Search (59 results, page 1 of 3)

0.050138313 = product of:
  0.1128112 = sum of:
    0.029363085 = weight(_text_:applications in 4276) [ClassicSimilarity], result of:
      0.029363085 = score(doc=4276,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.17024462 = fieldWeight in 4276, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.02734375 = fieldNorm(doc=4276)
    0.02191663 = weight(_text_:of in 4276) [ClassicSimilarity], result of:
      0.02191663 = score(doc=4276,freq=70.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.35774738 = fieldWeight in 4276, product of:
          8.3666 = tf(freq=70.0), with freq of:
            70.0 = termFreq=70.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.02734375 = fieldNorm(doc=4276)
    0.020234404 = weight(_text_:systems in 4276) [ClassicSimilarity], result of:
      0.020234404 = score(doc=4276,freq=4.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.16806422 = fieldWeight in 4276, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.02734375 = fieldNorm(doc=4276)
    0.041297078 = weight(_text_:software in 4276) [ClassicSimilarity], result of:
      0.041297078 = score(doc=4276,freq=6.0), product of:
        0.15541996 = queryWeight, product of:
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.03917671 = queryNorm
        0.26571283 = fieldWeight in 4276, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.02734375 = fieldNorm(doc=4276)
  0.44444445 = coord(4/9)

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

0.048823856 = product of:
  0.14647156 = sum of:
    0.08718575 = weight(_text_:applications in 1247) [ClassicSimilarity], result of:
      0.08718575 = score(doc=1247,freq=6.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.5054954 = fieldWeight in 1247, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.046875 = fieldNorm(doc=1247)
    0.016802425 = weight(_text_:of in 1247) [ClassicSimilarity], result of:
      0.016802425 = score(doc=1247,freq=14.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.2742677 = fieldWeight in 1247, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1247)
    0.042483397 = weight(_text_:systems in 1247) [ClassicSimilarity], result of:
      0.042483397 = score(doc=1247,freq=6.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.35286134 = fieldWeight in 1247, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.046875 = fieldNorm(doc=1247)
  0.33333334 = coord(3/9)

Abstract

In this era of the Internet and distributed, multimedia computing, new and emerging classes of information systems applications have swept into the lives of office workers and people in general. From digital libraries, multimedia systems, geographic information systems, and collaborative computing to electronic commerce, virtual reality, and electronic video arts and games, these applications have created tremendous opportunities for information and computer science researchers and practitioners. As applications become more pervasive, pressing, and diverse, several well-known information retrieval (IR) problems have become even more urgent. Information overload, a result of the ease of information creation and transmission via the Internet and WWW, has become more troublesome (e.g., even stockbrokers and elementary school students, heavily exposed to various WWW search engines, are versed in such IR terminology as recall and precision). Significant variations in database formats and structures, the richness of information media (text, audio, and video), and an abundance of multilingual information content also have created severe information interoperability problems -- structural interoperability, media interoperability, and multilingual interoperability.

0.04361126 = product of:
  0.09812533 = sum of:
    0.041947264 = weight(_text_:applications in 7469) [ClassicSimilarity], result of:
      0.041947264 = score(doc=7469,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.2432066 = fieldWeight in 7469, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.0390625 = fieldNorm(doc=7469)
    0.0140020205 = weight(_text_:of in 7469) [ClassicSimilarity], result of:
      0.0140020205 = score(doc=7469,freq=14.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.22855641 = fieldWeight in 7469, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=7469)
    0.02890629 = weight(_text_:systems in 7469) [ClassicSimilarity], result of:
      0.02890629 = score(doc=7469,freq=4.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.24009174 = fieldWeight in 7469, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=7469)
    0.013269759 = product of:
      0.026539518 = sum of:
        0.026539518 = weight(_text_:22 in 7469) [ClassicSimilarity], result of:
          0.026539518 = score(doc=7469,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = queryNorm
            0.19345059 = fieldWeight in 7469, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=7469)
      0.5 = coord(1/2)
  0.44444445 = coord(4/9)

