Call for Papers in Book DATA MINING FOR DESIGN AND MANUFACTURING: Methods and Applications Editor: Dr. Dan Braha Kluwer Academic Publishers Data Mining for Design and Manufacturing: Methods and Applications will be published by Kluwer Academic Publishers. The book is a volume in a series called "Massive Computing" that is organized by James Abello (AT&T Labs Research), Panos Pardalos (Univ. of Florida) and Mauricio Resende (AT&T Labs Research). The book is especially important since it will bring together the latest research and practice on the relationship between data mining and design and manufacturing environments. DESCRIPTION AND PURPOSE OF THE BOOK: Data Mining for Design and Manufacturing: Methods and Applications will bring together the latest research and practice on the relationship between data mining and design and manufacturing environments. Topics include data warehouses, marts, process, tasks, (e.g., association, clustering, classification, forecast), methods (e.g., statistics, decision trees and rules, neural networks, fuzzy learning, and case-based reasoning); machine learning in design (e.g., knowledge acquisition, learning in analogical design, conceptual design, and learning for design reuse); data mining for product development and concurrent engineering; design and manufacturing warehousing; computer-integrated manufacturing (CIM) and data mining; data mining for Material Requirements Planning (MRP); Enterprise Resource Planning (ERP) and Workflow Management; process and quality control; process analysis; data representation/visualization; fault diagnosis; adaptive schedulers; and learning in robotics. The contributors will include leading researchers and practitioners from academia and industry. Data Mining is defined as the process of extracting valid, previously unknown, comprehensible information from large databases in order to improve and optimize business decisions. Data mining methods have been used in various industrial fields, and have led to a broad range of research efforts. Powerful computerized integrated design and manufacturing tools (such as CAD, CAM, MRP and ERP) for collecting and managing data are in use in virtually all mid-range and large manufacturing companies. Over time, more and more product development, design, operation, and performance data are accumulated and computerized during product design and manufacturing processes. The abundance of data generated and collected during daily operations has impeded the ability to extract useful knowledge. In design and manufacturing environments, this situation calls for new techniques and tools that can intelligently and (semi)automatically turn low-level data into high-level and useful knowledge. The first of its kind, the objective of this book is to demonstrate the potential of data mining in design and manufacturing environments. The book provides an explanation of how data mining technology can be employed beyond prediction and modeling, and how to overcome several central problems in design and manufacturing environments. Practitioners can gain insight on how data mining is integrated with standard CAD/CAM, MRP, and ERP Systems. The book also presents the formal tools required to extract valuable information from design and manufacturing data (e.g., patterns, trends, associations, and dependencies), and thus facilitates interdisciplinary problem solving and optimizes design and manufacturing decisions. PROPOSED SCHEDULE: Declaration of interest: June 15, 2000 First draft due: December 15, 2000 Reviews to authors: January 15, 2000 Revised papers due to editor: March 15, 2001 Expected publication: May, 2001 Authors should submit full papers. Papers will be refereed. Submissions should be made electronically, preferably in MS-Word, to Professor Dan Braha at brahad@bgumail.bgu.ac.il PROSPECTIVE TOPICS: This book is oriented toward the exploration of recent advances in Data Mining as related to Engineering Design and Manufacturing, and the stimulation of further research and application in this area. Papers that represent significant contributions in the following broad range of domains are welcome: Data Mining for Product Development: * Enterprise Learning (For example: Integrated systems and technologies; Knowledge sharing; Enhancing service exchange networks) * Data Mining for Concept Development (For example: Extracting patterns from customer needs; Learning interrelationships between customer needs and design specifications; Clustering of design concepts; Indexing and retrieval of design concepts in knowledge bases; Data mining procedures for concept selection) * Data Mining for System-level Design * Prediction of Product Development Span Time and Cost * Project Evaluation * Visualizing Relationships in Large Product Development Databases * Concurrent Engineering (For example: Extracting interrelationships between design requirements and manufacturing specifications; Integration of data mining and team decision support; Exploring tradeoffs between overlapping activities and coordination costs; Combining expert knowledge) * Marketing and Logistics (For example: Supply and delivery forecasting; Learning suppliers, customers, partners involved in transportation and distribution; Time Series Analysis with Neural Networks for Inventory Applications) Data Mining for Engineering Design: * Data Mining Support for Design from Physical Principles * Extracting Guidelines and Rules for Design-for-X (manufacturability, assembly, economics, environment) * Extracting Product Characteristics from Prototypes * Case-based Reasoning in Conceptual Design * Clustering of Design Cases for Design Reuse * Interactive Exploration for Conceptual Design * Design Knowledge Acquisition with Data Mining * Design Analysis * Materials Data Mining * Dynamic Indexing and Retrieval of Design Information in Knowledge Bases * Creative Design using Genetic Algorithms and Evolutionary Programming * Cost Evaluation Systems (design for assembly and manufacturing) * Industrial Design * Integrated Data Mining and Design of Experiments Data Mining for Manufacturing: * Selection of Materials and Manufacturing Processes * Time Series Analysis and Data Mining * Fault Diagnosis * Data Mining for Preventive Machine Maintenance * Manufacturing Knowledge Acquisition with Data Mining * Process and Quality Control * Predicting Assembling Errors * Process Analysis * Operational Manufacturing Control (For example: schedules that learn, the effect of local dynamic behavior on global outcomes) * Dynamic indexing and retrieval of manufacturing information in knowledge bases * Summarization and abstraction of large and high-dimensional data (Self Organizing Map (SOM) based data visualization methods) * Adaptive Human-Machine Interface for Machine Operation * Feature Selection and Dimensionality Reduction of Manufacturing Data * Extracting Process Yield Classes with SOM * Feature Recognition with SOM and Neural Networks * Cutting tool-state classification for tool condition monitoring * Data Mining for Capturing Best Manufacturing Practice * Learning in the context of Robotics (For example: navigation and exploration, mapping, extracting knowledge from numerical and graphical sensor data) * VLSI implementation of Neural Networks and Fuzzy Systems Integrating Data Mining Systems: * Deploying Data Mining in On-line Systems * Data Mining for Enterprise Resource Planning and Workflow Management * Adaptation of Manufacturing Environments for Data Mining Applications * Infrastructure for Data Mining in Manufacturing (For example: integrating data mining with Computer Integrated Manufacturing, MRP, and CAD/CAM) Potential authors are encouraged to discuss with the editor (brahad@bgumail.bgu.ac.il) topics within and outside the above areas.