Prakash Data Warehouse Requirements Engineering

A Decision Based Approach
1. Auflage 2018
ISBN: 978-981-10-7019-8
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 182 Seiten

As the first to focus on the issue of Data Warehouse Requirements Engineering, this book introduces a model-driven requirements process used to identify requirements granules and incrementally develop data warehouse fragments. In addition, it presents an approach to the pair-wise integration of requirements granules for consolidating multiple data warehouse fragments. The process is systematic and does away with the fuzziness associated with existing techniques. Thus, consolidation is treated as a requirements engineering issue.The notion of a decision occupies a central position in the decision-based approach. On one hand, information relevant to a decision must be elicited from stakeholders; modeled; and transformed into multi-dimensional form. On the other, decisions themselves are to be obtained from decision applications. For the former, the authors introduce a suite of information elicitation techniques specific to data warehousing. This information is subsequently converted into multi-dimensional form. For the latter, not only are decisions obtained from decision applications for managing operational businesses, but also from applications for formulating business policies and for defining rules for enforcing policies, respectively. In this context, the book presents a broad range of models, tools and techniques. For readers from academia, the book identifies the scientific/technological problems it addresses and provides cogent arguments for the proposed solutions; for readers from industry, it presents an approach for ensuring that the product meets its requirements while ensuring low lead times in delivery.    

Naveen Prakash obtained his doctoral degree from the Indian Institute of Technology Delhi (IIT Delhi) in 1980. He subsequently worked at the Bhabha Atomic Research Centre, Mumbai and at the National Center for Software Development and Computing Techniques, Tata Institute of Fundamental Research (NCSDCT, TIFR) before joining the R&D group of CMC Ltd where he worked for over 10 years doing industrial R&D. In 1989, he moved to academics. He worked at the Department of Computer Science and Engineering, Indian Institute of Technology Kanpur (IIT Kanpur), and at the Delhi Institute of Technology (DIT) (now Netaji Subhas Institute of Technology (NSIT)), Delhi. During this period he provided consultancy services to Asian Development Bank and African Development Bank projects in Sri Lanka and Tanzania, respectively, as well as to the Indira Gandhi National Centre for the Arts (IGNCA) as a United Nations Development Programme (UNDP) consultant. He served as a scientific advisor to the British Council Division, New Delhi and took up the directorship of various educational institutes in India. Post-retirement, he worked on a World Bank project in Malawi. Professor Prakash has lectured extensively in various universities abroad. He is on the editorial board of the Requirements Engineering Journal, and of the International Journal of Information System Modeling and Design (IJISMD). He has published over 70 research papers and authored two books.Prof. Prakash continues to be an active researcher. Besides Business Intelligence and Data Warehousing, his interests include the Internet-of-things and NoSQL database. He also lectures at the Indira Gandhi Delhi Technical University for Women (IGDTUW), Delhi.Deepika Prakash obtained her Ph.D. from Delhi Technological University, Delhi in the area of Data Warehouse Requirements Engineering. Currently, she is an Assistant Professor at the Department of Big Data Analytics, Central University of Rajasthan, Rajasthan. Dr. Prakash has five years of teaching experience, as well as two years of experience in industrial R&D, building data marts for purchase, sales and inventory and in data mart integration. Her responsibilities spanned the complete life cycle, from requirements engineering through conceptual modeling to extract-transform-load (ETL) activities. As a researcher, she has authored a number of papers in international forums and has delivered invited lectures at a number of Institutes throughout India. Her current research interests include Business Intelligence, Health Analytics, and the Internet-of-Things.

Autoren/Hrsg.

