学位论文 > 优秀研究生学位论文题录展示
面向商业智能的数据挖掘算法和多智能体系统的体系结构以及优化
作 者: Gebeyehu Belay Gebremeskel
导 师: 何中市
学 校: 重庆大学
专 业: 计算机科学与技术
关键词: 数据挖掘算法 商务智能 多代理系统 模型 建模 算法
分类号: TP311.13
类 型: 博士论文
年 份: 2013年
下 载: 316次
引 用: 0次
阅 读: 论文下载
内容摘要
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目前，对于组织机构，数据挖掘大势所趋、广为应用。因为不管何时何地，一切皆为数据。为了有效应对，他们聘请数据处理专家，探索动态的开发工具和方法。热衷于隐私保护的人则关心数据的处理、管理以及再应用。科学家、数据员，技术专家以及企业家期望找到新的方法来搜索、提取，并预测可用的信息和知识。数据挖掘，商业智能和多智能体系统作为一种单一的搜索技术已经非常成熟。在当代商务领域，科技及工程理论、数据建构、存储设施都已基本完备。然而用工具和专属技术来挖掘有价值的信息，准确获取和传递数据，仍面临挑战。因此，发展一种信息整合的、智能的方法处理、管理和有效地利用数据，涌现出大量的需求，也是迎接数据时代挑战的根本之路。在信息技术发展，商业的动态性，用户的需求以及导致商业成功的背景中，对于优化工具、智能体的应用及其性能，至关重要。然而,单一的工具或是代理技术不能满足目前的商业需求，我们需要寻求一种可视化的、在大数据中挖掘有效信息的途径，在此前提下，多方法融合的智能的数据处理、管理和使用方法应运而生。本文研究了一种一般化的方法，对于发展物理、生物以及社会系统的一般模型有贡献意义。本研究主要侧重于研究大数据是如何有效利用的，来更好地发挥出信息的力量。在本文中，我们引入了基于商业智能的数据挖掘、多智能体系统的集成模式，提出了一种简单的一般推测模型和挖掘算法。该方法可以可扩展性地发展多个商业智能工具，可以让用户方便地根据所需传播信息的动态架构。为了搜索大范围的数据，我们采用了一种系统新颖的方法，优化了现有的工具和应用智能体的技术。为现代商业系统的优化提供了一种可行方法。这些尝试是对传统挖掘技术的革新，可以生成一种通用模型，结合了物理、生物、社会系统的特点，进行商业智能系统的配置。此外，多种挖掘算法和技术也经过了作者深入的比较、探讨和改进。此外，本文还尝试了两种不同分支间的重要交融。一是商业和科学的交融，把传统的挖掘方法和商务智能相结合，主要体现在第3、5、6和7章。技术细节和算法可以通过商务智能工具DW，OLAP, OLTP和在其他动态建模基础上量化和实现，并允许用户按需接入和传播数据。这是一种对于高级工具使用的代理技术在大数据上应用的新尝试，可以用于优化现有的商务智能系统。第二点融合是应用工具与代理技术的交融，并以可视化的形式呈献给用户，这要求决策者、用户、研究人员在传统商务流程中加入软实力来重建商务模式，第8、9和10章做了具体探讨。全文第4章是商务预期探讨，而第11章是对现有方法的融合及尝试。因此，没有工具的应用和代理技术，就谈不上商务决策的优化和效率。数据挖掘和多智能体系统的集成是处理大数据的可视化工具。这些可视化信息对于商业或者整个组织机构有易读性，为普适计算，数据数字化提供了一条有效路径。目前，对于商业组织，数字化已经不再是一个选择，而成为必备的技术手段，因为集成和智能方法的问题求解，性能优化和风险削减，也是现代商务的基本任务和方法，即“从信息时代到知识时代的转变”，是研究集成的应用和代理技术的科学方法。集成的应用和代理技术能够动态的自适应的解决各种问题。归根结底，数据挖掘是以解决实际问题为导向的。本论文的重要成果总结如下：-处理商业挑战的单一方法在诸多原因的限制下并不都是有效的。在本文中，我们讨论并提出了一种新颖的智能工具和智能体集成技术。该技术对于特定的商业需求，能给出最好的自适应方案，供决策者做出最优的判断。-可视化图示对于情景的概念化具有挑战。纵观文献，人们尝试了各种模型来应对这些棘手的问题。本文提出并发展了一种通用商业智能模型，并集成了数据挖掘算法。这些挖掘算法对于挖掘复杂而海量的数据是非常有必要的，并且它可以通过描画许多相关知识来辅助商业智能系统以及信息挖掘过程，以扩展用户的能力。-该研究所用的通用模块提供了一个共用框架，对概念和词表进行精确的识别、描绘，在分析各种商业活动中，缩短了决策者和专家之间的距离，可以对商务问题多角度地进行描述。本论文工作将分为三大部分，包括：（i）第1，2，3章讨论了研究的理论与技术，并做出需求分析。（ii）第4，5，6，7章引入模型与算法，进行新的系统构想。（iii）第8，9，10，11章提供了建模和性能评价，并展开了具体应用，第12章是结论与未来的研究方向。本论文的第6章到第11章由12篇论文作为支撑，其中包括6篇已发表文章（EI：2，国际会议：2，数据挖掘方面的书籍章节：2）。其他的6篇文章（其中1篇已录用）在审稿修订中。除此之外，本研究工作由9个相关主题课堂作业支撑，其中5篇论文已被国际会议收录。
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全文目录
ABSTRACT 6-9
摘要 9-11
ACKNOWLEDGEMENTS 11-15
TABLE OF CONTENTS 15-23
LIST OF FIGURES 23-27
LIST OF TABLES 27-28
LIST OF ACRONYMS 28-31
PART I: RESEARCH FOUNDATION, ART OF TECHNOLOGIES AND REQUIRMENT ANALYSIS 31-118
CHAPTER 1: INTRODUCTION 32-61
1.1 MOTIVATION 33-35
1.2 RESEARCH GOALS AND OBJECTIVES 35-36
1.3 THE RESEARCH ISSUES 36-39
1.4 RESEARCH METHODOLOGY 39-44
1.5 SCOPE OF THE DISSERTATION 44-45
1.6 TERMINOLOGY AND NOTIONS 45-47
1.6.1 Fundamental terms 45-47
1.6.2 Notions 47
1.7 CONTRIBUTIONS OF DISSERTATION 47-49
