Computer-supported co-operative work (CSCW) is a research area that aims at integrating the works of several people involved in a common goal, inside a co-operative universe, through the sharing of resources in an efficient way. This report contains the papers presented at a conference on CSCW in design. Topics covered include: techniques, methods, and tools for CSCW in design; social organization of the CSCW process; integration of methods & tools within the work organization; co-operation in virtual enterprises and electronic businesses; CSCW in design & manufacturing; interaction between the CSCW approach and knowledge reuse as found in knowledge management; intelligent agent & multi-agent systems; Internet/World Wide Web and CSCW in design; and applications & test beds.

This book gathers selected high-quality research papers presented at the Sixth International Congress on Information and Communication Technology, held at Brunel University, London, on February 25–26, 2021. It discusses emerging topics pertaining to information and communication technology (ICT) for managerial applications, e-governance, e-agriculture, e-education and computing technologies, the Internet of things (IoT) and e-mining. Written by respected experts and researchers working on ICT, the book offers a valuable asset for young researchers involved in advanced studies. The book is presented in four volumes.

This book provides practical knowledge on different aspects of information and knowledge management in businesses. In contemporary unstable time, enterprises/businesses deal with various challenges—such as large-scale competitions, high levels of uncertainty and risk, rush technological advancements, while increasing customer requirements. Thus, businesses work continually on improving efficiency of their operations and resources towards enabling sustainable solutions based on the knowledge and information accumulated previously. Consequently, this third volume of our subline persists to highlight different approaches of handling enterprise knowledge/information management directing to the importance of unceasing progress of structural management for the steady growth. We look forward that the works of this volume can encourage and initiate further research on this topic.

This book constitutes the thoroughly refereed joint post-proceedings of the VLDB 2002 Workshop on Efficiency and Effectiveness of XML Tools and Techniques, EEXTT and the CAiSE 2002 Workshop on Data Integration over the Web, DIWeb. The 10 revised full papers presented were carefully selected during two rounds of reviewing and revision. The papers are organized in topical sections on XML languages, XML modeling and integration, XML storage, benchmarking XML, and data integration over the Web.

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at https://www.cs.waikato.ac.nz/~ml/weka/book.html. It contains - Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book - Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book - Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. - Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects - Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods - Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface - Includes open-access online courses that introduce practical applications of the material in the book

Reference and compendium of algorithms for pattern recognition, data mining and statistical computing.