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E-raamat: Fundamentals of Business Intelligence

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This book presents a comprehensive and systematic introduction to transforming process-oriented data into information about the underlying business process, which is essential for all kinds of decision-making. To that end, the authors develop step-by-step models and analytical tools for obtaining high-quality data structured in such a way that complex analytical tools can be applied. The main emphasis is on process mining and data mining techniques and the combination of these methods for process-oriented data.

After a general introduction to the business intelligence (BI) process and its constituent tasks in chapter 1, chapter 2 discusses different approaches to modeling in BI applications. Chapter 3 is an overview and provides details of data provisioning, including a section on big data. Chapter 4 tackles data description, visualization, and reporting. Chapter 5 introduces data mining techniques for cross-sectional data. Different techniques for the analysis of temporal data are then detailed in Chapter 6. Subsequently, chapter 7 explains techniques for the analysis of process data, followed by the introduction of analysis techniques for multiple BI perspectives in chapter 8. The book closes with a summary and discussion in chapter 9. Throughout the book, (mostly open source) tools are recommended, described and applied; a more detailed survey on tools can be found in the appendix, and a detailed code for the solutions together with instructions on how to install the software used can be found on the accompanying website. Also, all concepts presented are illustrated and selected examples and exercises are provided.

The book is suitable for graduate students in computer science, and the dedicated website with examples and solutions makes the book ideal as a textbook for a first course in business intelligence in computer science or business information systems. Additionally, practitioners and industrial developers who are interested in the concepts behind business intelligence will benefit from the clear explanations and many examples.

Arvustused

The usage of examples and case studies enable real life application and brings asophisticated text to life. the book is a comprehensive and thoroughly well thought out introduction to the subject of business intelligence and the reader will not be left wanting as the clear examples are numerous. Readers interested in the value of data and the concepts behind business intelligence will find the book and its accompanying website highly informative. (Georgette Banham, bcs, The Chartered Institute for IT, bcs.org, August, 2016)

This book focuses primarily on the data mining, data warehousing, data analytics, data visualization, data presentation, and process analysis dimensions of BI in detail. One of the noteworthy strengths of this book is the inclusion of comprehensive lists with very recent and relevant references for BI at the end of each chapter. This should make the book very useful for academic research on the topic. (Satya Prakash Saraswat, Computing Reviews, February, 2016)

