Process Oriented Data Virtualization Design Model for Business Processes Evaluation(PODVDM) Research in Progress
DOI:
https://doi.org/10.11113/jt.v72.3926Keywords:
Business intelligence, business process management, process warehouse, process evaluation, data virtualization, data warehouseAbstract
During process enactment in the business process management (BPM) lifecycle, information collected on execution plans are stored in the form of log files and database tables by using information systems (IS). In the past decade, a new approach based on the applications of Business Intelligence (BI) in business process management has emerged. The approach implements process-oriented data warehouse and mining techniques. However, the main issue is providing the right information at the right time to facilitate process evaluation that can be used for performance analysis and improve business process. Existing techniques have limitations, including huge data in database log files, performance of Process Warehouse (PW), which is highly dependent on specific design), complexity of PW design, lack of convergence between business processes and PW specifications, and the need for real data during process evaluation stage. Objects such as processes, storage, and data repositories can be virtualized to address these limitations. The main aim of this study is to propose a process-oriented data virtualization design model for process evaluation in BPM. The model will be validated through expert reviews and prototype development as well as through a case study. In this paper, we describe the research motivation, questions, approach, and methodology related to addressing the described limitations by designing a model for evaluation in business processes using the Data Virtualization technique.
References
B. Dwolatzky, I. Kennedy, and J. Owens. 2002. Modern Software Engineering Methods for Developing Courseware.
F. Gacenga, A. Cater-Steel, M. Toleman et al. 2012. A Proposal and Evaluation of a Design Method in Design Science Research. Electronic Journal of Business Research Methods. 10(2).
A. R. Hevner, S. T. March, J. Park et al. 2004. Design Science in Information Systems Research. MIS Quarterly. 28(1): 75–105.
P. Offermann, O. Levina, M. Schönherr et al. Outline of a Design Science Research Process. 7.
C. Houy, P. Fettke, and P. Loos. 2010. Empirical Research in Business Process Management–Analysis of an Emerging Field of Research. Business Process Management Journal. 16(4): 619–661.
M. Weske, 2012. Business Process Management: Concepts, Languages, Architectures. Springer.
G. Aagesen, and J. Krogstie. 2010. Analysis and Design of Business Processes using BPMN. Handbook on Business Process Management 1. Springer. 213–235:
S. Adesola, and T. Baines. 2005.“Developing and Evaluating a Methodology for Business Process Improvement. Business Process Management Journal. 11(1): 37–46.
T. Bucher, A. Gericke, and S. Sigg. 2009. Process-centric Business Intelligence. Business Process Management Journal. 15(3): 408–429.
A. Pourshahid, D. Amyot, L. Peyton et al. 2009. Business Process Management with the User Requirements Notation. Electronic Commerce Research. 9(4): 269–316.
R. G. Lee, and B. G. Dale. 1998. Business Process Management: A Review and Evaluation. Business Process Management Journal. 4(3): 214–225.
W. M. Van Der Aalst, A. H. Ter Hofstede, and M. Weske. 2003. Business Process Management: A Survey. Springer,
M. Ferguson. 2011. Succeeding with Data Virtualization High Value Use Cases for Analytical Data Services.
B. Hopkins. 2011. Data Virtualization Reaches the Critical Mass.
R. F. v. d. Lans. 2012. Data Virtualization for Business Intelligence Systems: Revolutionizing Data Integration for Data Warehouses.
A. H. Mousa, N. Shiratuddin, and M. S. A. Bakar. 2014. Virtual Data Mart for Measuring Organizational Achievement Using Data Virtualization Technique (KPIVDM). Jurnal Teknologi. 68(3).
R. Eve, and J. R. Davis. 2011. Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility. Composite Software.
R. Van der Lans. 2012. Data Virtualization for Business Intelligence Systems: Revolutionizing Data Integration for Data Warehouses. Morgan Kaufmann.
L. Weng, G. Agrawal, U. Catalyurek et al. An Approach for Automatic Data Virtualization. 24–33.
A. Lodhi, V. Küppen, and G. Saake. 2011. An Extension of Bpmn Meta-model for Evaluation of Business Processes. Scientific Journal of Riga Technical University. Computer Sciences. 43(1): 27–34.
M. Zur Mühlen, and R. Shapiro. 2010. Business Process Analytics. Handbook on Business Process Management. 2: 137–157: Springer,
B. List, J. Schiefer, A. M. Tjoa et al. 2000. Multidimensional Business Process Analysis with the Process Warehouse.
B. List, and B. Korherr. An Evaluation of Conceptual Business Process Modelling Languages. 1532–1539.
V. Stefanov, and B. List. Explaining Data Warehouse Data to Business Users-A Model-Based Approach to Business Metadata.
Shahzad, and C. Giannoulis. Towards a Goal-Driven Approach for Business Process Improvement Using Process-Oriented Data Warehouse. 111–122.
Shahzad. A Data Warehouse Model for Integrating Fuzzy Concepts in Meta Table Structures.
G. Zellner. 2011. A Structure Evaluation of Business Process Improvement Approaches. Business Process Management Journal. 17(2): 203–237.