1.8 ORGANIZATION OF DISSERTATION 49-52
1.9 THE PHILOSOPHY AND BASIC CONCEPTS OF INTEGRATION OF THE INTELLIGENT BUSINESS PROCESS 52-57
1.9.1 Data mining 53-56
1.9.1.1 Data mining blessing 54
1.9.1.2 Data mining disambiguation 54-55
1.9.1.3 Data mining is a key to integrated technology 55-56
1.9.2 Agents and multi‐agent systems 56-57
1.9.3 Business intelligence 57
1.10 COMPLEXITY OF INTEGRATION BUSINESS INTELLIGENCE MODEL 57-60
1.11 SUMMARY 60-61
CHAPTER 2: RELATED WORK AND ART OF TECHNOLOGIES 61-91
2.1 BUSINESS INTELLIGENCE EXPECTATIONS AND CHALLENGES 62-63
2.2 INTEGRATED APPROACH TOWARDS BUSINESS PROCESSING 63-72
2.2.1 Tools integration framework 64-70
2.2.1.1 Data capture/acquisition 65
2.2.1.2 Data storage 65-67
2.2.1.3 Data access and analysis 67
2.2.1.4 Data warehouse, database and OLAP 67-68
2.2.1.5 Mining techniques and algorithms 68-70
2.2.2 Agents as a mining tool 70-71
2.2.3 Agent requirements 71-72
2.3 ARCHITECTURE FOR MODERN BUSINESS ONTOLOGICAL INTEGRATING 72-76
2.3.1 Integrating of data mining ontology 73-74
2.3.2 Integrating of agent (multi agent system)s ontology 74-76
2.3.3 Integrating of business intelligence ontology 76
2.4 DATA INTEGRATION AND DATA WAREHOUSING 76-77
2.5 DATA MINING ALGORITHMS AND BUSINESS INTELLIGENCE MODELING 77-81
2.5.1 Mining algorithms for business intelligence modeling 79-80
2.5.2 The demand of integrating for data mining with business intelligence 80
2.5.3 Mining structure for business intelligent modeling 80-81
2.6 AN INTEGRATING FOR DATA MINING AND BUSINESS INTELLIGENCE 81-84
2.6.1 The need of Integrating for data mining and multi agent systems 81-82
2.6.2 Integrated application 82-83
2.6.3 Integration visualization 83-84
2.7 AGENTS AND MULTI-AGENT SYSTEMS 84-86
2.7.1 Agents 84
2.7.2 Multi agent systems 84-85
2.7.3 Foundation for intelligent physical agents 85-86
2.7.4 Multi agent system development platform 86
2.8 MULTI AGENT DATA MINING 86-90
2.8.1 Central learning strategy 87
2.8.2 Meta-learning strategy 87-88
2.8.3 Hybrid-learning strategy 88-89
2.8.4 Generic multi-agent data mining 89-90
2.9 COMPLEXITIES IN INTEGRATING A PROCESS 90
2.10 SUMMARY 90-91
CHAPTER 3: INTEGRATION CONCEPTS, FUNDAMENTALS, APPROACH AND REQUIREMENT ANALYSIS 91-118
3.1 THE FUNDAMENTAL OF INTEGRATION CONCEPTS AND METHODOLOGY 93-98
3.1.1 Integration for mining tools fundamental and concepts 93-96
3.1.1.1 Data mining driven agent integration 94
3.1.1.2 Agent driven data exploration and tool's integration 94-95
3.1.1.3 Business intelligent driven business packages and agent integration 95-96
3.1.1.4 Generic model concepts and fundamental integration 96
3.1.2 Concepts of mining tools and agent's integration 96-98
3.2 INTEGRATED MODELING REQUIREMENT ANALYSIS AND DESIGN 98-100
