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Date

23. 3.2008 19:10:22

Footnote

Rez. in: JASIST 54(2003) no.9, S.905-906 (C.A. Badurek): "Visual approaches for knowledge discovery in very large databases are a prime research need for information scientists focused an extracting meaningful information from the ever growing stores of data from a variety of domains, including business, the geosciences, and satellite and medical imagery. This work presents a summary of research efforts in the fields of data mining, knowledge discovery, and data visualization with the goal of aiding the integration of research approaches and techniques from these major fields. The editors, leading computer scientists from academia and industry, present a collection of 32 papers from contributors who are incorporating visualization and data mining techniques through academic research as well application development in industry and government agencies. Information Visualization focuses upon techniques to enhance the natural abilities of humans to visually understand data, in particular, large-scale data sets. It is primarily concerned with developing interactive graphical representations to enable users to more intuitively make sense of multidimensional data as part of the data exploration process. It includes research from computer science, psychology, human-computer interaction, statistics, and information science. Knowledge Discovery in Databases (KDD) most often refers to the process of mining databases for previously unknown patterns and trends in data. Data mining refers to the particular computational methods or algorithms used in this process. The data mining research field is most related to computational advances in database theory, artificial intelligence and machine learning. This work compiles research summaries from these main research areas in order to provide "a reference work containing the collection of thoughts and ideas of noted researchers from the fields of data mining and data visualization" (p. 8). It addresses these areas in three main sections: the first an data visualization, the second an KDD and model visualization, and the last an using visualization in the knowledge discovery process. The seven chapters of Part One focus upon methodologies and successful techniques from the field of Data Visualization. Hoffman and Grinstein (Chapter 2) give a particularly good overview of the field of data visualization and its potential application to data mining. An introduction to the terminology of data visualization, relation to perceptual and cognitive science, and discussion of the major visualization display techniques are presented. Discussion and illustration explain the usefulness and proper context of such data visualization techniques as scatter plots, 2D and 3D isosurfaces, glyphs, parallel coordinates, and radial coordinate visualizations. Remaining chapters present the need for standardization of visualization methods, discussion of user requirements in the development of tools, and examples of using information visualization in addressing research problems.
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.

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RSWK

Natürlichsprachiges System / Dokumentverarbeitung / Wissensrepräsentation / Benutzermodell / Information Retrieval / Relationales Datenmodell
Natürlichsprachiges System / Dokumentverarbeitung / Wissensrepräsentation / Benutzermodell / Informationsauswahl / Relationales Datenmodell

Subject

Natürlichsprachiges System / Dokumentverarbeitung / Wissensrepräsentation / Benutzermodell / Information Retrieval / Relationales Datenmodell
Natürlichsprachiges System / Dokumentverarbeitung / Wissensrepräsentation / Benutzermodell / Informationsauswahl / Relationales Datenmodell

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RSWK

Natürlichsprachiges System / Dokumentverarbeitung / Wissensrepräsentation / Benutzermodell / Information Retrieval / Relationales Datenmodell
Natürlichsprachiges System / Dokumentverarbeitung / Wissensrepräsentation / Benutzermodell / Informationsauswahl / Relationales Datenmodell

Subject

Natürlichsprachiges System / Dokumentverarbeitung / Wissensrepräsentation / Benutzermodell / Information Retrieval / Relationales Datenmodell
Natürlichsprachiges System / Dokumentverarbeitung / Wissensrepräsentation / Benutzermodell / Informationsauswahl / Relationales Datenmodell

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RSWK

Semantic Web / Wissensbasiertes System / Ontologie <Wissensverarbeitung> / Aufsatzsammlung

Subject

Semantic Web / Wissensbasiertes System / Ontologie <Wissensverarbeitung> / Aufsatzsammlung

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Abstract

Information retrieval (IR) is a complex human activity supported by sophisticated systems. Information science has contributed much to the design and evaluation of previous generations of IR system development and to our general understanding of how such systems should be designed and yet, due to the increasing success and diversity of IR systems, many recent textbooks concentrate on IR systems themselves and ignore the human side of searching for information. This book is the first text to provide an information science perspective on IR. Unique in its scope, the book covers the whole spectrum of information retrieval, including: history and background information; behaviour and seeking task-based information; searching and retrieval approaches to investigating information; interaction and behaviour information; representation access models; evaluation interfaces for IR; interactive techniques; web retrieval, ranking and personalization; and, recommendation, collaboration and social search multimedia: interfaces and access. A key text for senior undergraduates and masters' level students of all information and library studies courses, this book is also useful for practising LIS professionals who need to better appreciate how IR systems are designed, implemented and evaluated.

RSWK

Information Retrieval / Informationsverhalten / Online-Recherche / Information-Retrieval-System / Aufsatzsammlung

Subject

Information Retrieval / Informationsverhalten / Online-Recherche / Information-Retrieval-System / Aufsatzsammlung

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Abstract

Information or Document Retrieval is the subject of this book. It is not an introductory book, although it is self-contained in the sense that it is not necessary to have a background in the theory or practice of Information Retrieval in order to understand its arguments. The book presents, as clearly as possible, one particular perspective on Information Retrieval, and attempts to say that certain aspects of the theory or practice of the management of documents are more important than others. The majority of Information Retrieval research has been aimed at the more experimentally tractable small-scale systems, and although much of that work has added greatly to our understanding of Information Retrieval it is becoming increasingly apparent that retrieval systems with large data bases of documents are a fundamentally different genre of systems than small-scale systems. If this is so, which is the thesis of this book, then we must now study large information retrieval systems with the same rigor and intensity that we once studied small-scale systems. Hegel observed that the quantitative growth of any system caused qualitative changes to take place in its structure and processes.

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Content

Inhalt: Digital Library Architectures Evaluation and Usability User Interfaces and Presentation New Approaches to Information Retrieval - From Abstract to Virtual Entities: Implementation of Work-Based Searching in a Multimedia Digital Library / Mark Notess, Jenn Riley, and Harriette Hemmasi Interoperability Enhanced Indexing and Searching Methods Personalisation and Annotation Music Digital Libraries Personal Digital Libraries Innovative Technologies for Digital Libraries Open Archives Initiative New Models and Tools User-Centred Design - Evaluating Strategic Support for Information Access in the DAFFODIL System / Claus-Peter Klas, Norbert Fuhr, and Andre Schaefer Innovative Technologies for Digital Libraries

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