Search (38 results, page 1 of 2)

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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."

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Abstract

The Library of Congress Subject Headings (LCSH) has been developed over the course of more than a century, predating the semantic web by some time. Until the 1986, the only concept-toconcept relationship available was an undifferentiated "See Also" reference, which was used for both associative (RT) and hierarchical (BT/NT) connections. In that year, in preparation for the first release of the headings in machine readable MARC Authorities form, an attempt was made to automatically convert these "See Also" links into the standardized thesaural relations. Unfortunately, the rule used to determine the type of reference to generate relied on the presence of symmetric links to detect associatively related terms; "See Also" references that were only present in one of the related terms were assumed to be hierarchical. This left the process vulnerable to inconsistent use of references in the pre-conversion data, with a marked bias towards promoting relationships to hierarchical status. The Library of Congress was aware that the results of the conversion contained many inconsistencies, and intended to validate and correct the results over the course of time. Unfortunately, twenty years later, less than 40% of the converted records have been evaluated. The converted records, being the earliest encountered during the Library's cataloging activities, represent the most basic concepts within LCSH; errors in the syndetic structure for these records affect far more subordinate concepts than those nearer the periphery. Worse, a policy of patterning new headings after pre-existing ones leads to structural errors arising from the conversion process being replicated in these newer headings, perpetuating and exacerbating the errors. As the LCSH prepares for its second great conversion, from MARC to SKOS, it is critical to address these structural problems. As part of the work on converting the headings into SKOS, I have experimented with different visualizations of the tangled web of broader terms embedded in LCSH. This poster illustrates several of these renderings, shows how they can help users to judge which relationships might not be correct, and shows just exactly how Doorbells and Mammals are related.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

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Abstract

This study investigated how people comprehend three-dimensional (3D) visualizations and what properties of such visualizations affect comprehension. Participants were asked to draw the face of a 3D visualization after it was cut in half. We videotaped the participants as they drew, erased, verbalized their thoughts, gestured, and moved about a two-dimensional paper presentation of the 3D visualization. The videorecords were analyzed using a grounded theory approach to generate hypotheses related to comprehension difficulties and visualization properties. Our analysis of the results uncovered three properties that made problem solving more difficult for participants. These were: (a) cuts that were at an angle in relation to at least one plane of reference, (b) nonplanar properties of the features contained in the 3D visualizations including curved layers and v-shaped layers, and (c) mixed combinations of layers. In contrast, (a) cutting planes that were perpendicular or parallel to the 3D visualization diagram's planes of reference, (b) internal features that were flat/planar, and (c) homogeneous layers were easier to comprehend. This research has direct implications for the generation and use of 3D information visualizations in that it suggests design features to include and avoid.

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Abstract

Describes the use of concept maps as one of the semantic tools employed in the ADEPT (Alexandria Digital Earth Prototype) Digital Learning Environment (DLE) for teaching undergraduate classes. The graphic representation of the conceptualizations is derived from the knowledge in stronglystructured models (SSMs) of concepts represented in one or more knowledge bases. Such knowledge bases function as a source of "reference" information about concepts in a given context, including information about their scientific representation, scientific semantics, manipulation, and interrelationships to other concepts.

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Abstract

The importance of information visualization as a means of transforming data into visual, understandable form is now embraced across university campuses and research institutes world-wide. Yet, the role of designers in this field of activity is often overlooked by the dominant scientific and technological interests in data visualization, and a corporate culture reliant on off-the-shelf visualization tools. This article is an attempt to describe the value of design thinking in information visualization with reference to Horst Rittel's ([1988]) definition of disorderly reasoning, and to frame design as a critical act of translating between scientific, technical, and aesthetic interests.