3.2.1 Structural Analysis and design 99
3.2.2 Models operational analysis and integration 99-100
3.3.3 Conceptual measuring and refining of integrated modeling 100
3.3 INTEGRATING FOR MINING ALGORITHMS 100-104
3.3.1 Choosing the right mining algorithm 101-102
3.3.2 Concepts and methods of integrated data architecture 102-104
3.4 FUNDAMENTAL TO INTEGRATING FOR TOOLS AND AGENTS 104-105
3.4.1 Application tools driven agent integrations 104
3.4.2 Agent driven application tool's integrations 104-105
3.5 INTEGRATION PROTOCOL 105-106
3.5.1 Data mining agent protocol 105-106
3.5.2 Agent registration protocol 106
3.6 THE ARCHITECTURE OF INTEGRATION 106-112
3.6.1 Integrated modeling algorithms 107-108
3.6.2 Modeling behavior protocol as of integrated business intelligence 108-109
3.6.3 Equations and model applications 109-112
3.6.3.1 Model types 110-112
3.6.3.2 Model complexity 112
3.7 DATA MINING PREDICTIVE AND DESCRIPTIVE MODELING METHODS 112-117
3.7.1 Predictive modeling 113-116
3.8.1.1 Information integration tasks 114
3.7.1.2 Information integration model 114-115
3.7.1.3 Building predictive model 115-116
3.7.2 Descriptive modeling 116-117
3.8 SUMMARY 117-118
PART II: MODELING DEVELOPMENT AND ITS ANALYTICS 118-231
CHAPTER 4: BUSINESS INTELLIGENCE DATA MINING AND DATA WAREHOUSE 119-144
4.1 INTEGRATION PROCESS IN THE DATA WAREHOUSE 123-130
4.1.1 Data Warehouse Architecture 125-128
4.1.2 Data warehouse and decision support systems 128-129
4.1.3 Data warehouse data preparation 129-130
4.2 BUSINESS INTELLIGENCE DATA MINING OF DATA WAREHOUSING MODELS 130-136
4.2.1 Data warehouse modeling techniques 130-131
4.2.2 Online transaction processing 131-132
4.2.3 Online analytical processing 132-134
4.2.4 Online transaction processing Vs online analytical processing 134-136
4.3 THE PARADIGM OF DATA WAREHOUSE INTEROPERABILITY AND APPLICATION 136-143
4.3.1 Features of and applications 137
4.3.2 Star Schema 137-138
4.3.3 Snowflake schema 138-139
4.3.4 Dimension reduction 139-142
4.3.5.1 Dimension table and fact table 141
4.3.5.2 Correlation analysis 141-142
4.3.5.5 Principal component analysis 142
4.3.6 Data granularity 142-143
4.4 SUMMARY 143-144
CHAPTER 5: ARCHITECTURE OF INTEGRATING EMERGING TECHNOLOGY 144-172
5.1 ARCHITECTURE CONCEPT AND METHODS FOR INTEGRATION 145-152
5.1.1 The concepts of enable technology 146-148
5.1.1.1 Elements of service oriented architecture 147-148
5.1.1.2 Gird service architecture 148
5.1.2 Integration based application service architecture 148-149
5.1.3 Role based integrated model platform architecture 149-152
5.1.3.1 Service location 150
5.1.3.2 Service instantiation 150-151
5.1.3.3 Task based instantiation 151-152
5.2 FUNDAMENTAL OF INTEGRATED EMERGING TECHNOLOGY 152-153
