Search (163 results, page 1 of 9)

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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.
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."

LCSH

Information visualization

RSWK

Information Retrieval (BVB)

Subject

Information Retrieval (BVB)
Information visualization

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Abstract

This chapter reviews visualization techniques that can be used to map the ever-growing domain structure of scientific disciplines and to support information retrieval and classification. In contrast to the comprehensive surveys conducted in traditional fashion by Howard White and Katherine McCain (1997, 1998), this survey not only reviews emerging techniques in interactive data analysis and information visualization, but also depicts the bibliographical structure of the field itself. The chapter starts by reviewing the history of knowledge domain visualization. We then present a general process flow for the visualization of knowledge domains and explain commonly used techniques. In order to visualize the domain reviewed by this chapter, we introduce a bibliographic data set of considerable size, which includes articles from the citation analysis, bibliometrics, semantics, and visualization literatures. Using tutorial style, we then apply various algorithms to demonstrate the visualization effectsl produced by different approaches and compare the results. The domain visualizations reveal the relationships within and between the four fields that together constitute the focus of this chapter. We conclude with a general discussion of research possibilities. Painting a "big picture" of scientific knowledge has long been desirable for a variety of reasons. Traditional approaches are brute forcescholars must sort through mountains of literature to perceive the outlines of their field. Obviously, this is time-consuming, difficult to replicate, and entails subjective judgments. The task is enormously complex. Sifting through recently published documents to find those that will later be recognized as important is labor intensive. Traditional approaches struggle to keep up with the pace of information growth. In multidisciplinary fields of study it is especially difficult to maintain an overview of literature dynamics. Painting the big picture of an everevolving scientific discipline is akin to the situation described in the widely known Indian legend about the blind men and the elephant. As the story goes, six blind men were trying to find out what an elephant looked like. They touched different parts of the elephant and quickly jumped to their conclusions. The one touching the body said it must be like a wall; the one touching the tail said it was like a snake; the one touching the legs said it was like a tree trunk, and so forth. But science does not stand still; the steady stream of new scientific literature creates a continuously changing structure. The resulting disappearance, fusion, and emergence of research areas add another twist to the tale-it is as if the elephant is running and dynamically changing its shape. Domain visualization, an emerging field of study, is in a similar situation. Relevant literature is spread across disciplines that have traditionally had few connections. Researchers examining the domain from a particular discipline cannot possibly have an adequate understanding of the whole. As noted by White and McCain (1997), the new generation of information scientists is technically driven in its efforts to visualize scientific disciplines. However, limited progress has been made in terms of connecting pioneers' theories and practices with the potentialities of today's enabling technologies. If the difference between past and present generations lies in the power of available technologies, what they have in common is the ultimate goal-to reveal the development of scientific knowledge.

Source

Annual review of information science and technology. 37(2003), S.179-258

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Abstract

The study adopts a naturalistic approach to investigate users' interaction with a browsable MeSH (medical subject headings) display designed to facilitate query construction for the PubMed bibliographic database. The purpose of the study is twofold: first, to test the usefulness of a browsable interface utilizing the principle of faceted classification; and second, to investigate users' preferred query submission methods in different problematic situations. An interface that incorporated multiple query submission methods - the conventional single-line query box as well as methods associated the faceted classification display was constructed. Participants' interactions with the interface were monitored remotely over a period of 10 weeks; information about their problematic situations and information retrieval behaviors were also collected during this time. The traditional controlled experiment was not adequate in answering the author's research questions; hence, the author provides his rationale for a naturalistic approach. The study's findings show that there is indeed a selective compatibility between query submission methods provided by the MeSH display and users' problematic situations. The query submission methods associated with the display were found to be the preferred search tools when users' information needs were vague and the search topics unfamiliar. The findings support the theoretical proposition that users engaging in an information retrieval process with a variety of problematic situations need different approaches. The author argues that rather than treat the information retrieval system as a general purpose tool, more attention should be given to the interaction between the functionality of the tool and the characteristics of users' problematic situations.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.13, S.1998-2006

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Abstract

This paper examines some traditional information searching methods and their role in Hungarian OPACs. What challenges are there in the digital and online environment? How do users work with them and do they give users satisfactory results? What kinds of techniques are users employing? In this paper I examine the user interfaces of UDC, thesauri, subject headings etc. in the Hungarian library. The key question of the paper is whether a universal system or local solutions is the best approach for searching in the digital environment.

