Search (95 results, page 3 of 5)

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Abstract

Data mining (DM) is part of a process by which information can be extracted from data or databases and used to inform decision making in a variety of contexts (Benoit, 2002; Michalski, Bratka & Kubat, 1997). DM includes a range of tools and methods for extractiog information; their use in the commercial sector for knowledge extraction and discovery has been one of the main driving forces in their development (Adriaans & Zantinge, 1996; Benoit, 2002). DM has been developed and applied in numerous areas. This review describes its use in analyzing health and medical information.

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Abstract

Imagine a world where you walk up to a computer and sing the song fragment that has been plaguing you since breakfast. The computer accepts your off-key singing, corrects your request, and promptly suggests to you that "Camptown Races" is the cause of your irritation. You confirm the computer's suggestion by listening to one of the many MP3 files it has found. Satisfied, you kindly decline the offer to retrieve all extant versions of the song, including a recently released Italian rap rendition and an orchestral score featuring a bagpipe duet. Does such a system exist today? No. Will it in the future? Yes. Will such a system be easy to produce? Most decidedly not. Myriad difficulties remain to be overcome before the creation, deployment, and evaluation of robust, large-scale, and content-based Music Information Retrieval (MIR) systems become reality. The dizzyingly complex interaction of music's pitch, temporal, harmonic, timbral, editorial, textual, and bibliographic "facets," for example, demonstrates just one of MIR's perplexing problems. The choice of music representation-whether symbol-based, audio-based, or both-further compounds matters, as each choice determines bandwidth, computation, storage, retrieval, and interface requirements and capabilities.

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Abstract

A community of practice has recently been defined as "a flexible group of professionals, informally bound by common interests, who interact through interdependent tasks guided by a common purpose thereby embodying a store of common knowledge" (Jubert, 1999, p. 166). The association of communities of practice with the production of collective knowledge has long been recognized, and they have been objects of study for a number of decades in the context of professional communication, particularly communication in science (Abbott, 1988; Bazerman & Paradis, 1991). Recently, however, they have been invoked in the domain of organization studies as sites where people learn and share insights. If, as Stinchcombe suggests, an organization is "a set of stable social relations, dehberately created, with the explicit intention of continuously accomplishing some specific goals or purposes" (Stinchcombe, 1965, p. 142), where does this "flexible" and "embodied" source of knowledge fit? Can communities of practice be harnessed, engineered, and managed like other organizational groups, or does their strength lie in the fact that they operate outside the stable and persistent social relations that characterize the organization?

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Abstract

"Theory weary, theory leery, why can't I be theory cheery?" (Erickson, 2002, p. 269). The field of human-computer interaction (HCI) is rapidly expanding. Alongside the extensive technological developments that are taking place, a profusion of new theories, methods, and concerns has been imported into the field from a range of disciplines and contexts. An extensive critique of recent theoretical developments is presented here together with an overview of HCI practice. A consequence of bringing new theories into the field has been much insightful explication of HCI phenomena and also a broadening of the field's discourse. However, these theoretically based approaches have had limited impact an the practice of interaction design. This chapter discusses why this is so and suggests that different kinds of mechanisms are needed that will enable both designers and researchers to better articulate and theoretically ground the challenges facing them today. Human-computer interaction is bursting at the seams. Its mission, goals, and methods, well established in the '80s, have all greatly expanded to the point that "HCI is now effectively a boundless domain" (Barnard, May, Duke, & Duce, 2000, p. 221). Everything is in a state of flux: The theory driving research is changing, a flurry of new concepts is emerging, the domains and type of users being studied are diversifying, many of the ways of doing design are new, and much of what is being designed is significantly different. Although potentially much is to be gained from such rapid growth, the downside is an increasing lack of direction, structure, and coherence in the field. What was originally a bounded problem space with a clear focus and a small set of methods for designing computer systems that were easier and more efficient to use by a single user is now turning into a diffuse problem space with less clarity in terms of its objects of study, design foci, and investigative methods. Instead, aspirations of overcoming the Digital Divide, by providing universal accessibility, have become major concerns (e.g., Shneiderman, 2002a). The move toward greater openness in the field means that many more topics, areas, and approaches are now considered acceptable in the worlds of research and practice.
A problem with allowing a field to expand eclectically is that it can easily lose coherence. No one really knows what its purpose is anymore or what criteria to use in assessing its contribution and value to both knowledge and practice. For example, among the many new approaches, ideas, methods, and goals now being proposed, how do we know which are acceptable, reliable, useful, and generalizable? Moreover, how do researchers and designers know which of the many tools and techniques to use when doing design and research? To be able to address these concerns, a young field in a state of flux (as is HCI) needs to take stock and begin to reflect an the changes that are happening. The purpose of this chapter is to assess and reflect an the role of theory in contemporary HCI and the extent to which it is used in design practice. Over the last ten years, a range of new theories has been imported into the field. A key question is whether such attempts have been productive in terms of "knowledge transfer." Here knowledge transfer means the translation of research findings (e.g., theory, empirical results, descriptive accounts, cognitive models) from one discipline (e.g., cognitive psychology, sociology) into another (e.g., human-computer interaction, computer supported cooperative work).

