İş Akış Motoru Tasarımı ve Gerçekleştirilmesi

Yıl 2020, Cilt: 22 Sayı: 64, 219 - 231, 24.01.2020

Semih Utku , Oğuzhan Kayış

https://doi.org/10.21205/deufmd.2020226422

Öz

Bu çalışma özellikle büyük ölçekli
işletmelerin süreçlerinde kullandıkları bir yapı olan iş akışı motorunun
dinamik işleyecek şekilde bir tasarımını ve gerçekleştirilmesini kapsamaktadır.
Üzerinde çalıştığımız temel konu, geliştirdiğimiz sistemin farklı işletmelere
ve işletmeler içerisindeki farklı süreçlere uyum sağlayabilecek esneklikte
olmasıdır. Endüsriyel alanlarda iş süreci bir çok etmene göre zamanla
değişmektedir. Bu amaçla çalışma kapsamında iş akış motoruyla bütünleşik
çalışabilen bir kural motoru geliştirilmiştir. Kural motoru, iş akış motorunun
değişen iş süreçlerine çalışma zamanlı olarak uyum sağlamasını amaçlamaktadır. Tasarlanan
bu sistem, bir teknoloji ürünleri firmasında, ürünlere bağlı belgelerin takip
süreçlerini otomatize etmek üzere uygulanmıştır. Uygulama sonucunda toplanan
bilgiler, sıralı örüntü madenciliği yöntemleri kullanılarak analiz edilmiştir.

Anahtar Kelimeler

İş Akış Motoru, Kural Motoru, Sıralı Örüntü Madenciliği

Workflow Engine Design and Implementation

Öz

This study includes a design and
implementation of a workflow engine which is a structure that is used in the
processes dynamically of the large-scale enterprises. The main issue is creating
a dynamic system that is flexible enough to adapt to different businesses and
different processes within the enterprises. Business process in industrial
areas changes over time according to many factors. For this purpose, within the
scope of the study, a rule engine integrated with the workflow engine was
developed. The rule engine enables the workflow engine to adapt to changing
business processes at runtime. This system was designed to automate the
follow-up processes of the documents connected to the products in a technology
products company.  In addition, the
results of the study were analysed using sequential pattern mining methods.

