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E-raamat: Cognition-Driven Decision Support for Business Intelligence: Models, Techniques, Systems and Applications

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Cognition-driven decision support system (DSS) has been recognized as a paradigm in the research and development of business intelligence (BI). Cognitive decision support aims to help managers in their decision making from human cognitive aspects, such as thinking, sensing, understanding and predicting, and fully reuse their experience. Among these cognitive aspects, decision makers' situation awareness (SA) and mental models are considered to be two important prerequisites for decision making, particularly in ill-structured and dynamic decision situations with uncertainties, time pressure and high personal stake. In today's business domain, decision making is becoming increasingly complex. To make a successful decision, managers' SA about their business environments becomes a critical factor.This book presents theoretical models as well practical techniques of cognitiondriven DSS. It first introduces some important concepts of cognition orientation in decision making process and some techniques in related research areas including DSS, data warehouse and BI, offering readers a preliminary for moving forward in this book. It then proposes a cognition-driven decision process (CDDP) model which incorporates SA and experience (mental models) as its central components. The goal of the CDDP model is to facilitate cognitive decision support to managers on the basis of BI systems. It also presents relevant techniques developed to support the implementation of the CDDP model in a BI environment. Key issues addressed of a typical business decision cycle in the CDDP model include: natural language interface for a manager's SA input, extraction of SA semantics, construction of data warehouse queries based on the manger's SA and experience, situation information retrieval from data warehouse, how the manager perceives situation information and update SA, how the manager's SA leads to a final decision. Finally, a cognition-driven DSS, FACETS, and two illustrative applications of this system are discussed.

The cognitive decision support system (DSS) provides excellent insight into the research and development of business intelligence. This volume presents current DDS technologies and the fundamentals that support them, in a systematic way.
Part I: Concepts
1 Decision Making and Decision Support Systems
3
1.1 Decision Making and Decision Makers
3
1.2 Decision Problem Classification
4
1.3 Decision-Making Process
5
1.4 Decision Support Systems
8
1.4.1 The Concept
9
1.4.2 Characteristics
9
1.4.3 Types
10
1.5 Decision Support Techniques
11
1.5.1 Optimization
12
1.5.2 Multiple Criteria Decision Making
12
1.5.3 Data Mining
13
1.5.4 Case-Based Reasoning
15
1.5.5 Decision Tree
16
1.6 What's New in This Book?
17
1.6.1 The Decision Problems Oriented in This Book
17
1.6.2 New Models and Techniques for Ill-Structured Decision Problems
18
2 Business Intelligence
19
2.1 What Is Business Intelligence?
19
2.2 The Architecture of a Business Intelligence System
20
2.3 Analytics of Business Intelligence
22
2.4 Commercial Tools
24
2.4.1 SAS Business Intelligence
24
2.4.2 IBM Cognos Business Intelligence
26
2.4.3 SAP BusinessObjects Business Intelligence
27
2.5 Limitations
28
2.6 Summary
29
3 Managerial Cognition
31
3.1 The Concept of Cognition
31
3.2 Situation Awareness
32
3.3 Mental Models
33
3.4 Naturalistic Decision Making
34
3.5 Summary
37
4 Cognition in Business Decision Support Systems
39
4.1 Complex Nature of Business Decision Making
39
4.2 Cognition in Business Decision Making
41
4.3 Cognition Oriented Information Systems
42
4.3.1 Cognitive Decision Support Systems
42
4.3.2 Case-Based Reasoning Systems
44
4.3.3 Natural Language Interfaces to Database