Abstract

With the growth of hypertext and multimedia applications that support and encourage browsing it is time to take a penetrating look at browsing behaviour. Several dimensions of browsing are exemined, to find out: first, what is browsing and what cognitive processes are associated with it: second, is there a browsing strategy, and if so, are there any differences between how subject-area experts and novices browse; and finally, how can this knowledge be applied to improve the design of hypertext systems. Two groups of students, subject-area experts and novices, were studied while browsing a Macintosh HyperCard application on the subject The Vietnam War. A protocol analysis technique was used to gather and analyze data. Components of the GOMS model were used to describe the goals, operators, methods, and selection rules observed: Three browsing strategies were identified: (1) search-oriented browse, scanning and and reviewing information relevant to a fixed task; (2) review-browse, scanning and reviewing intersting information in the presence of transient browse goals that represent changing tasks, and (3) scan-browse, scanning for interesting information (without review). Most subjects primarily used review-browse interspersed with search-oriented browse. Within this strategy, comparisons between subject-area experts and novices revealed differences in tactics: experts browsed in more depth, seldom used referential links, selected different kinds of topics, and viewed information differently thatn did novices. Based on these findings, suggestions are made to hypertext developers

Source

IEEE transactions on systems, man and cybernetics. 22(1992) no.5, S.865-884

0.033354186 = product of:
  0.10006255 = sum of:
    0.059322387 = weight(_text_:applications in 5054) [ClassicSimilarity], result of:
      0.059322387 = score(doc=5054,freq=4.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.34394607 = fieldWeight in 5054, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5054)
    0.011833867 = weight(_text_:of in 5054) [ClassicSimilarity], result of:
      0.011833867 = score(doc=5054,freq=10.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.19316542 = fieldWeight in 5054, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5054)
    0.02890629 = weight(_text_:systems in 5054) [ClassicSimilarity], result of:
      0.02890629 = score(doc=5054,freq=4.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.24009174 = fieldWeight in 5054, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5054)
  0.33333334 = coord(3/9)

Abstract

As increasing numbers of non-English resources have become available on the Web, the interesting and important issue of how Web users can retrieve documents in different languages has arisen. Cross-language information retrieval (CLIP), the study of retrieving information in one language by queries expressed in another language, is a promising approach to the problem. Cross-language information retrieval has attracted much attention in recent years. Most research systems have achieved satisfactory performance on standard Text REtrieval Conference (TREC) collections such as news articles, but CLIR techniques have not been widely studied and evaluated for applications such as Web portals. In this article, the authors present their research in developing and evaluating a multilingual English-Chinese Web portal that incorporates various CLIP techniques for use in the business domain. A dictionary-based approach was adopted and combines phrasal translation, co-occurrence analysis, and pre- and posttranslation query expansion. The portal was evaluated by domain experts, using a set of queries in both English and Chinese. The experimental results showed that co-occurrence-based phrasal translation achieved a 74.6% improvement in precision over simple word-byword translation. When used together, pre- and posttranslation query expansion improved the performance slightly, achieving a 78.0% improvement over the baseline word-by-word translation approach. In general, applying CLIR techniques in Web applications shows promise.

Footnote

Beitrag einer special topic section on multilingual information systems

Source

Journal of the American Society for Information Science and Technology. 57(2006) no.5, S.671-683

0.02546304 = product of:
  0.07638912 = sum of:
    0.041947264 = weight(_text_:applications in 3325) [ClassicSimilarity], result of:
      0.041947264 = score(doc=3325,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.2432066 = fieldWeight in 3325, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3325)
    0.0140020205 = weight(_text_:of in 3325) [ClassicSimilarity], result of:
      0.0140020205 = score(doc=3325,freq=14.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.22855641 = fieldWeight in 3325, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3325)
    0.020439833 = weight(_text_:systems in 3325) [ClassicSimilarity], result of:
      0.020439833 = score(doc=3325,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.1697705 = fieldWeight in 3325, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3325)
  0.33333334 = coord(3/9)

Abstract

It is important for education in computer science and information systems to keep up to date with the latest development in technology. With the rapid development of the Internet and the Web, many schools have included Internet-related technologies, such as Web search engines and e-commerce, as part of their curricula. Previous research has shown that it is effective to use search engine development tools to facilitate students' learning. However, the effectiveness of these tools in the classroom has not been evaluated. In this article, we review the design of three search engine development tools, SpidersRUs, Greenstone, and Alkaline, followed by an evaluation study that compared the three tools in the classroom. In the study, 33 students were divided into 13 groups and each group used the three tools to develop three independent search engines in a class project. Our evaluation results showed that SpidersRUs performed better than the two other tools in overall satisfaction and the level of knowledge gained in their learning experience when using the tools for a class project on Internet applications development.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.2, S.288-299