A. Sturm. 2012. Supporting Business Process Analysis Via Data Warehousing. Journal of Software: Evolution and Process. 24(3): 303–319.
H. Xia, Q. Yao, and F. Gao. Research and Design of Process Data Warehouse for Business Process Assessment. 377–385.
C. Jossen, L. Blunschi, M. Mori et al. The Credit Suisse Meta-data Warehouse. 1382–1393.
J. L. Liutong Xu, Ruixue Zhao, Bin Wu. 2011. A Paas Based Metadata-Driven Etl Framework. IEEE. 5,
R. J. Santos, J. Bernardino, and M. Vieira. 24/7 Real-Time Data Warehousing: A Tool for Continuous Actionable Knowledge. 279–288.
D. Grigori, F. Casati, M. Castellanos et al. 2004. Business Process Intelligence. Computers in Industry. 53(3): 321–343.
M. Castellanos, A. Simitsis, K. Wilkinson et al. Automating the Loading of Business Process Data Warehouses. 612–623.
F. Casati, M. Castellanos, U. Dayal et al. A Generic Solution for Warehousing Business Process Data. 1128–1137.
M. Sayal, F. Casati, U. Dayal et al. Business Process Cockpit. 880–883.
W. Tan, W. Shen, L. Xu et al. 2008. A Business Process Intelligence System for Enterprise Process Performance Management. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on. 38(6): 745–756.
N. L. Sarda. 2001. Structuring Business Metadata in Data Warehouse Systems for Effective Business Support. arXiv preprint cs/0110020.
V. Stefanov, and B. List. Business Metadata for the DataWarehouse. 20–20.
C. McGregor, and S. Kumaran. Business Process Monitoring Using Web Services in B2B e-Commerce. 87.
C. McGregor, and J. Scheifer. A Framework for Analyzing and Measuring Business Performance with Web Services. 405–412.
A. Pourshahid, D. Amyot, L. Peyton et al. Toward an Integrated User Requirements Notation Framework and Tool for Business Process Management. 3–15.
E. R. Aguilar, F. Ruiz, F. GarcÃa et al. Evaluation Measures for Business Process Models. 1567–1568.
M. K. Shahzad. 2012. Improving Business Processes using Process Oriented Data Warehouse. Dissertation, Computer and Systems Sciences, KTH-Royal Institute of Technology, School of Information and Communication Technologies.
A. Bonifati, F. Casati, U. Dayal et al. Warehousing workflow data: Challenges and opportunities. 649–652.
M. Zur Muehlen. Process-driven Management Information Systems Combining Data Warehouses and Workflow Technology. 550–566.
C. W. Günther, and W. M. van der Aalst. A Generic Import Framework for Process Event Logs. 81–92.
D. Grigori, F. Casati, U. Dayal et al. 2001. Improving Business Process Quality Through Exception Understanding, Prediction, and Prevention.
J. Eder, G. E. Olivotto, and W. Gruber. 2002. A Data Warehouse for Workflow Logs. Engineering and Deployment of Cooperative Information Systems. 1–15. Springer,.
P. Kueng, T. Wettstein, and B. List. 2001. A Holistic Process Performance Analysis Through a Performance Data Warehouse. Citeseer.
J. Schiefer, B. List, and R. M. Bruckner. Process Data Store: A Real-time Data Store for Monitoring Business Processes. 760–770.
P. Giorgini, S. Rizzi, and M. Garzetti. Goal-oriented Requirement Analysis for Data Warehouse Design. 47–56.
K.-C. Pau, Y.-W. Si, and M. Dumas. Data Warehouse Model for Audit Trial Analysis in Workflows.
L. Niedrite, D. Solodovnikova, M. Treimanis et al. Goal-Driven Design of a Data Warehouse Based Business Process Analysis System. 243–249.
T. Neumuth, S. Mansmann, M. H. Scholl et al. Data Warehousing Technology for Surgical Workflow Analysis. 230–235.
S. Mansmann, T. Neumuth, and M. H. Scholl. 2007. Multidimensional Data Modeling for Business Process Analysis.Conceptual Modeling-ER 2007. 23–38: Springer,
K.-H. Kim, J.-H. Lee, and C.-M. Kim. 2005. A Real-time Cooperative Swim-lane Business Process Modeler. Computational Science and Its Applications–ICCSA 2005. 176–185: Springer.
J. Ghattas, and P. Soffer. 2009. Evaluation of Inter-organizational Business Process Solutions: A Conceptual Model-based Approach. Information Systems Frontiers. 11(3): 273–291.
M. D. Moeinaddin, Hassan Dehghan; Motahari, Saied. 2012 A Comprehensive Model for Performance Evaluation of Manufacturing Firms by Integrating Balanced Score Card and Fuzzy Analytic Network Process (A Case Study: Tile and Ceramic Manufacturers of Yazd Province). Interdisciplinary Journal of Contemporary Research in Business. 4(4): 18.
V. C. Yen. 2009. An Integrated Model for Business Process Measurement. Business Process Management Journal. 15(6): 865–875.
B. Kang, J.-Y. Jung, N. W. Cho et al. 2011. Real-time Business Process Monitoring Using Formal Concept Analysis. Industrial Management & Data Systems. 111(5): 652–674.
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