5.3 THE BASIC PRIMITIVES OF EMERGING FOR BUSINESS INTELLIGENCE DATA MINING 153-155
5.3.1 Distributed integration architecture 153-155
5.3.2 Concepts and mechanisms of integration 155
5.3.3 Data centric integration mechanism 155
5.4 DATA MINING APPLICATION AND TRENDS FOR EMERGING TECHNOLOGY 155-157
5.5 PRINCIPLES OF DIMENSIONAL DATA MODELING 157-158
5.6 PREDICTION BUSINESS MODELS 158-171
5.6.1 Predictive/Supervised Business Modeling 158-165
5.6.1.1 Supervised structure prediction 159-160
5.6.1.2 Concept and fundamental to decision trees based predictive modeling 160
5.6.1.3 Algorithmic framework decision tree based predictive modeling 160-162
5.6.1.4 Fundamental of tree pruning for predictive modeling 162-164
5.6.1.5 Concepts of minimum description length based pruning 164-165
5.6.2 Predictive business model methodology and application 165-167
5.6.2.1 Logistic regression model 165-166
5.6.2.2 Naive Bayes approach text classification 166-167
5.6.3 Unsupervised predictive business model 167-170
5.6.3.1 Reduction for unsupervised to supervised 168
5.6.3.2 Unsupervised algorithms 168-169
5.6.3.3 Rule based unsupervised prediction modeling 169-170
5.6.4 Clustering 170-171
5.8 SUMMARY 171-172
CHAPTER 6: DEVELOPMENT OF GENERIC BUSINESS INTELLIGENCE MODEL 172-205
6.1 PARADIGM OF GENERIC MODEL 173-176
6.1.1 Model integration platform and information portal 173-175
6.1.2 Information access and distributions 175-176
6.1.3 Data exploratory and analysis 176
6.2 REQUIREMENT ANALYSIS AND DEVELOPMENT OF BUSINESS INTELLIGENCE GENERIC MODEL 176-184
6.2.1 A framework for integration of data mining and agents with business intelligence 177-181
6.2.1.1 The proposed generic architecture of a data mining framework 178-180
6.2.1.2 Generic data integration model 180-181
6.2.2 Model complexities in integrating process 181-182
6.2.3 Conceptual of theorizing and modeling 182-184
6.3 DATA INTEGRATIONS AND MINING ALGORITHMS 184-187
6.3.1 System development 185-186
6.3.2 Data mart design 186-187
6.4 MINING TECHNIQUES, ALGORITHMS AND MODELING FOR GENERIC BUSINESS INTELLIGENCE 187-202
6.4.1 The techniques of decision tree 188-189
6.4.2 Association Rules 189-199
6.4.2.1 Frequent pattern mining 193-195
6.4.2.2 A priori algorithms 195-199
6.4.3 CLUSTERING ANALYSIS 199-202
6.4.3.1 Clustering algorithms 200-201
6.4.3.2 Similarity measures 201-202
6.5 MODEL DEVELOPMENT (BUSINESS INTELLIGENCE) LIFE CYCLE 202-204
6.6 SUMMARY 204-205
CHAPTER 7: ANALYTIC OF INTEGRATION FOR TOOLS AND AGENTS TOWARDS GENERIC MODEL 205-231
7.1 DATA INTEGRATION AND PREPARATION 205-212
7.1.1 Data type and design 206-209
7.1.2 Data processing and flow 209-211
7.1.3 A process of knowledge discovery 211-212