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Abstract

Advanced technology has resulted in the generation of about one million terabytes of information every year. Ninety-reine percent of this is available in digital format (Keim, 2001). More information will be generated in the next three years than was created during all of previous human history (Keim, 2001). Collecting information is no longer a problem, but extracting value from information collections has become progressively more difficult. Various search engines have been developed to make it easier to locate information of interest, but these work well only for a person who has a specific goal and who understands what and how information is stored. This usually is not the Gase. Visualization was commonly thought of in terms of representing human mental processes (MacEachren, 1991; Miller, 1984). The concept is now associated with the amplification of these mental processes (Card, Mackinlay, & Shneiderman, 1999). Human eyes can process visual cues rapidly, whereas advanced information analysis techniques transform the computer into a powerful means of managing digitized information. Visualization offers a link between these two potent systems, the human eye and the computer (Gershon, Eick, & Card, 1998), helping to identify patterns and to extract insights from large amounts of information. The identification of patterns is important because it may lead to a scientific discovery, an interpretation of clues to solve a crime, the prediction of catastrophic weather, a successful financial investment, or a better understanding of human behavior in a computermediated environment. Visualization technology shows considerable promise for increasing the value of large-scale collections of information, as evidenced by several commercial applications of TreeMap (e.g., http://www.smartmoney.com) and Hyperbolic tree (e.g., http://www.inxight.com) to visualize large-scale hierarchical structures. Although the proliferation of visualization technologies dates from the 1990s where sophisticated hardware and software made increasingly faster generation of graphical objects possible, the role of visual aids in facilitating the construction of mental images has a long history. Visualization has been used to communicate ideas, to monitor trends implicit in data, and to explore large volumes of data for hypothesis generation. Imagine traveling to a strange place without a map, having to memorize physical and chemical properties of an element without Mendeleyev's periodic table, trying to understand the stock market without statistical diagrams, or browsing a collection of documents without interactive visual aids. A collection of information can lose its value simply because of the effort required for exhaustive exploration. Such frustrations can be overcome by visualization.
Visualization can be classified as scientific visualization, software visualization, or information visualization. Although the data differ, the underlying techniques have much in common. They use the same elements (visual cues) and follow the same rules of combining visual cues to deliver patterns. They all involve understanding human perception (Encarnacao, Foley, Bryson, & Feiner, 1994) and require domain knowledge (Tufte, 1990). Because most decisions are based an unstructured information, such as text documents, Web pages, or e-mail messages, this chapter focuses an the visualization of unstructured textual documents. The chapter reviews information visualization techniques developed over the last decade and examines how they have been applied in different domains. The first section provides the background by describing visualization history and giving overviews of scientific, software, and information visualization as well as the perceptual aspects of visualization. The next section assesses important visualization techniques that convert abstract information into visual objects and facilitate navigation through displays an a computer screen. It also explores information analysis algorithms that can be applied to identify or extract salient visualizable structures from collections of information. Information visualization systems that integrate different types of technologies to address problems in different domains are then surveyed; and we move an to a survey and critique of visualization system evaluation studies. The chapter concludes with a summary and identification of future research directions.

