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PhD Disputation

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  • 1. Improving Business Processes using Process-oriented Data Warehouse Muhammad Khurram Shahzad Doctoral Dissertation in Computer and Systems Sciences Supervised by: Paul Johannesson Jelena Zdravkovic
  • 2. Agenda•Introduction•Research Question and Research Goal•Research Methodology•The Proposed Artifacts•Evaluation•Conclusion
  • 3. Introduction • The BPM lifecycle consists of four phases, process design, process implementation, process enactment and performance evaluation [1, 2] • “The traces stored in logs are widely acknowledged as significant for analyzing performance of processes to identify opportunities for improvement” [3, 4] Problem Suggestion & Evaluation ConclusionAwareness Development
  • 4. Introduction • However, execution logs cannot be used [4, 5, 6], because - Logs capture traces for short time - During process execution, logs are continuously updated - Data from other sources cannot be added to process logs due to their design limitations • Solution: Data warehousing and data mining [4, 5, 7] Problem Suggestion & Evaluation ConclusionAwareness Development
  • 5. Why Data Warehousing? • According to DM review – a premier magazine on BI • The market of business intelligence tools and techniques raised to 13.4 billion in 2003 • According to the 451 Research* • Among these, specifically data warehousing market has seen fastest growth* • Annual growth rate from 2009 is 11.5%, and it is projected to be 13.2 billion dollar in revenue by 2013* Problem Suggestion & *also a consortium of companies Evaluation ConclusionAwareness Development
  • 6. Process Warehouse vs. Data Warehouse •Process Warehouse (PW) is a specialized data warehouse used for performance analysis and improvement of processes “PW provides comprehensive information on processes quickly, at various aggregation levels and from multidimensional points of view” [6] •PW differs from data warehouse because it designed to store process traces Problem Suggestion & Evaluation ConclusionAwareness Development
  • 7. Problem Space • PW is a large, and the magnitude of data needed for process performance analysis and decision making is small compared to PW size • Selection of appropriate dimensions may require significant domain expertise • Higher cognitive effort to extract and interpret the information from PW will not bring any value to the decision maker Problem Suggestion & Evaluation ConclusionAwareness Development
  • 8. Research Question and Goal •How to facilitate performance analysis and improvement of business processes using process warehouse? Goal - To develop a method for performance analysis of processes and deciding on process improvements using process warehouse. Problem Suggestion & Evaluation ConclusionAwareness Development
  • 9. Research Approach • IS research is classified into two research paradigms [8, 9] • Behavioral Science – justifying theories to explain human and organizational behavior • Design Science – problem solving paradigm to create (technology oriented) artifacts [9] • We use Design Science Problem Suggestion & Evaluation ConclusionAwareness Development
  • 10. Research Approach • Design Science – problem solving paradigm to create technology-oriented artifacts [9] Phases of design science [10] Problem Suggestion & Evaluation ConclusionAwareness Development
  • 11. Suggestion and Development Problem Suggestion & Evaluation ConclusionAwareness Development
  • 12. Suggestion and Development • Our approach is based on integration of goals with PW • To allow goal-based navigation of PW Quality of service state of a process intended to be • We propose Recall, PW is large, achieved. Like, efficient, timely, safe* navigation require • A Process Warehouse expertise, higher cognitive effort • A method for using PW for process analysis and improvement Problem Suggestion & *Swedish Institute of Medicine Evaluation ConclusionAwareness Development
  • 13. The Proposed Process Warehouse ✔ • Our PW differs from a PW in a number of ways that spans across two levels, - Structural level describes the design specification of data, relationship between data and constraints in a data - Architectural level is the set of specifications that describes the organization of warehouse objects, how they work together and how the data flows between them Problem Suggestion & Evaluation ConclusionAwareness Development