44
4.3.3.1 Pattern-Matching NLIDB Systems
45
4.3.3.2 Syntax-Based NLIDB Systems
45
4.3.3.3 Semantic Grammar NLIDB Systems
48
4.4 Summary
50
Part II: Models
5 Cognition-Driven Decision Processes
53
5.1 Essentials of Cognition-Driven Decision Making
53
5.1.1 The Conceptual Framework of Cognitive Decision Support
53
5.1.2 Cognition-Driven Decision Processes
55
5.1.3 User Centered Decision Processes
56
5.2 The Cognition-Driven Decision Process Model
57
5.2.1 Situation Retrieval
59
5.2.1.1 Information Retrieval and Situation Retrieval
59
5.2.1.2 Information Need and Knowledge Need
62
5.2.1.3 Situation Retrieval Process
63
5.2.2 Generating Navigation Knowledge
68
5.2.3 Situation Presentation
69
5.2.4 Situation Awareness Updating
69
5.2.5 Decision Generation
70
5.2.6 The Decision Cycle
71
5.3 Summary
73
Part III: Techniques
6 Domain Knowledge Representation and Processing
77
6.1 Ontology
77
6.1.1 Basics of Ontology
77
6.1.2 Property-Share Relationships
78
6.1.3 Class Tree
80
6.1.4 Class Graph
83
6.1.5 Role of the Ontology
84
6.1.6 Synonyms
84
6.1.7 Class Similarity
85
6.2 Experience
86
6.2.1 Experience Representation
87
6.2.2 Experience Elicitation
88
6.2.3 Creating an Experience Base
89
6.2.4 Cues
91
6.2.5 Extracting Cues
92
6.2.6 Knowledge Retrieval
94
6.2.7 Generating Navigation Knowledge
95
6.3 Summary
96
7 Natural Language Processing for Situation Awareness
97
7.1 Link Grammar Parser
97
7.2 Information Types
99
7.3 The Process of Situation Awareness Parsing
100
7.4 SA Plain Parsing: Instance Recognition
101
7.4.1 Numeric Meta Instances
102
7.4.2 Literal Meta Instances
103
7.4.3 Reference Properties
105
7.5 SA Semantic Parsing: Class Inferring
105
7.5.1 Class Trigger Construction
106
7.5.2 Triggering Rules
108
7.5.3 Reducing Uncertainties of SA Triples
112
7.6 Local Context Determination
114
7.6.1 Context Position Points
114
7.6.2 Context Coverage Points
116
7.6.3 Inverse Context Specificity Points
116
7.6.4 Local Contexts
117
7.7 Summary
118
8 Data Warehouse Query Construction and Situation Presentation
119
8.1 Query Languages for Data Warehouses
119
8.1.1 Structured Query Language
119
8.1.2 Multidimensional Expressions
121
8.2 Framework of Query Construction and Situation Presentation
124
8.3 Determining Query Data Sources
126
8.4 Constructing SQL Queries
127
8.5 Constructing MDX Queries
131
8.6 Navigation-Knowledge-Guided Situation Presentation
136
8.7 Data Analysis and Situation Presentation
138
8.8 Summary
139
Part IV: Systems and Applications
9 A Cognition-Driven Decision Support System: FACETS
143
9.1 The Development Environment
143
9.2 The Architecture of FACETS
143
9.3 Subsystems of FACETS
145
9.3.1 Data Warehouse System
145
9.3.2 Ontology Management
145
9.3.3 Experience Management
146
9.3.4 Situation Awareness Management
148
9.3.5 Situation Awareness Parsing
149
9.3.6 Situation Awareness Annotating
150
9.3.7 Query Builder
150
9.3.8 Situation Presentation
151
9.4 The Cognition-Driven Decision Process Based on FACETS
155
9.5 Summary
156
10 Evaluation of Algorithms and FACETS
157
10.1 Experiment Preparation
157
10.1.1 Data Warehouse
157
10.1.2 Ontology
158
10.1.3 Experience Base
158
10.1.4 Subjects
159
10.2 Experiment One: Algorithm Evaluation
159
10.2.1 Experiment Design
159
10.2.2 Meta Instance Recognition
162
10.2.3 Class Inferring
164
10.2.4 Local Context Determination
167
10.2.5 SA Triple Generation
169
10.2.6 Optimization Analysis
170
10.3 Experiment Two: System Evaluation
171
10.3.1 Experiment Design
171
10.3.2 Query Construction Evaluation
172
10.3.3 FACETS Evaluation
173
10.4 Summary
177
11 Application Cases of FACETS
179
11.1 Application Case I: Business
179
11.1.1 Organization Background
179
11.1.2 The Ontology
182
11.1.3 The Experience Base
182
11.1.4 Decision Situation
183
11.1.5 Decision Process
183
11.1.6 Final Decision
210
11.2 Application Case II: Public Health
210
11.3 Summary
214
References 215
Abbreviations 233
Index 235