Theme

Klassifikationssysteme im Online-Retrieval

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Abstract

This review describes experimental designs (users, search tasks, measures, etc.) used by 31 controlled user studies of information visualization (IV) tools for textual information retrieval (IR) and a meta-analysis of the reported statistical effects. Comparable experimental designs allow research designers to compare their results with other reports, and support the development of experimentally verified design guidelines concerning which IV techniques are better suited to which types of IR tasks. The studies generally use a within-subject design with 15 or more undergraduate students performing browsing to known-item tasks on sets of at least 1,000 full-text articles or Web pages on topics of general interest/news. Results of the meta-analysis (N = 8) showed no significant effects of the IV tool as compared with a text-only equivalent, but the set shows great variability suggesting an inadequate basis of comparison. Experimental design recommendations are provided which would support comparison of existing IV tools for IR usability testing.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.6, S.1012-1024

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Abstract

As the volume and breadth of online information is rapidly increasing, ad hoc search systems become less and less efficient to answer information needs of modern users. To support the growing complexity of search tasks, researchers in the field of information developed and explored a range of approaches that extend the traditional ad hoc retrieval paradigm. Among these approaches, personalized search systems and exploratory search systems attracted many followers. Personalized search explored the power of artificial intelligence techniques to provide tailored search results according to different user interests, contexts, and tasks. In contrast, exploratory search capitalized on the power of human intelligence by providing users with more powerful interfaces to support the search process. As these approaches are not contradictory, we believe that they can re-enforce each other. We argue that the effectiveness of personalized search systems may be increased by allowing users to interact with the system and learn/investigate the problem in order to reach the final goal. We also suggest that an interactive visualization approach could offer a good ground to combine the strong sides of personalized and exploratory search approaches. This paper proposes a specific way to integrate interactive visualization and personalized search and introduces an adaptive visualization based search system Adaptive VIBE that implements it. We tested the effectiveness of Adaptive VIBE and investigated its strengths and weaknesses by conducting a full-scale user study. The results show that Adaptive VIBE can improve the precision and the productivity of the personalized search system while helping users to discover more diverse sets of information.

Footnote

Beitrag im Rahmen einer Special section on Human-computer Information Retrieval.

Source

Information processing and management. 49(2013) no.5, S.1139-1164

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Abstract

The use of thesaurus-based indexing is a common approach for increasing the performance of information retrieval. In this thesis, we examine the suitability of a thesaurus for a given set of information and evaluate improvements of existing thesauri to get better search results. On this area, we focus on two aspects: 1. We demonstrate an analysis of the indexing results achieved by an automatic document indexer and the involved thesaurus. 2. We propose a method for thesaurus evaluation which is based on a combination of statistical measures and appropriate visualization techniques that support the detection of potential problems in a thesaurus. In this chapter, we give an overview of the context of our work. Next, we briefly outline the basics of thesaurus-based information retrieval and describe the Collexis Engine that was used for our experiments. In Chapter 3, we describe two experiments in automatically indexing documents in the areas of medicine and economics with corresponding thesauri and compare the results to available manual annotations. Chapter 4 describes methods for assessing thesauri and visualizing the result in terms of a treemap. We depict examples of interesting observations supported by the method and show that we actually find critical problems. We conclude with a discussion of open questions and future research in Chapter 5.

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Abstract

The internal organization of large software projects requires an extraordinary effort in the development and maintenance of repositories made up of software artifacts (business components, data models, functional and technical documentation, etc.). During the software development process, different artifacts are created to help users in the transfer of knowledge and enable communication between workers and teams. The storage, maintenance and publication of these artifacts in knowledge bases - usually referred to as "software repositories" are a useful tool for future software development projects, as they contain the collective, learned experience of the teams and provide the basis to estimate and reuse the work completed in the past. Different techniques similar to those used by the library community have been used in the past to organize these software repositories and help users in the difficult task or identifying and retrieving artifacts (software and documentation). These techniques include software classification - with a special emphasis on faceted classifications, keyword-based retrieval and formal method techniques. The paper discusses the different knowledge organization techniques applied in these repositories to identify and retrieve software artifacts and ensure the reusability of software components and documentation at the different phases of the development process across different projects. An enumeration of the main approaches documented in specialized bibliography is provided.