  • 14. Process Warehouse: Structural level •At structural level our PW differs from a PW, because it consists of two parts, stable and case specific - The stable part, to captures information about goals, indicators, satisfaction conditions and their relation with PW • This part is hard coded - The case specific part, captures the dimensions and facts essential for performance analysis of processes • This part is changeable (dynamic) Problem Suggestion & Evaluation ConclusionAwareness Development
  • 15. Process Warehouse: Architectural level •For populating the case-specific part of PW, data needs to be extracted and consolidated from process logs as well as from the transactional sources, which is not the case with traditional PW Process Warehouse Problem Suggestion & Evaluation ConclusionAwareness Development
  • 16. The Proposed Method ✔ • Build Goal structure Step 1: • Integrate Goals with Process Step 2 Warehouse • Performance Analysis and Step 3 Improvement Problem Suggestion & Evaluation ConclusionAwareness Development
  • 17. The Method – Step 1 ✔ • Build Goal structure Step 1 • Recursively analyze Task 1 Process Decomposition Tree Business Process • Identify goals of the Modular decomposition Task 2 Process & decompose of the control structure of a process • Identify criteria for Task 3 fulfillment of goals Goal Decomposition Tree Problem Suggestion & Evaluation ConclusionAwareness Development
  • 18. The Method – Step 1 ✔ • Build Goal structure Step 1 • Recursively analyze Task 1 Business Process • Identify goals of the Goal Decomposition Tree Task 2 Process & decompose Hierarchical structure of • Identify criteria for Task 3 goals aligned with fulfillment of goals modular decomposition of a process Output: Goal Decomposition Tree Problem Suggestion & Evaluation ConclusionAwareness Development
  • 19. The Method – Step 2 ✔ • Integrating Goals with ProcessStep 2 Warehouse • Concepts needed to relate Conceptual level goals with PW • Extensions to PW design Implementation level specification to integrate goals Output: Goal –PW Integration Problem Suggestion & Evaluation ConclusionAwareness Development
  • 20. The Method – Step 2 ✔ • Integrating Goals with ProcessStep 2 Warehouse • Concepts needed to relate Conceptual level goals with PW Problem Suggestion & Evaluation ConclusionAwareness Development
  • 21. The Method – Step 2 ✔ • Integrating Goals with ProcessStep 2 Warehouse • Extensions to PW design Implementation level specification to integrate goals Process Warehouse Bitmap attribute Bitmap attribute Stable part of PW Case-specific part of PW Problem Suggestion & Evaluation ConclusionAwareness Development
  • 22. The Method – Step 3 ✔ • Analyze and Improve Process Step 3 Task 1 • Condition Identification Task 2 • Goal Identification Task 3 • Information Analysis Task 4 • Decision Elicitation Task 5 • Process Change Solution Problem Suggestion & Evaluation ConclusionAwareness Development
  • 23. The Method – Step 3 ✔ • Analyze and Improve Process Step 3 Task 1 • Condition Identification Task 2 • Goal Identification Navigation Operations Task 3 • Information Analysis Traverse down, traverse Task 4 • Decision Elicitation up, traverse across, iterative traverse across Task 5 • Process Change Solution Problem Suggestion & Evaluation ConclusionAwareness Development
  • 24. The Method – Step 3 ✔ • Analyze and Improve Process Step 3 Task 1 • Condition Identification Task 2 • Goal Identification Task 3 • Information Analysis Task 4 • Decision Elicitation Suitability Estimation Model Task 5 • Process Change Solution Type level – suitability function µ Instance level – convenience σ Problem Suggestion & Evaluation ConclusionAwareness Development
  • 25. Evaluation Problem Suggestion & Evaluation ConclusionAwareness Development
  • 26. Evaluation •March [9] suggested two sequential steps for evaluation for design science - Criteria development - Assessment of artifact against the criteria •We use Moody’s Method evaluation model [11] , because - It is widely used for evaluation of IS artifacts - It incorporates performance and perception based evaluation • For perception based evaluation we adopt the evaluation model of Hong’s model [12] because – It is based on Technology acceptance model and IS success model – Also consider factors affecting DW success Problem Suggestion & Evaluation ConclusionAwareness Development
