßmann, U. (eds.) Models@run.time. LNCS, vol. 8378, pp. 208–236. Springer, Heidelberg (2014)

18.

Solé, M., Carmona, J.: Incremental process mining. In: Proceedings of the Workshops of the 31st International Conference on Application and Theory of Petri Nets and Other Models of Concurrency (PETRI NETS 2010) and of the 10th International Conference on Application of Concurrency to System Design (ACSD 2010). CEURWorkshop Proceedings, pp. 175190. CEUR-WS.org (2010)

19.

Van Der Aalst, W., Ter Hofstede, A.: YAWL: Yet Another Workflow Language (2003)

20.

Van Der Aalst, W., Weijters, A., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)CrossRef

21.

Van Der Aalst, W., Van Dongen, B.: ProM: the process mining toolkit. Ind. Eng. 489, 1–4 (2009)

22.

van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012) CrossRef

23.

Van Der Aalst, W.: Process Mining - Discovery, Conformance and Enhancementof Business Processes. Springer, Heidelberg (2011)

24.

Van Der Aalst, W., Adriansyah, A., Van Dongen, B.: Replaying history on process models for conformance checking and performance analysis. WIREs Data Min. Knowl. Discov. 2(2), 182–192 (2012)CrossRef

25.

Weijters, A., Van Der Aalst, W., Alves de Medeiros, A.: Process mining with the heuristics miner-algorithm. BETA Working Paper Series, WP 166, Eindhoven University of Technology (2006)

Titel: Dynamic Constructs Competition Miner - Occurrence- vs. Time-Based Ageing
verfasst von: David Redlich
Thomas Molka
Wasif Gilani
Gordon Blair
Awais Rashid
Verlag: Springer International Publishing
Buch: Data-Driven Process Discovery and Analysis
Print ISBN: 978-3-319-27242-9

Electronic ISBN: 978-3-319-27243-6

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-27243-6_4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner