| ABSTRACT | 第1-9页 |
| 摘要 | 第9-11页 |
| ACKNOWLEDGEMENTS | 第11-15页 |
| TABLE OF CONTENTS | 第15-23页 |
| LIST OF FIGURES | 第23-27页 |
| LIST OF TABLES | 第27-28页 |
| LIST OF ACRONYMS | 第28-31页 |
| PART I: RESEARCH FOUNDATION, ART OF TECHNOLOGIES AND REQUIRMENT ANALYSIS | 第31-118页 |
| CHAPTER 1: INTRODUCTION | 第32-61页 |
| ·MOTIVATION | 第33-35页 |
| ·RESEARCH GOALS AND OBJECTIVES | 第35-36页 |
| ·THE RESEARCH ISSUES | 第36-39页 |
| ·RESEARCH METHODOLOGY | 第39-44页 |
| ·SCOPE OF THE DISSERTATION | 第44-45页 |
| ·TERMINOLOGY AND NOTIONS | 第45-47页 |
| ·Fundamental terms | 第45-47页 |
| ·Notions | 第47页 |
| ·CONTRIBUTIONS OF DISSERTATION | 第47-49页 |
| ·ORGANIZATION OF DISSERTATION | 第49-52页 |
| ·THE PHILOSOPHY AND BASIC CONCEPTS OF INTEGRATION OF THE INTELLIGENT BUSINESS PROCESS | 第52-57页 |
| ·Data mining | 第53-56页 |
| ·Data mining blessing | 第54页 |
| ·Data mining disambiguation | 第54-55页 |
| ·Data mining is a key to integrated technology | 第55-56页 |
| ·Agents and multi‐agent systems | 第56-57页 |
| ·Business intelligence | 第57页 |
| ·COMPLEXITY OF INTEGRATION BUSINESS INTELLIGENCE MODEL | 第57-60页 |
| ·SUMMARY | 第60-61页 |
| CHAPTER 2: RELATED WORK AND ART OF TECHNOLOGIES | 第61-91页 |
| ·BUSINESS INTELLIGENCE EXPECTATIONS AND CHALLENGES | 第62-63页 |
| ·INTEGRATED APPROACH TOWARDS BUSINESS PROCESSING | 第63-72页 |
| ·Tools integration framework | 第64-70页 |
| ·Data capture/acquisition | 第65页 |
| ·Data storage | 第65-67页 |
| ·Data access and analysis | 第67页 |
| ·Data warehouse, database and OLAP | 第67-68页 |
| ·Mining techniques and algorithms | 第68-70页 |
| ·Agents as a mining tool | 第70-71页 |
| ·Agent requirements | 第71-72页 |
| ·ARCHITECTURE FOR MODERN BUSINESS ONTOLOGICAL INTEGRATING | 第72-76页 |
| ·Integrating of data mining ontology | 第73-74页 |
| ·Integrating of agent (multi agent system)s ontology | 第74-76页 |
| ·Integrating of business intelligence ontology | 第76页 |
| ·DATA INTEGRATION AND DATA WAREHOUSING | 第76-77页 |
| ·DATA MINING ALGORITHMS AND BUSINESS INTELLIGENCE MODELING | 第77-81页 |
| ·Mining algorithms for business intelligence modeling | 第79-80页 |
| ·The demand of integrating for data mining with business intelligence | 第80页 |
| ·Mining structure for business intelligent modeling | 第80-81页 |
| ·AN INTEGRATING FOR DATA MINING AND BUSINESS INTELLIGENCE | 第81-84页 |
| ·The need of Integrating for data mining and multi agent systems | 第81-82页 |
| ·Integrated application | 第82-83页 |
| ·Integration visualization | 第83-84页 |
| ·AGENTS AND MULTI-AGENT SYSTEMS | 第84-86页 |
| ·Agents | 第84页 |
| ·Multi agent systems | 第84-85页 |
| ·Foundation for intelligent physical agents | 第85-86页 |
| ·Multi agent system development platform | 第86页 |
| ·MULTI AGENT DATA MINING | 第86-90页 |
| ·Central learning strategy | 第87页 |
| ·Meta-learning strategy | 第87-88页 |
| ·Hybrid-learning strategy | 第88-89页 |
| ·Generic multi-agent data mining | 第89-90页 |
| ·COMPLEXITIES IN INTEGRATING A PROCESS | 第90页 |
| ·SUMMARY | 第90-91页 |
| CHAPTER 3: INTEGRATION CONCEPTS, FUNDAMENTALS, APPROACH AND REQUIREMENT ANALYSIS | 第91-118页 |
| ·THE FUNDAMENTAL OF INTEGRATION CONCEPTS AND METHODOLOGY | 第93-98页 |
| ·Integration for mining tools fundamental and concepts | 第93-96页 |
| ·Data mining driven agent integration | 第94页 |
| ·Agent driven data exploration and tool's integration | 第94-95页 |
| ·Business intelligent driven business packages and agent integration | 第95-96页 |
| ·Generic model concepts and fundamental integration | 第96页 |
| ·Concepts of mining tools and agent's integration | 第96-98页 |
| ·INTEGRATED MODELING REQUIREMENT ANALYSIS AND DESIGN | 第98-100页 |
| ·Structural Analysis and design | 第99页 |
| ·Models operational analysis and integration | 第99-100页 |
| ·Conceptual measuring and refining of integrated modeling | 第100页 |
| ·INTEGRATING FOR MINING ALGORITHMS | 第100-104页 |
| ·Choosing the right mining algorithm | 第101-102页 |
| ·Concepts and methods of integrated data architecture | 第102-104页 |
| ·FUNDAMENTAL TO INTEGRATING FOR TOOLS AND AGENTS | 第104-105页 |
| ·Application tools driven agent integrations | 第104页 |
| ·Agent driven application tool's integrations | 第104-105页 |
| ·INTEGRATION PROTOCOL | 第105-106页 |
| ·Data mining agent protocol | 第105-106页 |
| ·Agent registration protocol | 第106页 |
| ·THE ARCHITECTURE OF INTEGRATION | 第106-112页 |
| ·Integrated modeling algorithms | 第107-108页 |
| ·Modeling behavior protocol as of integrated business intelligence | 第108-109页 |
| ·Equations and model applications | 第109-112页 |
| ·Model types | 第110-112页 |
| ·Model complexity | 第112页 |
| ·DATA MINING PREDICTIVE AND DESCRIPTIVE MODELING METHODS | 第112-117页 |
| ·Predictive modeling | 第113-116页 |
| ·Information integration tasks | 第114页 |
| ·Information integration model | 第114-115页 |
| ·Building predictive model | 第115-116页 |
| ·Descriptive modeling | 第116-117页 |
| ·SUMMARY | 第117-118页 |
| PART II: MODELING DEVELOPMENT AND ITS ANALYTICS | 第118-231页 |
| CHAPTER 4: BUSINESS INTELLIGENCE DATA MINING AND DATA WAREHOUSE | 第119-144页 |
| ·INTEGRATION PROCESS IN THE DATA WAREHOUSE | 第123-130页 |
| ·Data Warehouse Architecture | 第125-128页 |
| ·Data warehouse and decision support systems | 第128-129页 |
| ·Data warehouse data preparation | 第129-130页 |
| ·BUSINESS INTELLIGENCE DATA MINING OF DATA WAREHOUSING MODELS | 第130-136页 |
| ·Data warehouse modeling techniques | 第130-131页 |
| ·Online transaction processing | 第131-132页 |
| ·Online analytical processing | 第132-134页 |
| ·Online transaction processing Vs online analytical processing | 第134-136页 |
| ·THE PARADIGM OF DATA WAREHOUSE INTEROPERABILITY AND APPLICATION | 第136-143页 |
| ·Features of and applications | 第137页 |
| ·Star Schema | 第137-138页 |
| ·Snowflake schema | 第138-139页 |
| ·Dimension reduction | 第139-142页 |
| ·Dimension table and fact table | 第141页 |
| ·Correlation analysis | 第141-142页 |
| ·Principal component analysis | 第142页 |
| ·Data granularity | 第142-143页 |
| ·SUMMARY | 第143-144页 |
| CHAPTER 5: ARCHITECTURE OF INTEGRATING EMERGING TECHNOLOGY | 第144-172页 |
| ·ARCHITECTURE CONCEPT AND METHODS FOR INTEGRATION | 第145-152页 |
| ·The concepts of enable technology | 第146-148页 |
| ·Elements of service oriented architecture | 第147-148页 |
| ·Gird service architecture | 第148页 |
| ·Integration based application service architecture | 第148-149页 |
| ·Role based integrated model platform architecture | 第149-152页 |
| ·Service location | 第150页 |
| ·Service instantiation | 第150-151页 |
| ·Task based instantiation | 第151-152页 |
| ·FUNDAMENTAL OF INTEGRATED EMERGING TECHNOLOGY | 第152-153页 |
| ·THE BASIC PRIMITIVES OF EMERGING FOR BUSINESS INTELLIGENCE DATA MINING | 第153-155页 |
| ·Distributed integration architecture | 第153-155页 |
| ·Concepts and mechanisms of integration | 第155页 |
| ·Data centric integration mechanism | 第155页 |
| ·DATA MINING APPLICATION AND TRENDS FOR EMERGING TECHNOLOGY | 第155-157页 |
| ·PRINCIPLES OF DIMENSIONAL DATA MODELING | 第157-158页 |
| ·PREDICTION BUSINESS MODELS | 第158-171页 |
| ·Predictive/Supervised Business Modeling | 第158-165页 |
| ·Supervised structure prediction | 第159-160页 |
| ·Concept and fundamental to decision trees based predictive modeling | 第160页 |
| ·Algorithmic framework decision tree based predictive modeling | 第160-162页 |
| ·Fundamental of tree pruning for predictive modeling | 第162-164页 |
| ·Concepts of minimum description length based pruning | 第164-165页 |
| ·Predictive business model methodology and application | 第165-167页 |
| ·Logistic regression model | 第165-166页 |
| ·Naive Bayes approach text classification | 第166-167页 |
| ·Unsupervised predictive business model | 第167-170页 |
| ·Reduction for unsupervised to supervised | 第168页 |
| ·Unsupervised algorithms | 第168-169页 |
| ·Rule based unsupervised prediction modeling | 第169-170页 |
| ·Clustering | 第170-171页 |
| ·SUMMARY | 第171-172页 |
| CHAPTER 6: DEVELOPMENT OF GENERIC BUSINESS INTELLIGENCE MODEL | 第172-205页 |
| ·PARADIGM OF GENERIC MODEL | 第173-176页 |
| ·Model integration platform and information portal | 第173-175页 |
| ·Information access and distributions | 第175-176页 |
| ·Data exploratory and analysis | 第176页 |
| ·REQUIREMENT ANALYSIS AND DEVELOPMENT OF BUSINESS INTELLIGENCE GENERIC MODEL | 第176-184页 |
| ·A framework for integration of data mining and agents with business intelligence | 第177-181页 |
| ·The proposed generic architecture of a data mining framework | 第178-180页 |
| ·Generic data integration model | 第180-181页 |
| ·Model complexities in integrating process | 第181-182页 |
| ·Conceptual of theorizing and modeling | 第182-184页 |
| ·DATA INTEGRATIONS AND MINING ALGORITHMS | 第184-187页 |
| ·System development | 第185-186页 |
| ·Data mart design | 第186-187页 |
| ·MINING TECHNIQUES, ALGORITHMS AND MODELING FOR GENERIC BUSINESS INTELLIGENCE | 第187-202页 |
| ·The techniques of decision tree | 第188-189页 |
| ·Association Rules | 第189-199页 |
| ·Frequent pattern mining | 第193-195页 |
| ·A priori algorithms | 第195-199页 |
| ·CLUSTERING ANALYSIS | 第199-202页 |
| ·Clustering algorithms | 第200-201页 |
| ·Similarity measures | 第201-202页 |
| ·MODEL DEVELOPMENT (BUSINESS INTELLIGENCE) LIFE CYCLE | 第202-204页 |
| ·SUMMARY | 第204-205页 |
| CHAPTER 7: ANALYTIC OF INTEGRATION FOR TOOLS AND AGENTS TOWARDS GENERIC MODEL | 第205-231页 |
| ·DATA INTEGRATION AND PREPARATION | 第205-212页 |
| ·Data type and design | 第206-209页 |
| ·Data processing and flow | 第209-211页 |
| ·A process of knowledge discovery | 第211-212页 |
| ·THE PARADIGM OF INFORMATION INTEGRATION | 第212-216页 |
| ·Issues of information integration | 第214页 |
| ·Information integration extending the data warehousing | 第214-216页 |
| ·Information integration for performance and scalability of business paradigm | 第216页 |
| ·PREDICTIVE ANALYTICS AND MINING PROCESS: STRATEGIC IMPLEMENTATION | 第216-222页 |
| ·Big Picture: CRISP-DM based integrated BIDM | 第218-221页 |
| ·Integrated BI modeling based knowledgy discovery | 第221-222页 |
| ·TECHNIQUES FOR EXTRACTION OF DATA | 第222-227页 |
| ·Extract, Transform and Load (ETL) | 第222-224页 |
| ·Business Intelligence Data Storage and management | 第224-225页 |
| ·Business process intelligence (BPI) | 第225-227页 |
| ·DATA TRANSFORMATION DESIGN | 第227页 |
| ·DATA STAGING AND QUALITY | 第227-229页 |
| ·DATA VISUALIZATION | 第229-230页 |
| ·SUMMARY | 第230-231页 |
| PART III: MODEL PERFORMANCE EVALUATION AND APPLICATION | 第231-338页 |
| CHAPTER 8: DATA EXPLORATION AND EVALUATION PERFORMANCE FOR GENERIC MODELING OPTIMIZATION199 | 第232-257页 |
| ·DATA EXPLORATION TOWARDS GENERIC MODEL PERFORMANCE OPTIMIZATION | 第233-236页 |
| ·Distributed databases systems | 第234-235页 |
| ·Searching and exploration | 第235-236页 |
| ·Search in generic model performance | 第235-236页 |
| ·Exploration in design | 第236页 |
| ·COMPONENT BASED GENERIC MODEL EXPLORATION | 第236-243页 |
| ·Generic model performance (degree of freedom) | 第237-241页 |
| ·Integrated (meta models) performance measure | 第237-239页 |
| ·Performance optimization and formulation | 第239-240页 |
| ·Model personalization and customization | 第240-241页 |
| ·Generic model framework and performance optimization | 第241-242页 |
| ·Generic model architectural view as performance measurement | 第242-243页 |
| ·GENERIC MODEL APPLICATION AND OPTIMIZATION | 第243页 |
| ·MODEL PERFORMANCE EVALUATION AND EXPLORATION | 第243-246页 |
| ·A Modeling performance and exploration framwork | 第244-246页 |
| ·Model performance evaluation and optimization | 第246页 |
| ·GENERIC MODEL PERFORMANCE TESTING AND VALIDATION | 第246-249页 |
| ·View for architectural performance validation | 第247-248页 |
| ·Input-output modeling based test for generic model | 第248页 |
| ·Accuracy modeling validation process | 第248-249页 |
| ·DATA MINING AND AGENT BASED MODELING EXPLORATION | 第249-251页 |
| ·Data mining based agent models exploration | 第249-250页 |
| ·Agent based modeling exploration | 第250-251页 |
| ·FUZZY LOGIC AND GENETIC ALGORITHM BASED EXPLORATION | 第251-256页 |
| ·Fuzzy logic and data mining | 第253页 |
| ·Genetic algorithms | 第253-255页 |
| ·Fuzzy-genetic algorithms integrating | 第255-256页 |
| ·SUMMARY | 第256-257页 |
| CHAPTER 9: GENERIC MODEL PERFORMANCE AND EVALUATIONS: DATA MINING AND BUSINESS INTELLIGENCE APPLICATIONS | 第257-278页 |
| ·INFORMATION REQUIREMENTS FOR BUSINESS SUCCESS | 第257-270页 |
| ·Quality of integrating for data mining and business intelligence | 第258-262页 |
| ·Data mining on what kind of data? | 第259-260页 |
| ·Data mining tool does data scoring | 第260页 |
| ·Business intelligence tool does data scoring | 第260页 |
| ·Paradigm of data mining with business intelligent system architecture | 第260-262页 |
| ·Information value and an application | 第262-266页 |
| ·Market basket analysis | 第263-264页 |
| ·Business fraud detection | 第264-266页 |
| ·Information access and distribution system | 第266-268页 |
| ·The benefit of integrating modeling of data mining in business intelligence | 第268-269页 |
| ·A generic model for information quality assurance: Integrating approach | 第269-270页 |
| ·DATA MINING: CONFLUENCE OF MULTIPLE TASKS AND APPLICATIONS | 第270页 |
| ·KNOWLEDGE DISCOVERY IN INTEGRATED DATA | 第270-273页 |
| ·Transforming data into information and knowledge | 第271页 |
| ·Integrated modeling towards knowledge discovery | 第271-272页 |
| ·Integrated knowledge discovery in databases | 第272-273页 |
| ·INTEGRATED BUSINESS INTELLIGENCE APPLICATIONS AND PERFORMANCE | 第273-274页 |
| ·HIGH PERFORMANCE AND PRIVACY PRESERVING DATA MINING | 第274-277页 |
| ·Privacy of individual data | 第275-276页 |
| ·Distrubuted data mining based privacy performance optimization | 第276-277页 |
| ·SUMMARY | 第277-278页 |
| CHAPTER 10: THE PARADIGM OF MULTI AGENT SYSTEMS IN BUILDING INTEGRATED BUSINESS INTELLIGENCE | 第278-305页 |
| ·FROM SINGLE APPLICATION INTO INTEGRATION | 第279-283页 |
| ·Agents modeling requirements and applications | 第280-282页 |
| ·Agents based modeling development | 第281页 |
| ·Agents applied in business modeling applications | 第281-282页 |
| ·Single agent Vs multi agent applications | 第282-283页 |
| ·AGENT/MULTIAGENT BASED DECISION SUPPORT SYSTEM | 第283-287页 |
| ·Integrated Multi agent framework | 第285-286页 |
| ·Decision support system framework | 第286-287页 |
| ·ONTOLOGY BASED INTEGRATION OF AGENTS AND DATA MINING | 第287-292页 |
| ·Data mining based decision support system | 第289-290页 |
| ·Data mining and agent based generic decision support system | 第290-291页 |
| ·Paradigm decision support system techniques | 第291-292页 |
| ·AGENT COMMUNICATIONS LANGUAGES | 第292-295页 |
| ·Knowledge interchange format | 第293页 |
| ·Knowledge query and manipulation format | 第293-294页 |
| ·Foundation for intelligent physical agent (FIPA) communication language | 第294-295页 |
| ·ONTOLOGY FOR INTEGRATION AGENT COMMUNICATION | 第295-297页 |
| ·MULTI-AGENT SYSTEMS AND ITS APPLICATION IN BUSINESS INTELLIGENCE | 第297-303页 |
| ·Software agents | 第298-301页 |
| ·Integrated intelligent information agents | 第299-300页 |
| ·Workflow management and virtual organizations as agents | 第300-301页 |
| ·The essence of Software agent for business intelligence | 第301页 |
| ·Applications for Agents with Physical or Virtual Bodies | 第301-303页 |
| ·Autonomous Control Systems | 第302页 |
| ·Traffic telemetric | 第302-303页 |
| ·AGENT BASED BUSINESS INTELLIGENCE MODEL PERFORMANCE FRAMEWORK | 第303-304页 |
| ·SUMMARY | 第304-305页 |
| CHAPTER 11: BUSINESS PROCESSING MANAGEMENT PERFORMANCE: SUCCESS OF BUSINESS INTELLIGENCE ... | 第305-332页 |
| ·VISUALIZATION OF BUSINESS INTELLIGENT PERFORMANCE QUALITY | 第306-309页 |
| ·Integrated business intelligence for business processing management performance | 第307-308页 |
| ·Qualitative performance of business intelligence in business processing performance | 第308-309页 |
| ·THE PARADIGM OF BUSINESS PROCESSING MANAGEMENT PERFORMANCE | 第309-316页 |
| ·A paradigm of business process management workflow | 第309-312页 |
| ·Adaptive workflow management | 第310-311页 |
| ·Workflow process modeling | 第311-312页 |
| ·Workflow engine and its interface | 第312页 |
| ·Decision making performance | 第312-315页 |
| ·Business knowledge creations | 第313-315页 |
| ·Decision support system and knowledge management systems in diction making process | 第315页 |
| ·Performance measurement systems as an entity | 第315-316页 |
| ·ACHIEVING BUSINESS INTELLIGENCE IMPACT | 第316-320页 |
| ·Integrating business intelligence with core business processes | 第316-317页 |
| ·Choice of Techniques | 第317-319页 |
| ·Measuring goal oriented business intelligence based business processing | 第319-320页 |
| ·CRITICAL ISSUES IN BUSINESS INTELLIGENCE BASED BUSINESS PROCESSING | 第320-324页 |
| ·Critical success factors | 第320-321页 |
| ·Business processing value of business intelligence | 第321-323页 |
| ·The reason of business intelligence for business processing | 第323-324页 |
| ·BUSINESS PROCESS DESIGN, DEPLOYMENT AND ONGOING MAINTENANCE | 第324-326页 |
| ·Business intelligence based business process design | 第324-325页 |
| ·Business processing system deployment and implementation | 第325-326页 |
| ·Business process maintenance | 第326页 |
| ·CHANGE MANAGEMENT | 第326-331页 |
| ·A business process change framework | 第327-328页 |
| ·Performance measurement towards change management | 第328-329页 |
| ·Business process model's re-engineering as change management | 第329-331页 |
| ·SUMMARY | 第331-332页 |
| CHAPTER 12: CONCLUSION AND FUTURE RESEARCH DIRECTION | 第332-338页 |
| ·CONCLUSION | 第332-335页 |
| ·FUTURE RESEARCH DIRECTION | 第335-338页 |
| REFERENCES | 第338-351页 |