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Abstract

Computers have become our companions in many of the activities we pursue in our life. They assist us, in particular, in searching relevant information that is needed to perform a variety of tasks, from professional usage to personal entertainment. They hold this information in a huge number of heterogeneous sources, either dedicated to a specific user community (e.g., enterprise databases) or maintained for the general public (e.g., websites and digital libraries). Whereas progress in basic information technology is nowadays capable of guaranteeing effective information management, information retrieval and dissemination has become a core issue that needs further accomplishments to achieve user satisfaction. The research communities in databases, information retrieval, information visualization, and human-computer interaction have already largely investigated these domains. However, the technical environment has so dramatically evolved in recent years, inducing a parallel and very significant evolution in user habits and expectations, that new approaches are definitely needed to meet current demand. One of the most evident and significant changes is the human-computer interaction paradigm. Traditional interactions relayed an programming to express user information requirements in formal code and an textual output to convey to users the information extracted by the system. Except for professional data-intensive application frameworks, still in the hands of computer speciahsts, we have basically moved away from this pattern both in terms of expressing information requests and conveying results. The new goal is direct interaction with the final user (the person who is looking for information and is not necessarily familiar with computer technology). The key motto to achieve this is "go visual." The well-known high bandwidth of the human-vision channel allows both recognition and understanding of large quantities of information in no more than a few seconds. Thus, for instance, if the result of an information request can be organized as a visual display, or a sequence of visual displays, the information throughput is immensely superior to the one that can be achieved using textual support. User interaction becomes an iterative query-answer game that very rapidly leads to the desired final result. Conversely, the system can provide efficient visual support for easy query formulation. Displaying a visual representation of the information space, for instance, lets users directly point at the information they are looking for, without any need to be trained into the complex syntax of current query languages. Alternatively, users can navigate in the information space, following visible paths that will lead them to the targeted items. Again, thanks to the visual support, users are able to easily understand how to formulate queries and they are likely to achieve the task more rapidly and less prone to errors than with traditional textual interaction modes.
The two facets of "going visual" are usually referred to as visual query systems, for query formulation, and information visualization, for result display. Visual Query Systems (VQSs) are defined as systems for querying databases that use a visual representation to depict the domain of interest and express related requests. VQSs provide both a language to express the queries in a visual format and a variety of functionalities to facilitate user-system interaction. As such, they are oriented toward a wide spectrum of users, especially novices who have limited computer expertise and generally ignore the inner structure of the accessed database. Information visualization, an increasingly important subdiscipline within the field of Human-Computer Interaction (HCI), focuses an visual mechanisms designed to communicate clearly to the user the structure of information and improve an the cost of accessing large data repositories. In printed form, information visualization has included the display of numerical data (e.g., bar charts, plot charts, pie charts), combinatorial relations (e.g., drawings of graphs), and geographic data (e.g., encoded maps). In addition to these "static" displays, computer-based systems, such as the Information Visualizer and Dynamic Queries, have coupled powerful visualization techniques (e.g., 3D, animation) with near real-time interactivity (i.e., the ability of the system to respond quickly to the user's direct manipulation commands). Information visualization is tightly combined with querying capabilities in some recent database-centered approaches. More opportunities for information visualization in a database environment may be found today in data mining and data warehousing applications, which typically access large data repositories. The enormous quantity of information sources an the World-Wide Web (WWW) available to users with diverse capabilities also calls for visualization techniques. In this article, we survey the main features and main proposals for visual query systems and touch upon the visualization of results mainly discussing traditional visualization forms. A discussion of modern database visualization techniques may be found elsewhere. Many related articles by Daniel Keim are available at http://www. informatik.uni-halle.de/dbs/publications.html.

Source

Encyclopedia of library and information science. Vol.72, [=Suppl.35]

