Search (5 results, page 1 of 1)

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Date

23. 3.2008 19:10:22

Footnote

In 13 chapters, Part Two provides an introduction to KDD, an overview of data mining techniques, and examples of the usefulness of data model visualizations. The importance of visualization throughout the KDD process is stressed in many of the chapters. In particular, the need for measures of visualization effectiveness, benchmarking for identifying best practices, and the use of standardized sample data sets is convincingly presented. Many of the important data mining approaches are discussed in this complementary context. Cluster and outlier detection, classification techniques, and rule discovery algorithms are presented as the basic techniques common to the KDD process. The potential effectiveness of using visualization in the data modeling process are illustrated in chapters focused an using visualization for helping users understand the KDD process, ask questions and form hypotheses about their data, and evaluate the accuracy and veracity of their results. The 11 chapters of Part Three provide an overview of the KDD process and successful approaches to integrating KDD, data mining, and visualization in complementary domains. Rhodes (Chapter 21) begins this section with an excellent overview of the relation between the KDD process and data mining techniques. He states that the "primary goals of data mining are to describe the existing data and to predict the behavior or characteristics of future data of the same type" (p. 281). These goals are met by data mining tasks such as classification, regression, clustering, summarization, dependency modeling, and change or deviation detection. Subsequent chapters demonstrate how visualization can aid users in the interactive process of knowledge discovery by graphically representing the results from these iterative tasks. Finally, examples of the usefulness of integrating visualization and data mining tools in the domain of business, imagery and text mining, and massive data sets are provided. This text concludes with a thorough and useful 17-page index and lengthy yet integrating 17-page summary of the academic and industrial backgrounds of the contributing authors. A 16-page set of color inserts provide a better representation of the visualizations discussed, and a URL provided suggests that readers may view all the book's figures in color on-line, although as of this submission date it only provides access to a summary of the book and its contents. The overall contribution of this work is its focus an bridging two distinct areas of research, making it a valuable addition to the Morgan Kaufmann Series in Database Management Systems. The editors of this text have met their main goal of providing the first textbook integrating knowledge discovery, data mining, and visualization. Although it contributes greatly to our under- standing of the development and current state of the field, a major weakness of this text is that there is no concluding chapter to discuss the contributions of the sum of these contributed papers or give direction to possible future areas of research. "Integration of expertise between two different disciplines is a difficult process of communication and reeducation. Integrating data mining and visualization is particularly complex because each of these fields in itself must draw an a wide range of research experience" (p. 300). Although this work contributes to the crossdisciplinary communication needed to advance visualization in KDD, a more formal call for an interdisciplinary research agenda in a concluding chapter would have provided a more satisfying conclusion to a very good introductory text.
With contributors almost exclusively from the computer science field, the intended audience of this work is heavily slanted towards a computer science perspective. However, it is highly readable and provides introductory material that would be useful to information scientists from a variety of domains. Yet, much interesting work in information visualization from other fields could have been included giving the work more of an interdisciplinary perspective to complement their goals of integrating work in this area. Unfortunately, many of the application chapters are these, shallow, and lack complementary illustrations of visualization techniques or user interfaces used. However, they do provide insight into the many applications being developed in this rapidly expanding field. The authors have successfully put together a highly useful reference text for the data mining and information visualization communities. Those interested in a good introduction and overview of complementary research areas in these fields will be satisfied with this collection of papers. The focus upon integrating data visualization with data mining complements texts in each of these fields, such as Advances in Knowledge Discovery and Data Mining (Fayyad et al., MIT Press) and Readings in Information Visualization: Using Vision to Think (Card et. al., Morgan Kauffman). This unique work is a good starting point for future interaction between researchers in the fields of data visualization and data mining and makes a good accompaniment for a course focused an integrating these areas or to the main reference texts in these fields."

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Editor

Staab, S. u. R. Studer

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Content

Inhalt: OIL and DAML + OIL: Ontology Languages for the Semantic Web (pages 11-31) / Dieter Fensel, Frank van Harmelen and Ian Horrocks A Methodology for Ontology-Based Knowledge Management (pages 33-46) / York Sure and Rudi Studer Ontology Management: Storing, Aligning and Maintaining Ontologies (pages 47-69) / Michel Klein, Ying Ding, Dieter Fensel and Borys Omelayenko Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema (pages 71-89) / Jeen Broekstra, Arjohn Kampman and Frank van Harmelen Generating Ontologies for the Semantic Web: OntoBuilder (pages 91-115) / R. H. P. Engels and T. Ch. Lech OntoEdit: Collaborative Engineering of Ontologies (pages 117-132) / York Sure, Michael Erdmann and Rudi Studer QuizRDF: Search Technology for the Semantic Web (pages 133-144) / John Davies, Richard Weeks and Uwe Krohn Spectacle (pages 145-159) / Christiaan Fluit, Herko ter Horst, Jos van der Meer, Marta Sabou and Peter Mika OntoShare: Evolving Ontologies in a Knowledge Sharing System (pages 161-177) / John Davies, Alistair Duke and Audrius Stonkus Ontology Middleware and Reasoning (pages 179-196) / Atanas Kiryakov, Kiril Simov and Damyan Ognyanov Ontology-Based Knowledge Management at Work: The Swiss Life Case Studies (pages 197-218) / Ulrich Reimer, Peter Brockhausen, Thorsten Lau and Jacqueline R. Reich Field Experimenting with Semantic Web Tools in a Virtual Organization (pages 219-244) / Victor Iosif, Peter Mika, Rikard Larsson and Hans Akkermans A Future Perspective: Exploiting Peer-To-Peer and the Semantic Web for Knowledge Management (pages 245-264) / Dieter Fensel, Steffen Staab, Rudi Studer, Frank van Harmelen and John Davies Conclusions: Ontology-driven Knowledge Management - Towards the Semantic Web? (pages 265-266) / John Davies, Dieter Fensel and Frank van Harmelen

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Footnote

Rez. in: Information Research, 9(3), review no. R129 (T.D. Wilson): "The first edition of this book was published ten years ago and rapidly become something of a classic in the field of information seeking behaviour. It is good to see the second edition which incorporates not only the work the author has done since 1993, but also related work by other researchers. Kuhlthau is one of the most cited authors in the field and her model of the information search process, involving stages in the search and associated feelings, has been used by others in a variety of contexts. However, what makes this book different (as was the case with the first edition) is the author's dedication to the field of practice and the book's sub-title demonstrates her commitment to the transfer of research. In Kuhlthau's case this is the practice of the school library media specialist, but her research has covered students of various ages as well as a wide range of occupational groups. Because the information search model is so well known, I shall concentrate in this review on the relationship between the research findings and practice. It is necessary, however, to begin with the search process model, because this is central. Briefly, the model proposes that the searcher goes through the stages of initiation, selection, exploration, formulation, collection and presentation, and, at each stage, experiences various feelings ranging from optimism and satisfaction to confusion and disappointment. Personally, I occasionally suffer despair, but perhaps that is too extreme for most!