1 Introduction 1(34)
1.1 Definition of Business Intelligence
1(3)
1.2 Putting Business Intelligence into Context
4(10)
1.2.1 Business Intelligence Scenarios
4(2)
1.2.2 Perspectives in Business Intelligence
6(2)
1.2.3 Business Intelligence Views on Business Processes
8(3)
1.2.4 Goals of Business Intelligence
11(2)
1.2.5 Summary: Putting Business Intelligence in Context
13(1)
1.3 Business Intelligence: Tasks and Analysis Formats
14(10)
1.3.1 Data Task
14(1)
1.3.2 Business and Data Understanding Task
15(2)
1.3.3 Modeling Task
17(2)
1.3.4 Analysis Task
19(1)
1.3.5 Evaluation and Reporting Task
20(1)
1.3.6 Analysis Formats
20(4)
1.3.7 Summary: Tasks and Analysis Formats
24(1)
1.4 Use Cases
24(7)
1.4.1 Application in Patient Treatment
25(3)
1.4.2 Application in Higher Education
28(1)
1.4.3 Application in Logistics
29(1)
1.4.4 Application in Customer Relationship Management
30(1)
1.5 Structure and Outline of the Book
31(1)
1.6 Recommended Reading (Selection)
32(1)
References
32(3)
2 Modeling in Business Intelligence 35(52)
2.1 Models and Modeling in Business Intelligence
35(11)
2.1.1 The Representation Function of Models
36(3)
2.1.2 Model Presentation
39(2)
2.1.3 Model Building
41(3)
2.1.4 Model Assessment and Quality of Models
44(1)
2.1.5 Models and Patterns
45(1)
2.1.6 Summary: Models and Modeling in Business Intelligence
46(1)
2.2 Logical and Algebraic Structures
46(5)
2.2.1 Logical Structures
46(2)
2.2.2 Modeling Using Logical Structures
48(3)
2.2.3 Summary: Logical Structures
51(1)
2.3 Graph Structures
51(7)
2.3.1 Model Structure
51(3)
2.3.2 Modeling with Graph Structures
54(3)
2.3.3 Summary: Graph Structures
57(1)
2.4 Analytical Structures
58(16)
2.4.1 Calculus
58(3)
2.4.2 Probabilistic Structures
61(6)
2.4.3 Statistical Structures
67(3)
2.4.4 Modeling Methods Using Analytical Structures
70(3)
2.4.5 Summary: Analytical Structures
73(1)
2.5 Models and Data
74(8)
2.5.1 Data Generation
74(2)
2.5.2 The Role of Time
76(2)
2.5.3 Data Quality
78(4)
2.5.4 Summary: Models and Data
82(1)
2.6 Conclusion and Lessons Learned
82(1)
2.7 Recommended Reading (Selection)
83(1)
References
83(4)
3 Data Provisioning 87(32)
3.1 Introduction and Goals
87(1)
3.2 Data Collection and Description
88(2)
3.3 Data Extraction
90(8)
3.3.1 Extraction-Transformation-Load (ETL) Process
90(3)
3.3.2 Big Data
93(5)
3.3.3 Summary on Data Extraction
98(1)
3.4 From Transactional Data Towards Analytical Data
98(10)
3.4.1 Table Formats and Online Analytical Processing (OLAP)
100(4)
3.4.2 Log Formats
104(4)
3.4.3 Summary: From Transactional Towards Analytical Data
108(1)
3.5 Schema and Data Integration
108(7)
3.5.1 Schema Integration
108(4)
3.5.2 Data Integration and Data Quality
112(1)
3.5.3 Linked Data and Data Mashups
113(1)
3.5.4 Summary: Schema and Data Integration
114(1)
3.6 Conclusion and Lessons Learned
115(1)
3.7 Recommended Reading
115(1)
References
115(4)
4 Data Description and Visualization 119(36)
4.1 Introduction
119(1)
4.2 Description and Visualization of Business Processes
120(7)
4.2.1 Process Modeling and Layout
121(1)
4.2.2 The BPM Tools' Perspective
122(1)
4.2.3 Process Runtime Visualization
123(1)
4.2.4 Visualization of Further Aspects
123(3)
4.2.5 Challenges in Visualizing Process-Related Information
126(1)
4.2.6 Summary: Description and Visualization of Business Processes
127(1)
4.3 Description and Visualization of Data in the Customer Perspective
127(6)
4.3.1 Principles for Description and Visualization of Collections of Process Instances
127(4)
4.3.2 Interactive and Dynamic Visualization
131(2)
4.3.3 Summary: Visualization of Process Instances
133(1)
4.4 Basic Visualization Techniques
133(14)
4.4.1 Description and Visualization of Qualitative Information
134(3)
4.4.2 Description and Visualization of Quantitative Variables
137(3)
4.4.3 Description and Visualization of Relationships
140(3)
4.4.4 Description and Visualization of Temporal Data
143(2)
4.4.5 Interactive and Dynamic Visualization
145(1)
4.4.6 Summary: Basic Visualization Techniques
146(1)
4.5 Reporting
147(6)
4.5.1 Description and Visualization of Metadata
147(2)
4.5.2 High-Level Reporting
149(2)
4.5.3 Infographics
151(1)
4.5.4 Summary: Reporting
152(1)
4.6 Recommended Reading
153(1)
References
153(2)
5 Data Mining for Cross-Sectional Data 155(52)
5.1 Introduction to Supervised Learning
155(4)
5.2 Regression Models
159(14)
5.2.1 Model Formulation and Terminology
159(2)
5.2.2 Linear Regression
161(5)
5.2.3 Neural Networks
166(3)
5.2.4 Kernel Estimates
169(2)
5.2.5 Smoothing Splines
171(1)
5.2.6 Summary: Regression Models
172(1)
5.3 Classification Models
173(20)
5.3.1 Model Formulation and Terminology
173(4)