7.2 THE PARADIGM OF INFORMATION INTEGRATION 212-216
7.2.1 Issues of information integration 214
7.2.2 Information integration extending the data warehousing 214-216
7.2.3 Information integration for performance and scalability of business paradigm 216
7.3 PREDICTIVE ANALYTICS AND MINING PROCESS: STRATEGIC IMPLEMENTATION 216-222
7.3.1 Big Picture: CRISP-DM based integrated BIDM 218-221
7.3.2 Integrated BI modeling based knowledgy discovery 221-222
7.4 TECHNIQUES FOR EXTRACTION OF DATA 222-227
7.4.1 Extract, Transform and Load (ETL) 222-224
7.4.2 Business Intelligence Data Storage and management 224-225
7.4.3 Business process intelligence (BPI) 225-227
7.5 DATA TRANSFORMATION DESIGN 227
7.6 DATA STAGING AND QUALITY 227-229
7.7 DATA VISUALIZATION 229-230
7.8 SUMMARY 230-231
PART III: MODEL PERFORMANCE EVALUATION AND APPLICATION 231-338
CHAPTER 8: DATA EXPLORATION AND EVALUATION PERFORMANCE FOR GENERIC MODELING OPTIMIZATION199 232-257
8.1 DATA EXPLORATION TOWARDS GENERIC MODEL PERFORMANCE OPTIMIZATION 233-236
8.1.1 Distributed databases systems 234-235
8.1.2 Searching and exploration 235-236
8.1.2.1 Search in generic model performance 235-236
8.1.2.2 Exploration in design 236
8.2 COMPONENT BASED GENERIC MODEL EXPLORATION 236-243
8.2.1 Generic model performance (degree of freedom) 237-241
8.2.1.1 Integrated (meta models) performance measure 237-239
8.2.1.2 Performance optimization and formulation 239-240
8.2.1.3 Model personalization and customization 240-241
8.2.2 Generic model framework and performance optimization 241-242
8.2.3 Generic model architectural view as performance measurement 242-243
8.3 GENERIC MODEL APPLICATION AND OPTIMIZATION 243
8.4 MODEL PERFORMANCE EVALUATION AND EXPLORATION 243-246
8.4.1 A Modeling performance and exploration framwork 244-246
8.4.2 Model performance evaluation and optimization 246
8.5 GENERIC MODEL PERFORMANCE TESTING AND VALIDATION 246-249
8.5.1 View for architectural performance validation 247-248
8.5.2 Input-output modeling based test for generic model 248
8.5.3 Accuracy modeling validation process 248-249
8.6 DATA MINING AND AGENT BASED MODELING EXPLORATION 249-251
8.6.1 Data mining based agent models exploration 249-250
8.6.2 Agent based modeling exploration 250-251
8.7 FUZZY LOGIC AND GENETIC ALGORITHM BASED EXPLORATION 251-256
8.7.1 Fuzzy logic and data mining 253
8.7.2 Genetic algorithms 253-255
8.7.3 Fuzzy-genetic algorithms integrating 255-256
8.8 SUMMARY 256-257
CHAPTER 9: GENERIC MODEL PERFORMANCE AND EVALUATIONS: DATA MINING AND BUSINESS INTELLIGENCE APPLICATIONS 257-278
9.1 INFORMATION REQUIREMENTS FOR BUSINESS SUCCESS 257-270
9.1.1 Quality of integrating for data mining and business intelligence 258-262
9.1.1.1 Data mining on what kind of data? 259-260
