Scalable Enterprise Systems

1. A Review of Enterprise Process Modelling Techniques.- 1. Introduction.- 1.1 Enterprise modeling.- 1.1.1 The GERAM Framework.- 1.1.2 Enterprise process modeling.- 1.2 Significance of process modelling in the context of enterprise systems.- 1.3 Outline of the chapter.- 2. Review of Process Modelling Techniques.- 2.1 Data flow diagrams (DFD).- 2.2 IDEF0/IDEF3.- 2.2.1 IDEF0.- 2.2.2 IDEF3.- 2.3 CIMOSA.- 2.4 ARIS and its event-based process chain method.- 2.5 Event-driven process chain (EPC) method in SAP R/3.- 2.6 Integrated enterprise modelling (IEM) method.- 2.7 Toronto virtual enterprise (TOVE) method.- 2.8 Baan’s dynamic enterprise modelling (DEM) technique.- 2.9 Unified Modelling Language (UML).- 2.10 Workflow management.- 2.11 Evaluation of process modelling techniques — a summary.- 3. Modelling Next-Generation Enterprises.- 3.1 Modelling and incorporating distributed computing.- 3.2 Integrating process description and analysis.- 3.3 Linking engineering and business processes to support process improvement initiatives.- 3.4 Incorporating activity-based management approaches.- 4. The Distributed Integrated modelling of enterprises (DME) framework.- 4.1 Petri nets as a theoretical base.- 4.2 DIME framework development.- 4.2.1 Enterprise analysis using Petri net models.- 4.2.2 Current and future work on the DIME framework.- 5. Conclusions.- 6. Acknowledgements.- 7. References.- 2. Design and Manufacturing Process Management in a Network of Distributed Suppliers.- 1. Introduction.- 2. Background.- 2.1 Why not just optimize?.- 2.2 Abstracting the key dimensions of the problem: What kind of problem is this?.- 2.3 Adaptive planning systems.- 2.4 Problem space perspective on process planning.- 3. Process modeling: A brief review.- 4. Functional Requirements of Process Management: Specification and Execution.- 4.1 Process Specification.- 4.2 Execution Environment.- 5. Description of Midas System.- 5.1 Process grammar.- 6. Process Flow Generation and Execution.- 6.1 XML-based scalability.- 7. Percolation and Sensitivity Analysis: Process Expansion.- 7.1 Productions for Design Task.- 7.2 Productions for Manufacturing Task.- 8. A simple example.- 9. Conclusion.- 3. Finite Automata Modeling and Analysis of Supply Chain Networks.- 1. Introduction.- 2. Preliminaries.- 2.1 Supply Chains: Tasks and Dependencies.- 2.2 Discrete Event System Theory — A Control Theoretic Approach.- 2.3 Supervisory Controller.- 3. Supply Chain Modeling.- 3.1 Deriving the Behavioral Model.- 3.2 Deriving the Specification Model.- 4. Supply Chain Analysis.- 4.1 Supply Chain Consistency.- 4.2 Redundancy Checking.- 4.2.1 Control Specification Redundancy.- 4.2.2 Event Redundancy (partial observation).- 4.3 Event Controllability Analysis.- 4.4 Scalability.- 4.4.1 Task Scalability.- 4.4.2 Specification Scalability.- 5. “GOURMET-TO-GO”- A Case Study.- 6. Conclusion.- 4. Distributed Control Algorithms for Scalable Decision-Making from Sensors-to Suppliers.- 1. Introduction.- 2. Feedback Control of Discrete Event-Timing.- 3. Modeling Event Timing Control Using Discontinuous Differential Equations.- 3.1 Definitions.- 3.1.1 Closure of Convex Hull.- 3.1.2 Measure Zero.- 3.1.3 Piecewise Continuous Function.- 3.1.4 Absolutely Continuous Function.- 3. 2 Solution of Discontinuous Differential Equations.- 3.3 Distributed Arrival Time Control Solution.- 3.3.1 Solution in Decoupled Region.- 3.3.2 Solution in Dead-Zone Region.- 3.3.3 Solution in Discontinuity Region.- 3.3.4 Convex Hull Geometry.- 3.3.5 Steady-State Arrival Time.- 3.3.6 Two Part, One Machine Case.- 3.3.7 Three-Part, One-Machine Case.- 3.4 Extensions and Generalizations of Event Timing Control.- 4. Unified Modeling and Control from Sensors-to-Suppliers.