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Object

IFLA Library Reference Model

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Content

Traditional print reference guides often have two methods of finding information: an order (alphabetical for dictionaries and encyclopedias, by subject hierarchy in the case of thesauri) and indices (ordered lists, with a more complete listing of words and concepts, which refers back to original content from the main body of the book). A user of such traditional print reference guides who is looking for information will either browse through the ordered information in the main body of the reference book, or scan through the indices to find what is necessary. The advent of the computer allows for much more rapid electronic searches of the same information, and for multiple layers of indices. Users can either search through information by entering a keyword, or users can browse through the information through an outline index, which represents the information contained in the main body of the data. There are two traditional user interfaces for such applications. First, the user may type text into a search field and in response, a list of results is returned to the user. The user then selects a returned entry and may page through the resulting information. Alternatively, the user may choose from a list of words from an index. For example, software thesaurus applications, in which a user attempts to find synonyms, antonyms, homonyms, etc. for a selected word, are usually implemented using the conventional search and presentation techniques discussed above. The presentation of results only allows for a one-dimensional order of data at any one time. In addition, only a limited number of results can be shown at once, and selecting a result inevitably leads to another page-if the result is not satisfactory, the users must search again. Finally, it is difficult to present information about the manner in which the search results are related, or to present quantitative information about the results without causing confusion. Therefore, there exists a need for a multidimensional graphical display of information, in particular with respect to information relating to the meaning of words and their relationships to other words. There further exists a need to present large amounts of information in a way that can be manipulated by the user, without the user losing his place. And there exists a need for more fluid, intuitive and powerful thesaurus functionality that invites the exploration of language.

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Abstract

UDC aims to be a consistent and complete classification system, that enables practitioners to classify documents swiftly and smoothly. The eventual goal of UDC is to enable the public at large to retrieve documents from large collections of documents that are classified with UDC. The large size of the UDC Master Reference File, MRF with over 66.000 records, makes it difficult to obtain an overview and to understand its structure. Moreover, finding the right classification in MRF turns out to be difficult in practice. Last but not least, retrieval of documents requires insight and understanding of the coding system. Visualization is an effective means to support the development of UDC as well as its use by practitioners. Moreover, visualization offers possibilities to use the classification without use of the coding system as such. MagnaView has developed an application which demonstrates the use of interactive visualization to face these challenges. In our presentation, we discuss these challenges, and we give a demonstration of the way the application helps face these. Examples of visualizations can be found below.

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Abstract

The idea of constructing science maps based on bibliographic data has intrigued researchers for decades, and various techniques have been developed to map the structure of research disciplines. Most science mapping studies use a single method. However, as research fields have various properties, a valid map of a field should actually be composed of a set of maps derived from a series of investigations using different methods. That leads to the question of what can be learned from a combination-triangulation-of these different science maps. In this paper we propose a method for triangulation, using the example of water science. We combine three different mapping approaches: journal-journal citation relations (JJCR), shared author keywords (SAK), and title word-cited reference co-occurrence (TWRC). Our results demonstrate that triangulation of JJCR, SAK, and TWRC produces a more comprehensive picture than each method applied individually. The outcomes from the three different approaches can be associated with each other and systematically interpreted to provide insights into the complex multidisciplinary structure of the field of water research.

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Abstract

In this article, we propose a graph-based interactive bibliographic information retrieval system-GIBIR. GIBIR provides an effective way to retrieve bibliographic information. The system represents bibliographic information as networks and provides a form-based query interface. Users can develop their queries interactively by referencing the system-generated graph queries. Complex queries such as "papers on information retrieval, which were cited by John's papers that had been presented in SIGIR" can be effectively answered by the system. We evaluate the proposed system by developing another relational database-based bibliographic information retrieval system with the same interface and functions. Experiment results show that the proposed system executes the same queries much faster than the relational database-based system, and on average, our system reduced the execution time by 72% (for 3-node query), 89% (for 4-node query), and 99% (for 5-node query).