0.02546203 = product of:
  0.07638609 = sum of:
    0.041947264 = weight(_text_:applications in 5276) [ClassicSimilarity], result of:
      0.041947264 = score(doc=5276,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.2432066 = fieldWeight in 5276, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5276)
    0.021169065 = weight(_text_:of in 5276) [ClassicSimilarity], result of:
      0.021169065 = score(doc=5276,freq=32.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.34554482 = fieldWeight in 5276, product of:
          5.656854 = tf(freq=32.0), with freq of:
            32.0 = termFreq=32.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5276)
    0.013269759 = product of:
      0.026539518 = sum of:
        0.026539518 = weight(_text_:22 in 5276) [ClassicSimilarity], result of:
          0.026539518 = score(doc=5276,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = queryNorm
            0.19345059 = fieldWeight in 5276, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5276)
      0.5 = coord(1/2)
  0.33333334 = coord(3/9)

Abstract

With the rapid proliferation of Internet technologies and applications, misuse of online messages for inappropriate or illegal purposes has become a major concern for society. The anonymous nature of online-message distribution makes identity tracing a critical problem. We developed a framework for authorship identification of online messages to address the identity-tracing problem. In this framework, four types of writing-style features (lexical, syntactic, structural, and content-specific features) are extracted and inductive learning algorithms are used to build feature-based classification models to identify authorship of online messages. To examine this framework, we conducted experiments on English and Chinese online-newsgroup messages. We compared the discriminating power of the four types of features and of three classification techniques: decision trees, backpropagation neural networks, and support vector machines. The experimental results showed that the proposed approach was able to identify authors of online messages with satisfactory accuracy of 70 to 95%. All four types of message features contributed to discriminating authors of online messages. Support vector machines outperformed the other two classification techniques in our experiments. The high performance we achieved for both the English and Chinese datasets showed the potential of this approach in a multiple-language context.

Date

22. 7.2006 16:14:37

Source

Journal of the American Society for Information Science and Technology. 57(2006) no.3, S.378-393

0.01981098 = product of:
  0.08914941 = sum of:
    0.07118686 = weight(_text_:applications in 4242) [ClassicSimilarity], result of:
      0.07118686 = score(doc=4242,freq=4.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.41273528 = fieldWeight in 4242, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.046875 = fieldNorm(doc=4242)
    0.017962547 = weight(_text_:of in 4242) [ClassicSimilarity], result of:
      0.017962547 = score(doc=4242,freq=16.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.2932045 = fieldWeight in 4242, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=4242)
  0.22222222 = coord(2/9)

Abstract

With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich knowledge base. The knowledge comes not only from the content of the pages themselves, but also from the unique characteristics of the Web, such as its hyperlink structure and its diversity of content and languages. Analysis of these characteristics often reveals interesting patterns and new knowledge. Such knowledge can be used to improve users' efficiency and effectiveness in searching for information an the Web, and also for applications unrelated to the Web, such as support for decision making or business management. The Web's size and its unstructured and dynamic content, as well as its multilingual nature, make the extraction of useful knowledge a challenging research problem. Furthermore, the Web generates a large amount of data in other formats that contain valuable information. For example, Web server logs' information about user access patterns can be used for information personalization or improving Web page design.

Source

Annual review of information science and technology. 38(2004), S.289-330

0.013399933 = product of:
  0.0602997 = sum of:
    0.01737605 = weight(_text_:of in 3232) [ClassicSimilarity], result of:
      0.01737605 = score(doc=3232,freq=44.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.28363106 = fieldWeight in 3232, product of:
          6.6332498 = tf(freq=44.0), with freq of:
            44.0 = termFreq=44.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.02734375 = fieldNorm(doc=3232)
    0.042923648 = weight(_text_:systems in 3232) [ClassicSimilarity], result of:
      0.042923648 = score(doc=3232,freq=18.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.35651803 = fieldWeight in 3232, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.02734375 = fieldNorm(doc=3232)
  0.22222222 = coord(2/9)

