PhD Disputation

Improving Business Processes using
Process-oriented Data Warehouse

Muhammad Khurram Shahzad

Doctoral Dissertation in
Computer and Systems Sciences

Supervised by: Paul Johannesson
Jelena Zdravkovic

Agenda

•Introduction
•Research Question and Research Goal
•Research Methodology
•The Proposed Artifacts
•Evaluation
•Conclusion

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 Conclusion
Awareness Development

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]


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

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 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


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.


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


Research Approach
• Design Science – problem solving paradigm to create
technology-oriented artifacts [9]

Phases of design science [10]


Suggestion and Development


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

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


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)


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


The Proposed Method ✔

• Build Goal structure
Step 1:

• Integrate Goals with Process
Step 2 Warehouse

• Performance Analysis and
Step 3 Improvement


The Method – Step 1 ✔

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



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



• Integrating Goals with Process
Step 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



Step 2
Warehouse

• Concepts needed to relate
Conceptual level
goals with PW



Step 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


• 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



Step 3


Navigation Operations
Traverse down, traverse
Task 4 • Decision Elicitation up, traverse across,
iterative traverse across



Step 3




Task 4 • Decision Elicitation Suitability Estimation Model

Type level – suitability function µ
Instance level – convenience σ


Evaluation


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


Evaluation

•In addition to that, mandatory elements of the method [12]


Prototype

Research
Introduction Contribution Conclusion
Question

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



• Accessible dimensions remains fixed with traditional
approach, but changes with our goal based approach
•The domain expertise required to select appropriate
dimension

Accessible dimensions



• Increase in precision affirms the retrieval of relevant data

Comparison of precision


Perception based Evaluation

• The method overall received a positive response
• This indicates that the method was found to be useful

improved task outcome
Improve analysis performance
Easy to learn
Help making better decisions
Easy to get required info
Help finishing task quickly
Ease to become expert user
Useful for analysis
Help improving analysis task
Easy to locate data
Easy to use data access tools
Completeness
Sufficient data access tools
Granularity
Sufficiency

Frequency distribution of constructs

Problem Suggestion & PEOU - Perceived easy of use
Awareness Development PU – Perceived usefulness

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

Conclusion


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


Acknowldgements


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 (EDBT'09), Russia.

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 (ECIS'2003), pp. 1327-1336, Italy.

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