The National Science Foundation (NSF) is the leading sponsor of basic academic research in engineering, and its influence far exceeds its budget. We think NSF is at its best when it uses that influence to focus interest within the researcher community on critical new challenges and technologies. NSF's Scalable Enterprise Systems (SES) initiative, for which we were responsible in our successive terms in the division of Design, Manufacture and Industrial Innovation (DMII), was just such a venture. A collaborative effort spanning NSF's engineering and computer science directorates, SES sought to concentrate the energies of the academic engineering research community on developing a science base for designing, planning and controlling the extended, spatially and managerially distributed enterprises that have become the norm in the manufacture, distribution and sale of the products of U. S. industry. The of associated issues addressed included everything from management supply chains, to product design across teams of collaborating companies, to e-marketing and make-to-order manufacturing, to the information technology challenges of devising inter-operable planning and control tools that can scale with exploding enterprise size and scope. A total of 27 teams with nearly 100 investigators were selected from the 89 submitted proposals in the Phase I, exploratory part of the effort (see the list below). Seven of these were awarded larger multi-year grants to continue their research in Phase II. As the contents of this book amply illustrate, these investigations continue to flourish, with and without direct NSF support.

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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…