Anahtar Kelimeler

Workflow Engine, Rule Engine, Sequential pattern mining

Kaynakça

• [1] Chung S., Synder C. 1999. ERP initiation -a historical perspective, Proceedings of AMCIS'99, s. 213-215.
• [2] Ouyang C., Adams M., Wynn M., Ter A. 2010. Workflow Management, Handbook on Business Process Management 1: Introduction, Methods, and Information Systems, s. 387-418 DOI: 10.1007/978-3-642-00416-2.
• [3] Marek R, Amit S. 1995. Specification and execution of transactional workflows. In Modern database systems, ACM Press/Addison-Wesley Publishing Co., New York, NY, USA, s. 592-620.
• [4] Bergmann S. 2007. Design and implementation of a workflow engine”, Diploma thesis of, Rhenish Friedrich Wilhelm University, Siegburg 92s.
• [5] McCoy, D. W. and Sinur J., Achieving Agility: The Agile Power of Business Rules, Gartner, Special report on Driving Enterprise Agility, 20 April 2006.
• [6] De Leusse P., Kwolek B., Zielinski K. 2012. A common interface for multi-rule-engine distributed systems, Distributed System Research Group, AGH University of Science and Technology Krakow, Poland
• [7] Diimitrios G., Mark H., Amit S. 1995. A n overview of workflow management: from process modeling to workflow automation infrastructure, Distributed and Parallel Databases Cilt 3, s 119-153.
• [8] Wil van der Aalst, Kees van Hee 2000. “Workflow management models, methods and systems”, Eindhoven, Hollanda.
• [9] Bouafia, K., Molnár B. 2018. Dynamic business process: comparative models and workflow patterns, The 11th conference of phd students ın computer scıence volume of short papers CS2. 194, June 2018 Szeged, Magyarország.
• [10] Krasimira P. S. Todor A. S. 1999. Evolution of the workflow management systems, Information system, Cilt 28 s 27-60.
• [11] De Lay, E, Jacobs D. 2011. Rules-based analysis with JBoss Drools: adding intelligence to automation, Proceedings of ICALEPCS 2011, s. 790-793.
• [12] Rakesh A., Dimitrios G., Frank L. 1998. Mining Process Models from Workflow Logs In Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology (EDBT '98), Hans-Jörg Schek, Fèlix Saltor, Isidro Ramos, and Gustavo Alonso (Eds.). Springer-Verlag, Berlin, Heidelberg, s 469-483.
• [13] Aalst W.M.P., F. van Dongen B., Herbst J. Măruşter L., Schimm G., Weijters A. 2003. Workflow mining: a survey of issues and approaches. Data & Knowledge Engineering. Cilt 47(2) s 237-267 DOI: 10.1016/S0169-023X(03)00066-1.
• [14] Singh G. N., Sandeep A. 2011. A process model for workflow mining, International Journal of Information Technology and Knowledge Management, Cilt. 4(2) s. 719-722.
• [15] Allen, R. 2001 Workflow: An Introduction" in: WfMC Workflow Handbook 2001, L. Fischer (ed.), Future Strategies Inc, Florida US, s15-38.
• [16] Lucas, S. M., Reynolds, T. J. 2005. Learning deterministic finite automata with a smart state labeling evolutionary algorithm. IEEE Transactions on Pattern Analysis & Machine Intelligence, Cilt 7, s. 1063-1074. DOI: 10.1109/TPAMI.2005.143
• [17] Xiangru XU, Yiguang H. 2012 .Matrix expression and reachability analysis of finite automata. J Control Theory Appl, 10(2) s 210-215.
• [18] Molnár, B., Máriás Z. 2015. Design and Implementation of a Workflow Oriented ERP System, International Conference on e-Business, s. 160-167.
• [19] ZETA Workflow http://zetacomponents.org/news.html (Erişim Tarihi 10.06.2019)
• [20] Van Der Aalst, W. M., Ter Hofstede, A. H. 2005. YAWL: yet another workflow language. Information systems, Cilt. 30(4), s. 245-275. DOI: 10.1016/j.is.2004.02.002
• [21] Pirt, B. 2004. Learning eZ publish 3: Building Content Management Solutions. Packt Publishing Ltd.
• [22] Galaxia Workflow https://galaxyproject.org/ learn/advanced-workflow/ (Erişim Tarihi 10.06.2019)
• [23] Radicore Workflow. https://www.radicore.org/ viewarticle.php?article_id=3 (Erişim Tarihi 10.06.2019)
• [24] Visual Workflow https://www.adobe.com/tr/ marketing/experience-manager-forms/visual-workflow-editor.html (Erişim Tarihi 10.06.2019)
• [25] Verve Worklow http://www.lateralsystems.com.au/verve-cms-1.html (Erişim Tarihi 10.06.2019)
• [26] Staffware http://www.workflowpatterns.com/vendors/staffware.php (Erişim Tarihi 10.06.2019)
• [27] MQSeries Workflow https://www.ibm.com/ support/knowledgecenter/en/SSFKSJ_7.1.0/com.ibm.mq.doc/fg15350_.htm (Erişim Tarihi 10.06.2019)
• [28] Ran, S., Brebner, P., Gorton, I. 2001. The rigorous evaluation of Enterprise Java Bean technology. In Proceedings 15th International Conference on Information Networking. s. 93-100. DOI: 10.1109/ICOIN.2001.905336
• [29] HP ChangeEngine http://www.hp.com/hpinfo /newsroom/press_kits/2009/HPSoftwareUniverseHamburg09/HPOODataSheet.pdf (Erişim Tarihi 10.06.2019)
• [30] Fujitsu i-Flow http://www.iflowbpm.com/ (Erişim Tarihi 10.06.2019)
• [31] SAP R/3 Workflow https://wiki.scn.sap.com /wiki/pages/viewpage.action?pageId=473964457 (Erişim Tarihi 10.06.2019)
• [32] Swamynathan, S., Geetha, T.V. 2014 Active rule engine for dynamic business ruleshttps://www.researchgate.net/publication/249903201_ACTIVE_RULE_ENGINE_FOR_DYNAMIC_BUSINESS_RULES (Erişim Tarihi 10.06.2019)
• [33] Desai, N., Ganatra, A. 2014. Draw Attention to Potential Customer with the Help of Subjective Measures in Sequential Pattern Mining (SPM) Approach, Conference: Proc. of Int. Conf. on Recent Trends in Information, Telecommunication and Computing, ITC.
• [34] Zhang S., Zhang C., Yang Q. 2003 Data preparation for data mining. Applied Artificial Intelligence,Cilt 17(5-6) s. 375-381.
• [35] WEKA, Open source machine learning tool. https://www.cs.waikato.ac.nz/ml/weka/ (Erişim Tarihi 10.06.2019)