5.3.2 Classification Based on Probabilistic Structures
177(5)
5.3.3 Methods Using Trees
182(3)
5.3.4 K-Nearest-Neighbor Classification
185(1)
5.3.5 Support Vector Machines
186(4)
5.3.6 Combination Methods
190(1)
5.3.7 Application of Classification Methods
191(1)
5.3.8 Summary: Classification Models
192(1)
5.4 Unsupervised Learning
193(11)
5.4.1 Introduction and Terminology
193(2)
5.4.2 Hierarchical Clustering
195(4)
5.4.3 Partitioning Methods
199(2)
5.4.4 Model-Based Clustering
201(2)
5.4.5 Summary: Unsupervised Learning
203(1)
5.5 Conclusion and Lessons Learned
204(1)
5.6 Recommended Reading
204(1)
References
205(2)
6 Data Mining for Temporal Data 207(38)
6.1 Terminology and Approaches Towards Temporal Data Mining
207(5)
6.2 Classification and Clustering of Time Sequences
212(8)
6.2.1 Segmentation and Classification Using Time Warping
214(3)
6.2.2 Segmentation and Classification Using Response Features
217(3)
6.2.3 Summary: Classification and Clustering of Time Sequences
220(1)
6.3 Time-to-Event Analysis
220(4)
6.4 Analysis of Markov Chains
224(9)
6.4.1 Structural Analysis of Markov Chains
226(4)
6.4.2 Cluster Analysis for Markov Chains
230(1)
6.4.3 Generalization of the Basic Model
231(2)
6.4.4 Summary: Analysis of Markov Chains
233(1)
6.5 Association Analysis
233(4)
6.6 Sequence Mining
237(3)
6.7 Episode Mining
240(2)
6.8 Conclusion and Lessons Learned
242(1)
6.9 Recommended Reading
243(1)
References
244(1)
7 Process Analysis 245(30)
7.1 Introduction and Terminology
245(2)
7.2 Business Process Analysis and Simulation
247(5)
7.2.1 Static Analysis
248(1)
7.2.2 Dynamic Analysis and Simulation
248(3)
7.2.3 Optimization
251(1)
7.2.4 Summary: Process Analysis and Simulation
252(1)
7.3 Process Performance Management and Warehousing
252(3)
7.3.1 Performance Management
252(1)
7.3.2 Process Warehousing
253(2)
7.3.3 Summary: Process Performance Management and Warehousing
255(1)
7.4 Process Mining
255(13)
7.4.1 Process Discovery
256(7)
7.4.2 Change Mining
263(3)
7.4.3 Conformance Checking
266(1)
7.4.4 Summary: Process Mining
267(1)
7.5 Business Process Compliance
268(2)
7.5.1 Compliance Along the Process Life Cycle
268(2)
7.5.2 Summary: Compliance Checking
270(1)
7.6 Evaluation and Assessment
270(1)
7.6.1 Process Mining
270(1)
7.6.2 Compliance Checking
271(1)
7.7 Conclusion and Lessons Learned
271(1)
7.8 Recommended Reading
272(1)
References
272(3)
8 Analysis of Multiple Business Perspectives 275(44)
8.1 Introduction and Terminology
275(2)
8.2 Social Network Analysis and Organizational Mining
277(13)
8.2.1 Social Network Analysis
277(5)
8.2.2 Organizational Aspect in Business Processes
282(2)
8.2.3 Organizational Mining Techniques for Business Processes
284(6)
8.2.4 Summary: Social Network Analysis and Organizational Mining
290(1)
8.3 Decision Point Analysis
290(4)
8.4 Text Mining
294(19)
8.4.1 Introduction and Terminology
294(2)
8.4.2 Data Preparation and Modeling
296(5)
8.4.3 Descriptive Analysis for the Document Term Matrix
301(2)
8.4.4 Analysis Techniques for a Corpus
303(4)
8.4.5 Further Aspects of Text Mining
307(6)
8.4.6 Summary: Text Mining
313(1)
8.5 Conclusion and Lessons Learned
313(2)
8.6 Recommended Reading
315(1)
References
315(4)
9 Summary 319(10)
A Survey on Business Intelligence Tools 329(14)
A.1 Data Modeling and ETL Support
329(1)
A.2 Big Data
330(4)
A.3 Visualization, Visual Mining, and Reporting
334(3)
A.4 Data Mining
337(1)
A.5 Process Mining
338(1)
A.6 Text Mining
339(1)
References
340(3)
Index 343
Wilfried Grossmann is retired full professor for statistics at the Faculty of Informatics, University of Vienna. He has published in the areas of business informatics, medical informatics, data mining, operations research, applied statistics and statistical computing. His research focuses on the interface between applied statistics, statistical data management and metadata for statistical information systems. He was leader and key researcher of several European projects for the development of statistical information systems, statistical metadata management and data validation.

Stefanie Rinderle-Ma is full professor and head of the research group Workflow Systems and Technology at the Faculty of Computer Science, University of Vienna. Stefanies research interests focus on business process compliance and intelligence, human-centered approaches in business process management (BPM,) and process-aware information systems (PAIS). She is co-author of more than 120 conference and journal publications with over 4600 citations (according to scholar.google.com) in the area of PAIS, BPM, and service-oriented architectures.
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