9.1.1.2 Data mining tool does data scoring 260
9.1.1.3 Business intelligence tool does data scoring 260
9.1.1.4 Paradigm of data mining with business intelligent system architecture 260-262
9.1.2 Information value and an application 262-266
9.1.2.1 Market basket analysis 263-264
9.1.2.2 Business fraud detection 264-266
9.1.3 Information access and distribution system 266-268
9.1.4 The benefit of integrating modeling of data mining in business intelligence 268-269
9.1.5 A generic model for information quality assurance: Integrating approach 269-270
9.2 DATA MINING: CONFLUENCE OF MULTIPLE TASKS AND APPLICATIONS 270
9.3 KNOWLEDGE DISCOVERY IN INTEGRATED DATA 270-273
9.3.1 Transforming data into information and knowledge 271
9.3.2 Integrated modeling towards knowledge discovery 271-272
9.3.3 Integrated knowledge discovery in databases 272-273
9.4 INTEGRATED BUSINESS INTELLIGENCE APPLICATIONS AND PERFORMANCE 273-274
9.5 HIGH PERFORMANCE AND PRIVACY PRESERVING DATA MINING 274-277
9.5.1 Privacy of individual data 275-276
9.5.2 Distrubuted data mining based privacy performance optimization 276-277
9.6 SUMMARY 277-278
CHAPTER 10: THE PARADIGM OF MULTI AGENT SYSTEMS IN BUILDING INTEGRATED BUSINESS INTELLIGENCE 278-305
10.1 FROM SINGLE APPLICATION INTO INTEGRATION 279-283
10.1.1 Agents modeling requirements and applications 280-282
10.1.1.1 Agents based modeling development 281
10.1.1.2 Agents applied in business modeling applications 281-282
101.1.3 Single agent Vs multi agent applications 282-283
10.2 AGENT/MULTIAGENT BASED DECISION SUPPORT SYSTEM 283-287
10.2.1 Integrated Multi agent framework 285-286
10.2.2 Decision support system framework 286-287
10.3 ONTOLOGY BASED INTEGRATION OF AGENTS AND DATA MINING 287-292
10.3.1 Data mining based decision support system 289-290
10.3.2 Data mining and agent based generic decision support system 290-291
10.3.4 Paradigm decision support system techniques 291-292
10.4 AGENT COMMUNICATIONS LANGUAGES 292-295
10.4.1 Knowledge interchange format 293
10.4.2 Knowledge query and manipulation format 293-294
10.4.3 Foundation for intelligent physical agent (FIPA) communication language 294-295
10.5 ONTOLOGY FOR INTEGRATION AGENT COMMUNICATION 295-297
10.6 MULTI-AGENT SYSTEMS AND ITS APPLICATION IN BUSINESS INTELLIGENCE 297-303
10.6.1 Software agents 298-301
10.6.1.1 Integrated intelligent information agents 299-300
10.6.1.2 Workflow management and virtual organizations as agents 300-301
10.6.1.3 The essence of Software agent for business intelligence 301
10.6.2 Applications for Agents with Physical or Virtual Bodies 301-303
10.6.2.1 Autonomous Control Systems 302
10.6.2.2 Traffic telemetric 302-303
10.7 AGENT BASED BUSINESS INTELLIGENCE MODEL PERFORMANCE FRAMEWORK 303-304