Abstract

Making the Nation Safer: The Role of Science and Technology in Countering Terrorism The commitment of the scientific, engineering, and health communities to helping the United States and the world respond to security challenges became evident after September 11, 2001. The U.S. National Research Council's report an "Making the Nation Safer: The Role of Science and Technology in Countering Terrorism," (National Research Council, 2002, p. 1) explains the context of such a new commitment: Terrorism is a serious threat to the Security of the United States and indeed the world. The vulnerability of societies to terrorist attacks results in part from the proliferation of chemical, biological, and nuclear weapons of mass destruction, but it also is a consequence of the highly efficient and interconnected systems that we rely an for key services such as transportation, information, energy, and health care. The efficient functioning of these systems reflects great technological achievements of the past century, but interconnectedness within and across systems also means that infrastructures are vulnerable to local disruptions, which could lead to widespread or catastrophic failures. As terrorists seek to exploit these vulnerabilities, it is fitting that we harness the nation's exceptional scientific and technological capabilities to Counter terrorist threats. A committee of 24 of the leading scientific, engineering, medical, and policy experts in the United States conducted the study described in the report. Eight panels were separately appointed and asked to provide input to the committee. The panels included: (a) biological sciences, (b) chemical issues, (c) nuclear and radiological issues, (d) information technology, (e) transportation, (f) energy facilities, Cities, and fixed infrastructure, (g) behavioral, social, and institutional issues, and (h) systems analysis and systems engineering. The focus of the committee's work was to make the nation safer from emerging terrorist threats that sought to inflict catastrophic damage an the nation's people, its infrastructure, or its economy. The committee considered nine areas, each of which is discussed in a separate chapter in the report: nuclear and radiological materials, human and agricultural health systems, toxic chemicals and explosive materials, information technology, energy systems, transportation systems, Cities and fixed infrastructure, the response of people to terrorism, and complex and interdependent systems. The chapter an information technology (IT) is particularly relevant to this special issue. The report recommends that "a strategic long-term research and development agenda should be established to address three primary counterterrorismrelated areas in IT: information and network security, the IT needs of emergency responders, and information fusion and management" (National Research Council, 2002, pp. 11 -12). The MD in information and network security should include approaches and architectures for prevention, identification, and containment of cyber-intrusions and recovery from them. The R&D to address IT needs of emergency responders should include ensuring interoperability, maintaining and expanding communications capability during an emergency, communicating with the public during an emergency, and providing support for decision makers. The R&D in information fusion and management for the intelligence, law enforcement, and emergency response communities should include data mining, data integration, language technologies, and processing of image and audio data. Much of the research reported in this special issue is related to information fusion and management for homeland security.

Source

Journal of the American Society for Information Science and Technology. 56(2005) no.3, S.217-220

0.012433175 = product of:
  0.055949286 = sum of:
    0.041947264 = weight(_text_:applications in 2386) [ClassicSimilarity], result of:
      0.041947264 = score(doc=2386,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.2432066 = fieldWeight in 2386, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2386)
    0.0140020205 = weight(_text_:of in 2386) [ClassicSimilarity], result of:
      0.0140020205 = score(doc=2386,freq=14.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.22855641 = fieldWeight in 2386, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2386)
  0.22222222 = coord(2/9)

Abstract

Effectively harnessing available data to support homeland-security-related applications is a major focus in the emerging science of intelligence and security informatics (ISI). Many studies have focused on criminal-network analysis as a major challenge within the ISI domain. Though various methodologies have been proposed, none have been tested for usefulness in creating link charts. This study compares manually created link charts to suggestions made by the proposed importance-flooding algorithm. Mirroring manual investigational processes, our iterative computation employs association-strength metrics, incorporates path-based node importance heuristics, allows for case-specific notions of importance, and adjusts based on the accuracy of previous suggestions. Interesting items are identified by leveraging both node attributes and network structure in a single computation. Our data set was systematically constructed from heterogeneous sources and omits many privacy-sensitive data elements such as case narratives and phone numbers. The flooding algorithm improved on both manual and link-weight-only computations, and our results suggest that the approach is robust across different interpretations of the user-provided heuristics. This study demonstrates an interesting methodology for including user-provided heuristics in network-based analysis, and can help guide the development of ISI-related analysis tools.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.13, S.2099-2114

0.011926386 = product of:
  0.053668734 = sum of:
    0.019949809 = weight(_text_:of in 5460) [ClassicSimilarity], result of:
      0.019949809 = score(doc=5460,freq=58.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.32564276 = fieldWeight in 5460, product of:
          7.615773 = tf(freq=58.0), with freq of:
            58.0 = termFreq=58.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.02734375 = fieldNorm(doc=5460)
    0.033718925 = weight(_text_:software in 5460) [ClassicSimilarity], result of:
      0.033718925 = score(doc=5460,freq=4.0), product of:
        0.15541996 = queryWeight, product of:
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.03917671 = queryNorm
        0.21695362 = fieldWeight in 5460, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.02734375 = fieldNorm(doc=5460)
  0.22222222 = coord(2/9)