  • 27. Evaluation •In addition to that, mandatory elements of the method [12] Problem Suggestion & Evaluation ConclusionAwareness Development
  • 28. Prototype ResearchIntroduction Contribution Conclusion Question
  • 29. Performance based Evaluation • Accessible facts remains fixed with traditional approach, but changes with our goal based approach •The cognitive efforts to interpret information is reduced Accessible facts Problem Suggestion & Evaluation ConclusionAwareness Development
  • 30. Performance based Evaluation • Accessible dimensions remains fixed with traditional approach, but changes with our goal based approach •The domain expertise required to select appropriate dimension Accessible dimensions Problem Suggestion & Evaluation ConclusionAwareness Development
  • 31. Performance based Evaluation • Increase in precision affirms the retrieval of relevant data Comparison of precision Problem Suggestion & Evaluation ConclusionAwareness Development
  • 32. Perception based Evaluation • The method overall received a positive response • This indicates that the method was found to be usefulimproved task outcomeImprove analysis performanceEasy to learnHelp making better decisionsEasy to get required info Help finishing task quicklyEase to become expert user Useful for analysis Help improving analysis taskEasy to locate dataEasy to use data access toolsCompletenessSufficient data access toolsGranularitySufficiency Frequency distribution of constructs Problem Suggestion & PEOU - Perceived easy of use Evaluation ConclusionAwareness Development PU – Perceived usefulness
  • 33. Perception based Evaluation • Experienced users agreed in larger percentage than novice • Indicates construct items are better perceived by experience users than novice users Frequency distribution of constructs Problem Suggestion & Sufficient training Evaluation ConclusionAwareness Development
  • 34. Conclusion Problem Suggestion & Evaluation ConclusionAwareness Development
  • 35. Conclusions • The method provides a step by step approach that can facilitate process analysis and improvement • Results indicate that use of the proposed method has been perceived positively • Due to traceability between goals and PW content, relevant content is retrieved •Due to goal based navigation the task of navigating through PW is simplified Problem Suggestion & Evaluation ConclusionAwareness Development
  • 36. Acknowldgements Problem Suggestion & Evaluation ConclusionAwareness Development
  • 37. References[1] M. Weske, W.M.P. van der Aalst, H.M.W. Verbeek. Advances in business process management. Data and Knowledge Engineering, 50(1), pp. 1-8, 2004.[2] M. zur Muhlen. Workflow-based process controlling: Foundations, Design, and Application of Workflow-driven Process Information Systems. 1st edition, Logos Verlag Berlin, 2004.[3] W. van der Aalst, Mariska Netjes and Hajo A. Reijers. "Supporting the Full BPM Life-Cycle Using Process Mining and Intelligent Redesign."Contemporary Issues in Database Design and Information Systems Development. IGI Global, 2007. 100-132. Web. 13 Dec. 2011. doi:10.4018/978-1-59904-289-3.ch004.[4] D Grigori, F Casati, M Castellanos, U Dayal, M Sayal, M C Shan. Business Process Intelligence. Computer in Industry 53(4), pp. 321-343, 2004.[5] M Castellanos, A Simitsis, K Wilkinson, U Dayal. Automating the loading of business process data warehouses. Proceedings of the 12th International Conference on Extending database technology: Advances in Database Technology (EDBT09), Russia.
  • 38. References[6] B. List, J. Schiefer, A.M. Tjoa, G. Quirchmayr. Multidimensional business process analysis with the process warehouse. Knowledge discovery for business information systems, Vol 600, pp. 211-227, Kluwer Publications, 2002.[7] T. Bucher, A Gericke. Process-centric business intelligence. Business Process Management Journal, 15(3), pp. 408-429, 2009.[8] A.R. Hevner, S.T. March, J. Park. Design Science in Information Systems Research, MIS Quarterly, 28 (1), pp. 75-105, 2004.[9] S.T. March, G.F. Smith. Design and natural science research on information technology. Decision Support Systems, 15 (4), 251-266, 1995.[10] H. Takeda, P. Veerkamp, T. Tomiyama, H. Yoshikawam. "Modeling Design Processes." AI MagazineWinter: 37-48, (1990).[11] Daniel L. Moody: The method evaluation model: a theoretical model for validating information systems design methods. In proceedings of the European Conference on Information Systems (ECIS2003), pp. 1327-1336, Italy.