10.8 SUMMARY 304-305
CHAPTER 11: BUSINESS PROCESSING MANAGEMENT PERFORMANCE: SUCCESS OF BUSINESS INTELLIGENCE ... 305-332
11.1 VISUALIZATION OF BUSINESS INTELLIGENT PERFORMANCE QUALITY 306-309
11.1.1 Integrated business intelligence for business processing management performance 307-308
11.1.2 Qualitative performance of business intelligence in business processing performance 308-309
11.2 THE PARADIGM OF BUSINESS PROCESSING MANAGEMENT PERFORMANCE 309-316
11.2.1 A paradigm of business process management workflow 309-312
11.2.1.1 Adaptive workflow management 310-311
11.2.1.2 Workflow process modeling 311-312
11.2.1.3 Workflow engine and its interface 312
11.2.2 Decision making performance 312-315
11.2.2.1 Business knowledge creations 313-315
11.2.2.2 Decision support system and knowledge management systems in diction making process 315
11.2.3 Performance measurement systems as an entity 315-316
11.3 ACHIEVING BUSINESS INTELLIGENCE IMPACT 316-320
11.3.1 Integrating business intelligence with core business processes 316-317
11.3.2 Choice of Techniques 317-319
11.3.3 Measuring goal oriented business intelligence based business processing 319-320
11.4 CRITICAL ISSUES IN BUSINESS INTELLIGENCE BASED BUSINESS PROCESSING 320-324
11.4.1 Critical success factors 320-321
11.4.2 Business processing value of business intelligence 321-323
11.4.3 The reason of business intelligence for business processing 323-324
11.5 BUSINESS PROCESS DESIGN, DEPLOYMENT AND ONGOING MAINTENANCE 324-326
11.5.1 Business intelligence based business process design 324-325
11.5.2 Business processing system deployment and implementation 325-326
11.5.3 Business process maintenance 326
11.6 CHANGE MANAGEMENT 326-331
11.6.1 A business process change framework 327-328
11.6.2 Performance measurement towards change management 328-329
11.6.3 Business process model's re-engineering as change management 329-331
11.7 SUMMARY 331-332
CHAPTER 12: CONCLUSION AND FUTURE RESEARCH DIRECTION 332-338
12.1 CONCLUSION 332-335
12.2 FUTURE RESEARCH DIRECTION 335-338
REFERENCES 338-351
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- 基于SVM的常压塔石脑油干点软测量建模研究,TE622.1
- 基于Thermo-Calc三元共晶合金凝固路径的耦合计算,TG111.4
- 电火花加工中的电极损耗机理及控制研究,TG661
- 油漆焦油基单颗粒危险废物热解和燃烧特性的实验研究,X705
- 粉末活性炭—超滤工艺处理微污染地表水试验研究,X703
- 压气机优化平台建立与跨音速压气机气动优化设计,TH45
- 陀螺稳定吊舱控制系统稳定回路设计与研究,V241.5
- 卫星姿态的磁控制方法研究,V448.222
- 涡轮S2流面正问题气动优化设计研究,V235.11
- 光纤陀螺温度漂移建模与补偿,V241.5
- 多导弹协同作战突防效能评估及组合优化算法研究,TJ760.1
- 电磁轨道炮外弹道建模与仿真研究,TJ399
- 轨道交通引起周围环境竖向振动的振源特性分析,U211.3
- RUV4汽车点火线圈工艺参数分析及影响因素研究,U463.64
- 基于感性负载的车身网络控制系统,U463.6
- 基于LIN总线的电动车窗控制方法研究,U463.6
中图分类: > 工业技术 > 自动化技术、计算机技术 > 计算技术、计算机技术 > 计算机软件 > 程序设计、软件工程 > 程序设计 > 数据库理论与系统
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