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Abstract

In this article we present a method for retrieving documents from a digital library through a visual interface based on automatically generated concepts. We used a vocabulary generation algorithm to generate a set of concepts for the digital library and a technique called the max-min distance technique to cluster them. Additionally, the concepts were visualized in a spring embedding graph layout to depict the semantic relationship among them. The resulting graph layout serves as an aid to users for retrieving documents. An online archive containing the contents of D-Lib Magazine from July 1995 to May 2002 was used to test the utility of an implemented retrieval and visualization system. We believe that the method developed and tested can be applied to many different domains to help users get a better understanding of online document collections and to minimize users' cognitive load during execution of search tasks. Over the past few years, the volume of information available through the World Wide Web has been expanding exponentially. Never has so much information been so readily available and shared among so many people. Unfortunately, the unstructured nature and huge volume of information accessible over networks have made it hard for users to sift through and find relevant information. To deal with this problem, information retrieval (IR) techniques have gained more intensive attention from both industrial and academic researchers. Numerous IR techniques have been developed to help deal with the information overload problem. These techniques concentrate on mathematical models and algorithms for retrieval. Popular IR models such as the Boolean model, the vector-space model, the probabilistic model and their variants are well established.
From the user's perspective, however, it is still difficult to use current information retrieval systems. Users frequently have problems expressing their information needs and translating those needs into queries. This is partly due to the fact that information needs cannot be expressed appropriately in systems terms. It is not unusual for users to input search terms that are different from the index terms information systems use. Various methods have been proposed to help users choose search terms and articulate queries. One widely used approach is to incorporate into the information system a thesaurus-like component that represents both the important concepts in a particular subject area and the semantic relationships among those concepts. Unfortunately, the development and use of thesauri is not without its own problems. The thesaurus employed in a specific information system has often been developed for a general subject area and needs significant enhancement to be tailored to the information system where it is to be used. This thesaurus development process, if done manually, is both time consuming and labor intensive. Usage of a thesaurus in searching is complex and may raise barriers for the user. For illustration purposes, let us consider two scenarios of thesaurus usage. In the first scenario the user inputs a search term and the thesaurus then displays a matching set of related terms. Without an overview of the thesaurus - and without the ability to see the matching terms in the context of other terms - it may be difficult to assess the quality of the related terms in order to select the correct term. In the second scenario the user browses the whole thesaurus, which is organized as in an alphabetically ordered list. The problem with this approach is that the list may be long, and neither does it show users the global semantic relationship among all the listed terms.
Nevertheless, because thesaurus use has shown to improve retrieval, for our method we integrate functions in the search interface that permit users to explore built-in search vocabularies to improve retrieval from digital libraries. Our method automatically generates the terms and their semantic relationships representing relevant topics covered in a digital library. We call these generated terms the "concepts", and the generated terms and their semantic relationships we call the "concept space". Additionally, we used a visualization technique to display the concept space and allow users to interact with this space. The automatically generated term set is considered to be more representative of subject area in a corpus than an "externally" imposed thesaurus, and our method has the potential of saving a significant amount of time and labor for those who have been manually creating thesauri as well. Information visualization is an emerging discipline and developed very quickly in the last decade. With growing volumes of documents and associated complexities, information visualization has become increasingly important. Researchers have found information visualization to be an effective way to use and understand information while minimizing a user's cognitive load. Our work was based on an algorithmic approach of concept discovery and association. Concepts are discovered using an algorithm based on an automated thesaurus generation procedure. Subsequently, similarities among terms are computed using the cosine measure, and the associations among terms are established using a method known as max-min distance clustering. The concept space is then visualized in a spring embedding graph, which roughly shows the semantic relationships among concepts in a 2-D visual representation. The semantic space of the visualization is used as a medium for users to retrieve the desired documents. In the remainder of this article, we present our algorithmic approach of concept generation and clustering, followed by description of the visualization technique and interactive interface. The paper ends with key conclusions and discussions on future work.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Abstract

The reader of a hypertext document in a web environment, if maximum use of the document is to be obtained, must visualize the overall structure of the paths through the document as well as the document space. Graphic visualization displays of this space, produced to assist in navigation, are classified into four groups, and Heo and Hirtle compare three of these classes as to their effectiveness. Distortion displays expand regions of interest while relatively diminishing the detail of the remaining regions. This technique will show both local detail and global structure. Zoom techniques use a series of increasingly focused displays of smaller and smaller areas, and can reduce cogitative overload, but do not provide an easy movement to other parts of the total space. Expanding outline displays use a tree structure to allow movement through a hierarchy of documents, but if the organization has a wide horizontal structure, or is not particularly hierarchical in nature such display can break down. Three dimensional layouts, which are not evaluated here, place objects by location in three space, providing more information and freedom. However, the space must be represented in two dimensions resulting in difficulty in visually judging depth, size and positioning. Ten students were assigned to each of eight groups composed of viewers of the three techniques and an unassisted control group using either a large (583 selected pages) or a small (50 selected pages) web space. Sets of 10 questions, which were designed to elicit the use of a visualization tool, were provided for each space. Accuracy and time spent were extracted from a log file. Users views were also surveyed after completion. ANOVA shows significant differences in accuracy and time based upon the visualization tool in use. A Tukey test shows zoom accuracy to be significantly less than expanding outline and zoom time to be significantly greater than both the outline and control groups. Size significantly affected accuracy and time, but had no interaction with tool type. While the expanding tool class out performed zoom and distortion, its performance was not significantly different from the control group.

