Business and Scientific Workflows A Web Service-Oriented Approach

WEI TAN, PhD, is currently a Research Staff Member at IBM's Thomas J. Watson Research Center. He received a Best Paper Award from the IEEE International Conference on Services Computing (2011), a Pacesetter Award from Argonne National Laboratory (2010), and caBIG Teamwork Award from the National Cancer Institute (2008).

MENGCHU ZHOU, PhD, is a Professor of Electrical and Computer Engineering and Director of the Discrete Event Systems Laboratory at the New Jersey Institute of Technology (NJIT). He is also a Professor at The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China.

Preface xiii

1. Introduction 1

1.1 Background and Motivations, 1

1.1.1 Web Service and Service-Oriented Architecture, 1

1.1.2 Workflow Technology, 4

1.2Overview of Standards, 8

1.2.1 Web Service-Related Standards, 8

1.2.2 Workflow-Related Standards, 19

1.3 Workflow Design: State of the Art, 22

1.3.1 Automatic Service Composition, 22

1.3.2 Mediation-Aided Service Composition, 23

1.3.3 Verification of Service-Based Workflows, 24

1.3.4 Decentralized Execution of Workflows, 25

1.3.5 Scientific Workflow Systems, 26

1.4 Contributions, 27

2. Petri Net Formalism 29

2.1 Basic Petri Nets, 29

2.2 Workflow Nets, 32

2.3 Colored Petri Nets, 35

3. Data-Driven Service Composition 39

3.1 Problem Statement, 40

3.1.1 Domains and Data Relations, 41

3.1.2 Problem Formulation, 43

3.2 Data-Driven Composition Rules, 45

3.2.1 Sequential Composition Rule, 46

3.2.2 Parallel Composition Rule, 46

3.2.3 Choice Composition Rule, 47

3.3 Data-Driven Service Composition, 48

3.3.1 Basic Definitions, 48

3.3.2 Derive AWSP from Service Net, 50

3.4 Effectiveness and Efficiency of the Data-Driven Approach, 55

3.4.1 Solution Effectiveness, 55

3.4.2 Complexity Analysis, 56

3.5 Case Study, 57

3.6 Discussion, 60

3.7 Summary, 61

3.8 Bibliographic Notes, 62

4. Analysis and Composition of Partially-Compatible

Web Services 65

4.1 Problem Definition and Motivating Scenario, 65

4.1.1 A Motivating Scenario, 68

4.2 Petri Net Formalism for BPEL Service, Mediation, and

Compatibility, 70

4.2.1 CPN Formalism for BPEL Process, 70

4.2.2 CPN Formalism for Service Composition, 73

4.2.3 Mediator and Mediation-Aided Service Composition, 75

4.3 Compatibility Analysis via Petri Net Models, 78

4.3.1 Transforming Abstract BPEL Process to SWF-net, 79

4.3.2 Specifying Data Mapping, 80

4.3.3 Mediator Existence Checking, 81

4.3.4 Proof of Theorem 4.1, 85

4.4 Mediator Generation Approach, 88

4.4.1 Types of Mediation, 88

4.4.2 Guided Mediator Generation, 90

4.5 Bibliographic Notes, 94

4.5.1 Web Service Composition, 94

4.5.2 Business Process Integration, 94

4.5.3 Web Service Configuration, 94

4.5.4 Petri Net Model of BPEL Processes, 94

4.5.5 Component/Web Service Mediation, 95

5. Web Service Configuration with Multiple

Quality-of-Service Attributes 99

5.1 Introduction, 99

5.2 Quality-of-Service Measurements, 104

5.2.1 QoS Attributes, 104

5.2.2 Aggregation, 104

5.2.3 Computation of QoS, 105

5.3 Assembly Petri Nets and Their Properties, 107

5.3.1 Assembly and Disassembly Petri Nets, 107

5.3.2 Definition of Incidence Matrix and State-Shift Equation, 110

5.3.3 Definition of Subgraphs and Solutions, 111

5.4 Optimal Web Service Configuration, 114

5.4.1 Web Service Configuration under Single QoS

Objective, 115

5.4.2 Web Service Configuration under Multiple QoS

Objectives, 116

5.4.3 Experiments and Performance Analysis, 117

5.5 Implementation, 121

5.6 Summary, 123

5.7 Bibliographic Notes, 124

6. A Web Service-Based Public-Oriented Personalized

Health Care Platform 127

6.1 Background and Motivation, 127

6.2 System Architecture, 129

6.2.1 The System Architecture of PHISP, 129

6.2.2 Services Encapsulated in PHISP, 131

6.2.3 Composite Service Specifications, 133

6.2.4 User/Domain Preferences, 134

6.3 Web Service Composition with Branch Structures, 137

6.3.1 Basic Ideas and Concepts, 137

6.3.2 Service Composition Planner Supporting Branch

Structures, 139

6.3.3 Illustrating Examples, 148

6.4 Web Service Composition with Parallel Structures, 153

6.5 Demonstrations and Results, 155

6.5.1 WSC Example in PHISP, 155

6.5.2 Implementation of PHISP, 158

6.6 Summary, 159

7. Scientific Workflows Enabling Web-Scale

Collaboration 161

7.1 Service-Oriented Infrastructure for Science, 162

7.1.1 Service-Oriented Scientific Exploration, 162

7.1.2 Case Study: The Cancer Grid (caGrid), 166

7.2 Scientific Workflows in Service-Oriented Science, 167

7.2.1 Scientific Workflow: Old Wine in New Bottle? 167

7.2.2 caGrid Workflow Toolkit, 174

7.2.3 Exemplary caGrid Workflows, 183

7.3 Summary, 188

8. Network Analysis and Reuse of Scientific

Workflows 189

8.1 Social Computing Meets Scientific Workflow, 190

8.1.1 Social Network Services for Scientists, 191

8.1.2 Related Research Work, 197

8.2 Network Analysis of myExperiment, 199

8.2.1 Network Model at a Glance, 199

8.2.2 Undirected Network, 200

8.2.3 Directed Graph, 205

8.2.4 Summary of Findings, 206

8.3 ServiceMap: Providing Map and GPS Assisting Service

Composition in Bioinformatics, 207

8.3.1 Motivation, 207

8.3.2 ServiceMap Approach, 209

8.3.3 What Do People Who Use These Services Also Use? 210

8.3.4 What is an Operation Chain Between

Services/Operations, 212

8.3.5 An Empirical Study, 218

8.4 Summary, 219

9. Future Perspectives 221

9.1 Workflows in Hosting Platforms, 222

9.2 Workflows Empowered by Social Computing, 223

9.3 Workflows Meeting Big Data, 224

9.4 Emergency Workflow Management, 225

Abbreviations List 227

References 231

Index 247

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