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Abstract

This chapter reviews research and applications in statistical language modeling for information retrieval (IR), which has emerged within the past several years as a new probabilistic framework for describing information retrieval processes. Generally speaking, statistical language modeling, or more simply language modeling (LM), involves estimating a probability distribution that captures statistical regularities of natural language use. Applied to information retrieval, language modeling refers to the problem of estimating the likelihood that a query and a document could have been generated by the same language model, given the language model of the document either with or without a language model of the query. The roots of statistical language modeling date to the beginning of the twentieth century when Markov tried to model letter sequences in works of Russian literature (Manning & Schütze, 1999). Zipf (1929, 1932, 1949, 1965) studied the statistical properties of text and discovered that the frequency of works decays as a Power function of each works rank. However, it was Shannon's (1951) work that inspired later research in this area. In 1951, eager to explore the applications of his newly founded information theory to human language, Shannon used a prediction game involving n-grams to investigate the information content of English text. He evaluated n-gram models' performance by comparing their crossentropy an texts with the true entropy estimated using predictions made by human subjects. For many years, statistical language models have been used primarily for automatic speech recognition. Since 1980, when the first significant language model was proposed (Rosenfeld, 2000), statistical language modeling has become a fundamental component of speech recognition, machine translation, and spelling correction.

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Abstract

As an informational technology, the World Wide Web has enjoyed spectacular success. In just ten years it has transformed the way information is produced, stored, and shared in arenas as diverse as shopping, family photo albums, and high-level academic research. The "Semantic Web" is touted by its developers as equally revolutionary, although it has not yet achieved anything like the Web's exponential uptake. It seeks to transcend a current limitation of the Web - that it largely requires indexing to be accomplished merely on specific character strings. Thus, a person searching for information about "turkey" (the bird) receives from current search engines many irrelevant pages about "Turkey" (the country) and nothing about the Spanish "pavo" even if he or she is a Spanish-speaker able to understand such pages. The Semantic Web vision is to develop technology to facilitate retrieval of information via meanings, not just spellings. For this to be possible, most commentators believe, Semantic Web applications will have to draw on some kind of shared, structured, machine-readable conceptual scheme. Thus, there has been a convergence between the Semantic Web research community and an older tradition with roots in classical Artificial Intelligence (AI) research (sometimes referred to as "knowledge representation") whose goal is to develop a formal ontology. A formal ontology is a machine-readable theory of the most fundamental concepts or "categories" required in order to understand information pertaining to any knowledge domain. A review of the attempts that have been made to realize this goal provides an opportunity to reflect in interestingly concrete ways on various research questions such as the following: - How explicit a machine-understandable theory of meaning is it possible or practical to construct? - How universal a machine-understandable theory of meaning is it possible or practical to construct? - How much (and what kind of) inference support is required to realize a machine-understandable theory of meaning? - What is it for a theory of meaning to be machine-understandable anyway?

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Abstract

An online series of articles explaining controlled vocabularies and, in particular, faceted classification. It is not yet finished, but what they have covered is very well done, practical and informative, with useful advice and a full treatment. It is worth reading now, and when they actually get to performing facet analysis and making a faceted system, it will make a very useful reference.

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Abstract

Considerable documentation, tools, and manuals are available to aid catalogers, but without some guidance, many have no idea how to use these shelves of reference guides and manuals. This bibliography is intended as a guide to the information and resources available to assist the cataloger in cataloging. The availability of each resource is given (including online availability), along with a brief summary of the type of information that each resource contains.