Prakash, Naveen

Prakash, Deepika

Inhaltsverzeichnis

1;Preface;6
2;Contents;9
3;About the Authors;12
4;1 Requirements Engineering for Transactional Systems;14
4.1;1.1 Transactional System Development Life Cycle;15
4.2;1.2 Transactional Requirements Engineering;18
4.3;1.3 Requirements Engineering (RE) as a Process;19
4.4;1.4 Informal Approaches to Requirements Elicitation;21
4.5;1.5 Model-Driven Techniques;24
4.5.1;1.5.1 Goal Orientation;24
4.5.2;1.5.2 Agent-Oriented Requirements Engineering;26
4.5.3;1.5.3 Scenario Orientation;27
4.5.4;1.5.4 Goal–Scenario Coupling;28
4.6;1.6 Conclusion;28
4.7;References;29
5;2 Requirements Engineering for Data Warehousing;31
5.1;2.1 Data Warehouse Background;31
5.2;2.2 Data Warehouse Development Experience;34
5.3;2.3 Data Warehouse Systems Development Life Cycle, DWSDLC;36
5.4;2.4 Methods for Data Warehouse Development;40
5.4.1;2.4.1 Monolithic Versus Bus Architecture;40
5.4.2;2.4.2 Data Warehouse Agile Methods;42
5.5;2.5 Data Mart Consolidation;46
5.6;2.6 Strategic Alignment;50
5.7;2.7 Data Warehouse Requirements Engineering;52
5.7.1;2.7.1 Goal-Oriented DWRE Techniques;55
5.7.2;2.7.2 Goal-Motivated Techniques;58
5.7.3;2.7.3 Miscellaneous Approaches;59
5.7.4;2.7.4 Obtaining Information;59
5.8;2.8 Conclusion;60
5.9;References;61
6;3 Issues in Data Warehouse Requirements Engineering;63
6.1;3.1 The Central Notion of a Decision;63
6.1.1;3.1.1 The Decision Process;64
6.1.2;3.1.2 Decision-Oriented Data Warehousing;66
6.2;3.2 Obtaining Information Requirements;72
6.2.1;3.2.1 Critical Success Factors;72
6.2.2;3.2.2 Ends Achievement;73
6.2.3;3.2.3 Means Efficiency;74
6.2.4;3.2.4 Feedback Analysis;74
6.2.5;3.2.5 Summary;74
6.3;3.3 Requirements Consolidation;75
6.4;3.4 Conclusion;80
6.5;References;81
7;4 Discovering Decisions;82
7.1;4.1 Deciding Enterprise Policies;83
7.1.1;4.1.1 Representing Policies;85
7.1.2;4.1.2 Policies to Choice Sets;86
7.2;4.2 Deciding Policy Enforcement Rules;90
7.2.1;4.2.1 Representing Enforcement Rules;91
7.2.2;4.2.2 Developing Choice Sets;93
7.3;4.3 Defining Operational Decisions;100
7.3.1;4.3.1 Structure of an Action;100
7.4;4.4 Computer-Aided Support for Obtaining Decisions;103
7.4.1;4.4.1 Architecture;103
7.4.2;4.4.2 User Interface;105
7.5;4.5 Conclusion;109
7.6;References;110
8;5 Information Elicitation;111
8.1;5.1 Obtaining Multidimensional Structure;111
8.2;5.2 Decisional Information Elicitation;113
8.3;5.3 The Decision Requirement Model;116
8.3.1;5.3.1 The Notion of a Decision;116
8.3.2;5.3.2 Metamodel of Decisions;117
8.3.3;5.3.3 Information;119
8.4;5.4 Eliciting Information;121
8.4.1;5.4.1 CSFI Elicitation;121
8.4.2;5.4.2 ENDSI Elicitation;122
8.4.3;5.4.3 MEANSI Elicitation;123
8.4.4;5.4.4 Feedback Information Elicitation;124
8.5;5.5 The Global Elicitation Process;124
8.6;5.6 Eliciting Information for Policy Decision-Making;126
8.6.1;5.6.1 CSFI Elicitation;126
8.6.2;5.6.2 Ends Information Elicitation;128
8.7;5.7 Eliciting Information for PER Formulation;128
8.8;5.8 Information Elicitation for Operational Systems;130
8.8.1;5.8.1 Elicitation for Selecting PER;130
8.8.2;5.8.2 Information Elicitation for Actions;131
8.9;5.9 The Late Information Substage;135
8.9.1;5.9.1 ER Schema for Policy Formulation;135
8.9.2;5.9.2 ER Schema for PER Formulation and Operations;136
8.9.3;5.9.3 Guidelines for Constructing ER Schema;136
8.10;5.10 Computer-Based Support for Information Elicitation;137
8.10.1;5.10.1 User Interfaces;137
8.10.2;5.10.2 The Early Information Base;141
8.11;5.11 Conclusion;142
8.12;References;143
9;6 The Development Process;144
9.1;6.1 Agile Data Warehouse Development;144
9.2;6.2 Decision Application Model (DAM) for Agility;146
9.3;6.3 A Hierarchical View;148
9.4;6.4 Granularity of Requirements;150
9.4.1;6.4.1 Selecting the Right Granularity;153
9.5;6.5 Showing Agility Using an Example;157
9.6;6.6 Comparison of DAM and Epic–Theme–Story Approach;159
9.7;6.7 Data Warehouse Consolidation;160
9.8;6.8 Approaches to Consolidation;164
9.9;6.9 Consolidating Requirements Granules;165
9.9.1;6.9.1 An Example Showing Consolidation;169
9.10;6.10 Tool Support;174
9.11;6.11 Conclusion;176
9.12;References;177
10;7 Conclusion;178