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Abstract

Purpose The study aims to paint a representative picture of the current state of search interfaces of Swedish online museum collections, focussing on search functionalities with particular reference to subject searching, as well as the use of controlled vocabularies, with the purpose of identifying which improvements of the search interfaces are needed to ensure high-quality information retrieval for the end user. Design/methodology/approach In the first step, a set of 21 search interface criteria was identified, based on related research and current standards in the domain of cultural heritage knowledge organization. Secondly, a complete set of Swedish museums that provide online access to their collections was identified, comprising nine cross-search services and 91 individual museums' websites. These 100 websites were each evaluated against the 21 criteria, between 1 July and 31 August 2020. Findings Although many standards and guidelines are in place to ensure quality-controlled subject indexing, which in turn support information retrieval of relevant resources (as individual or full search results), the study shows that they are not broadly implemented, resulting in information retrieval failures for the end user. The study also demonstrates a strong need for the implementation of controlled vocabularies in these museums. Originality/value This study is a rare piece of research which examines subject searching in online museums; the 21 search criteria and their use in the analysis of the complete set of online collections of a country represents a considerable and unique contribution to the fields of knowledge organization and information retrieval of cultural heritage. Its particular value lies in showing how the needs of end users, many of which are documented and reflected in international standards and guidelines, should be taken into account in designing search tools for these museums; especially so in subject searching, which is the most complex and yet the most common type of search. Much effort has been invested into digitizing cultural heritage collections, but access to them is hindered by poor search functionality. This study identifies which are the most important aspects to improve.

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Date

30. 5.2010 16:22:35

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Date

1. 2.2016 18:25:22

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Abstract

The DeweyBrowser allows users to search and browse collections of library resources organized by the Dewey Decimal Classification (DDC) system. The visual interface provides access to several million records from the OCLC WorldCat database and to a collection of records derived from the abridged edition of DDC. The prototype was developed out of a desire to make the most of Dewey numbers assigned to library materials and to explore new ways of providing access to the DDC.

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Abstract

This study explores an image-based retrieval interface for drug information, focusing on usability for a specific population - seniors. Qualitative, task-based interviews examined participants' health information behaviors and documented search strategies using an existing database (www.drugs.com) and a new prototype that uses similarity-based clustering of pill images for retrieval. Twelve participants (aged 65 and older), reflecting a diversity of backgrounds and experience with Web-based resources, located pill information using the interfaces and discussed navigational and other search preferences. Findings point to design features (e.g., image enlargement) that meet seniors' needs in the context of other health-related information-seeking strategies (e.g., contacting pharmacists).

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Date

22. 1.2017 16:54:03
22. 1.2017 17:10:56

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Abstract

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.

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Abstract

We propose an approach to visualizing the scientific world and its evolution by constructing minimum spanning trees (MSTs) and a two-dimensional map of scientific journals using the database of the Science Citation Index (SCI) during 1994-2001. The structures of constructed MSTs are consistent with the sorting of SCI categories. The map of science is constructed based on our MST results. Such a map shows the relation among various knowledge clusters and their citation properties. The temporal evolution of the scientific world can also be delineated in the map. In particular, this map clearly shows a linear structure of the scientific world, which contains three major domains including physical sciences, life sciences, and medical sciences. The interaction of various knowledge fields can be clearly seen from this scientific world map. This approach can be applied to various levels of knowledge domains.

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Abstract

This study proposes an approach for visualizing knowledge structures that creates a "research-focused parallelship network," "keyword co-occurrence network," and a knowledge map to visualize Sci-Tech policy research structure. A total of 1,125 Sci-Tech policy-related papers (873 journal papers [78%], 205 conference papers [18%], and 47 review papers [4%]) have been retrieved from the Web of Science database for quantitative analysis and mapping. Different network and contour maps based on these 1,125 papers can be constructed by choosing different information as the main actor, such as the paper title, the institute, the country, or the author keywords, to reflect Sci-Tech policy research structures in micro-, meso-, and macro-levels, respectively. The quantitative way of exploring Sci-Tech policy research papers is investigated to unveil important or emerging Sci-Tech policy implications as well as to demonstrate the dynamics and visualization of the evolution of Sci-Tech policy research.