Source

Journal of the American Society for Information Science and technology. 52(2001) no.8, S.666-675

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Abstract

Although information retrieval techniques used by Web search engines have improved substantially over the years, the results of Web searches have continued to be represented in simple list-based formats. Although the list-based representation makes it easy to evaluate a single document for relevance, it does not support the users in the broader tasks of manipulating or exploring the search results as they attempt to find a collection of relevant documents. HotMap is a meta-search system that provides a compact visual representation of Web search results at two levels of detail, and it supports interactive exploration via nested sorting of Web search results based on query term frequencies. An evaluation of the search results for a set of vague queries has shown that the re-sorted search results can provide a higher portion of relevant documents among the top search results. User studies show an increase in speed and effectiveness and a reduction in missed documents when comparing HotMap to the list-based representation used by Google. Subjective measures were positive, and users showed a preference for the HotMap interface. These results provide evidence for the utility of next-generation Web search results interfaces that promote interactive search results exploration.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.1, S.90-110

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Abstract

Science mapping aims to build bibliometric maps that describe how specific disciplines, scientific domains, or research fields are conceptually, intellectually, and socially structured. Different techniques and software tools have been proposed to carry out science mapping analysis. The aim of this article is to review, analyze, and compare some of these software tools, taking into account aspects such as the bibliometric techniques available and the different kinds of analysis.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.7, S.1382-1402

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Source

Information processing and management. 37(2001) no.4, S.639-657

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Source

Interactive information seeking, behaviour and retrieval. Eds.: Ruthven, I. u. D. Kelly

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Abstract

Semantische Netze unterstützen den Suchvorgang im Information Retrieval. Sie bestehen aus relationierten Begriffen und helfen dem Nutzer das richtige Vokabular zur Fragebildung zu finden. Eine leicht und intuitiv erfassbare Darstellung und eine interaktive Bedienungsmöglichkeit optimieren den Suchprozess mit der Begriffsstruktur. Als Interaktionsform bietet sich Hy-pertext mit dem etablierte Point- und Klickverfahren an. Eine Visualisierung zur Unterstützung kognitiver Fähigkeiten kann durch eine Darstellung der Informationen mit Hilfe von Punkten und Linien erfolgen. Vorgestellt wer-den die Anwendungsbeispiele Wissensnetz im Brockhaus multimedial, WordSurfer der Firma BiblioMondo, SpiderSearch der Firma BOND und Topic Maps Visualization in dandelon.com und im Portal Informationswis-senschaft der Firma AGI - Information Management Consultants.

Date

30. 1.2007 18:22:41

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Abstract

This paper seeks to provide a brief overview of those developments which have taken the theory and practice of image and video retrieval into the digital age. Drawing on a voluminous literature, the context in which visual information retrieval takes place is followed by a consideration of the conceptual and practical challenges posed by the representation and recovery of visual material on the basis of its semantic content. An historical account of research endeavours in content-based retrieval, directed towards the automation of these operations in digital image scenarios, provides the main thrust of the paper. Finally, a look forwards locates visual information retrieval research within the wider context of content-based multimedia retrieval.

Source

Information science in transition, Ed.: A. Gilchrist

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Abstract

The goal of information visualization (IV) is to amplify human cognition through computer-generated, interactive, and visual data representation. By combining the computational power with human perceptional and associative capabilities, IV will make it easier for users to navigate through large amounts of information, discover patterns or hidden structures of the information, and understand semantics of the information space. This entry reviews the history and background of IV and discusses its basic principles with pointers to relevant resources. The entry also summarizes major IV techniques and toolkits and shows various examples of IV applications.

Source

Encyclopedia of library and information sciences. 3rd ed. Ed.: M.J. Bates

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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.

Source

Annual review of information science and technology. 39(2005), S.139-177

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Abstract

Interface designs for lexical databases in NLP have suffered from not following design principles developed in the information visualization research community. We present a design paradigm and show it can be used to generate visualizations which maximize the usability and utility ofWordNet. The techniques can be generally applied to other lexical databases used in NLP research.