- 5. Conclusions.- 6. References.- 5. Collaborative Multiagent Based Information Infrastructure for Transportation Problem Solving.- 1. Introduction.- 2. The Transportation problem.- 3. AGENT Interactions.- 3.1 KQML and Logistics Language (LogL).- 3.2 KOMI, interactions in link a.- 3.3 KQML interactions in link b.- 3.4 KQML interactions in link c.- 3.5 KQML interactions in link d.- 3.6 All Other Interaction Links.- 4. Multiagent Model.- 4.1 The Model of a Master.- 4.2 The Model of a Slave.- 4.3 Putting It All Together.- 5. Comparison with Other Research.- 6. Conclusions.- 6. Improving Scalability of E-Commerce Systems with Knowledge Discovery.- 1. Background.- 2. Case Study: Online Auctions for Recyclable Products.- 3. The Curse of Dimensionality.- 3.1 Feature Selection.- 3.1.1 An Information Gain Filter.- 3.1.2 Correlation-Based Feature Selection.- 3.2 Clustering.- 3.2.1 Clustering Using the k-Means Algorithm.- 3.2.2 Clustering Using the Cob Web Algorithm.- 4. Expediting the System Operations.- 4.1 Decision Trees.- 4.2 Support Vector Machines.- 4.3 Association Rule Discovery.- 5. Improving Scalability of an Online Auction System.- 6. Conclusions.- References.- 7. A Scalable Supply Chain Infrastructure Research Test-Bed.- 1. Background.- 1.1 Introduction.- 1.2 Literature Review.- 2. A Scalable SCI test-bed Architecture.- 2.1 Test-bed Components.- 2.2 SCI Test-bed Architecture.- 2.3 SCI Test-bed Integration Data Models.- 2.4 SCI Integration Business Models.- 3. Advanced decision models.- 3.1 Available-to-Promise Decision Models.- 3.2. Simulation-Based Decision Models.- 4. Support for Other Research Projects.- 4.1 Performance Scalability in Supply Chain Infrastructures.- 4.2 Toshiba Global Supply Chain Model.- 4.3 Impact of Internet on Supply Chain Architectures.- 5. Conclusions.- 6. Acknowledgements.- 7. References.- 8. Publish Subscribe Middleware.- 1. Introduction.- 1.1 What is a publish subscribe system.- 1.2 Classification of publish subscribe system.- 1.2.1 Subject based.- 1.2.2 Content based.- 2. Evolution of Publish Subscribe Systems.- 2.1 Internet News.- 2.2 Group Communication Systems.- 2.3 Linda.- 2.4 Information Bus.- 2.5 Tibco Randezvous.- 2.6 Content Based Systems.- 3. Design Issues in Content Based Systems.- 3.1 Event Matching and Delivery.- 3.2 Security in Content Based Systems.- 4. Event Multicast in Content Based Systems.- 4.1 Ideal Algorithm.- 4.2 Flooding.- 4.3 Clustered Group Multicast (CGM).- 4.4 The Neighbor Matching Algorithm.- 4.5 Group Approximation Algorithm.- 4.6 Summary of Multicast Algorithms.- 5. Secure end point delivery.- 5.1 Group Key Caching.- 5.1.1 Simple caching.- 5.1.2 Build-up cache.- 5.1.3 Clustered cache.- 5.1.4 Clustered popular cache.- 5.1.5 Simulations.- 5.2 Conclusions.- 6. Summary.- 9. Experimental Study of Scalability Enhancement for Reverse Logistics E-Commerce.- 1. Introduction.- 2. Description of Experimental Prototype.- 2.1 Roles of Manufacturer, Demanufacturer, and Recycler.- 2.2 Sealed Bid Double Auction (SBDA) Mechanism.- 3. Experimental Design.- 3.1 Data Analysis.- 3.2 Results.- 4. Auction Recommender.- 4.1 Constructing a Recommendation.- 5. Conclusion.- 10. Web-Based Distributed Multi-Agent Architecture for Implementing Value Nets.- 1. Introduction.- 2. Literature Review.- 3. Multi-Agent Architecture.- 4. Agent Communication.- 4.1 Information/workflow within the multi-agent value net.- 4.2 BDI model.- 4.3 Semantics of the Valcomm Performatives.- 4.4 Multi-Agent interactions.- 4.4.1 Customer/Coordinator — Coordinator Interactions (Link A).- 4.4.2 Coordinator — Department (Link B).- 4.4.3 Internal Departmental-External Coordinator Interactions (Link C).- 5. Implementation of Multi-agents for value nets.- 6. Conclusions.- 7. Future work.