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Abstract

The concept of information as we use it in everyday English, in the sense of knowledge communicated, plays a central role in contemporary society. The development and widespread use of computer networks since the end of World War II, and the emergence of information science as a discipline in the 1950s, are evidence of this focus. Although knowledge and its communication are basic phenomena of every human society, it is the rise of information technology and its global impacts that characterize ours as an information society. It is commonplace to consider information as a basic condition for economic development together with capital, labor, and raw material; but what makes information especially significant at present is its digital nature. The impact of information technology an the natural and social sciences in particular has made this everyday notion a highly controversial concept. Claude Shannon's (1948) "A Mathematical Theory of Communication" is a landmark work, referring to the common use of information with its semantic and pragmatic dimensions, while at the same time redefining the concept within an engineering framework. The fact that the concept of knowledge communication has been designated by the word information seems, prima facie, a linguistic happenstance. For a science like information science (IS), it is of course important how fundamental terms are defined; and in IS, as in other fields, the question of how to define information is often raised. This chapter is an attempt to review the status of the concept of information in IS, with reference also to interdisciplinary trends. In scientific discourse, theoretical concepts are not true or false elements or glimpses of some element of reality; rather, they are constructions designed to do a job in the best possible way. Different conceptions of fundamental terms like information are thus more or less fruitful, depending an the theories (and in the end, the practical actions) they are expected to support. In the opening section, we discuss the problem of defining terms from the perspective of the philosophy of science. The history of a word provides us with anecdotes that are tangential to the concept itself. But in our case, the use of the word information points to a specific perspective from which the concept of knowledge communication has been defined. This perspective includes such characteristics as novelty and relevante; i.e., it refers to the process of knowledge transformation, and particularly to selection and interpretation within a specific context. The discussion leads to the questions of why and when this meaning was designated with the word information. We will explore this history, and we believe that our results may help readers better understand the complexity of the concept with regard to its scientific definitions.
Discussions about the concept of information in other disciplines are very important for IS because many theories and approaches in IS have their origins elsewhere (see the section "Information as an Interdisciplinary Concept" in this chapter). The epistemological concept of information brings into play nonhuman information processes, particularly in physics and biology. And vice versa: the psychic and sociological processes of selection and interpretation may be considered using objective parameters, leaving aside the semantic dimension, or more precisely, by considering objective or situational parameters of interpretation. This concept can be illustrated also in physical terms with regard to release mechanisms, as we suggest. Our overview of the concept of information in the natural sciences as well as in the humanities and social sciences cannot hope to be comprehensive. In most cases, we can refer only to fragments of theories. However, the reader may wish to follow the leads provided in the bibliography. Readers interested primarily in information science may derive most benefit from the section an "Information in Information Science," in which we offer a detailed explanation of diverse views and theories of information within our field; supplementing the recent ARIST chapter by Cornelius (2002). We show that the introduction of the concept of information circa 1950 to the domain of special librarianship and documentation has in itself had serious consequences for the types of knowledge and theories developed in our field. The important question is not only what meaning we give the term in IS, but also how it relates to other basic terms, such as documents, texts, and knowledge. Starting with an objectivist view from the world of information theory and cybernetics, information science has turned to the phenomena of relevance and interpretation as basic aspects of the concept of information. This change is in no way a turn to a subjectivist theory, but an appraisal of different perspectives that may determine in a particular context what is being considered as informative, be it a "thing" (Buckland, 1991b) or a document. Different concepts of information within information science reflect tensions between a subjective and an objective approach. The concept of interpretation or selection may be considered to be the bridge between these two poles. It is important, however, to consider the different professions involved with the interpretation and selection of knowledge. The most important thing in IS (as in information policy) is to consider information as a constitutive forte in society and, thus, recognize the teleological nature of information systems and services (Braman, 1989).

Type

a