Abstract

The purpose of this paper is to identify and explain the role of individual learning and development in acquiring tacit knowledge in the context of the inexorable and intense continuous change (technological and otherwise) that characterizes our society today, and also to investigate the software (SW) sector, which is at the core of contemporary continuous change and is a paradigm of effective and intrinsic knowledge sharing (KS). This makes the SW sector unique and different from others where KS is so hard to implement. Design/methodology/approach The study employed an inductive qualitative approach based on a multi-case study approach, composed of three successful SW companies in China. These companies are representative of the fabric of the sector, namely a small- and medium-sized enterprise, a large private company and a large state-owned enterprise. The fieldwork included 44 participants who were interviewed using a semi-structured script. The interview data were coded and interpreted following the Straussian grounded theory pattern of open coding, axial coding and selective coding. The process of interviewing was stopped when theoretical saturation was achieved after a careful process of theoretical sampling.
Findings The findings of this research suggest that individual learning and development are deemed to be the fundamental feature for professional success and survival in the continuously changing environment of the SW industry today. However, individual learning was described by the participants as much more than a mere individual process. It involves a collective and participatory effort within the organization and the sector as a whole, and a KS process that transcends organizational, cultural and national borders. Individuals in particular are mostly motivated by the pressing need to face and adapt to the dynamic and changeable environments of today's digital society that is led by the sector. Software practitioners are continuously in need of learning, refreshing and accumulating tacit knowledge, partly because it is required by their companies, but also due to a sound awareness of continuous technical and technological changes that seem only to increase with the advances of information technology. This led to a clear theoretical understanding that the continuous change that faces the sector has led to individual acquisition of culture and somatic knowledge that in turn lay the foundation for not only the awareness of the need for continuous individual professional development but also for the creation of habitus related to KS and continuous learning. Originality/value The study reported in this paper shows that there is a theoretical link between the existence of conducive organizational and sector-wide somatic and cultural knowledge, and the success of KS practices that lead to individual learning and development. Therefore, the theory proposed suggests that somatic and cultural knowledge are crucial drivers for the creation of habitus of individual tacit knowledge acquisition. The paper further proposes a habitus-driven individual development (HDID) Theoretical Model that can be of use to both academics and practitioners interested in fostering and developing processes of KS and individual development in knowledge-intensive organizations.

Source

Journal of documentation. 75(2019) no.5, S.927-953

0.010152737 = product of:
  0.045687314 = sum of:
    0.010999769 = weight(_text_:of in 1610) [ClassicSimilarity], result of:
      0.010999769 = score(doc=1610,freq=6.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.17955035 = fieldWeight in 1610, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1610)
    0.034687545 = weight(_text_:systems in 1610) [ClassicSimilarity], result of:
      0.034687545 = score(doc=1610,freq=4.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.28811008 = fieldWeight in 1610, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.046875 = fieldNorm(doc=1610)
  0.22222222 = coord(2/9)

Abstract

Research in information retrieval (IR) has advanced significantly in the past few decades. Many tasks, such as indexing and text categorization, can be performed automatically with minimal human effort. Machine learning has played an important role in such automation by learning various patterns such as document topics, text structures, and user interests from examples. In recent years, it has become increasingly difficult to search for useful information an the World Wide Web because of its large size and unstructured nature. Useful information and resources are often hidden in the Web. While machine learning has been successfully applied to traditional IR systems, it poses some new challenges to apply these algorithms to the Web due to its large size, link structure, diversity in content and languages, and dynamic nature. On the other hand, such characteristics of the Web also provide interesting patterns and knowledge that do not present in traditional information retrieval systems.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.7, S.621-624

0.009652025 = product of:
  0.043434113 = sum of:
    0.014818345 = weight(_text_:of in 2657) [ClassicSimilarity], result of:
      0.014818345 = score(doc=2657,freq=8.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.24188137 = fieldWeight in 2657, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2657)
    0.028615767 = weight(_text_:systems in 2657) [ClassicSimilarity], result of:
      0.028615767 = score(doc=2657,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.23767869 = fieldWeight in 2657, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2657)
  0.22222222 = coord(2/9)

Abstract

In the 1980s, knowledge-based techniques also made an impressive contribution to 'intelligent' information retrieval and indexing. More recently, researchers have turned to newer artificial intelligence based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms grounded on diverse paradigms. These have provided great opportunities to enhance the capabilities of current information storage and retrieval systems. Provides an overview of these techniques and presents 3 popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evaluation based genetic algorithms in the context of information retrieval. The techniques are promising in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search and can greatly complement the prevailing full text, keyword based, probabilistic, and knowledge based techniques

Source

Journal of the American Society for Information Science. 46(1995) no.3, S.194-216

0.00953518 = product of:
  0.04290831 = sum of:
    0.0140020205 = weight(_text_:of in 1615) [ClassicSimilarity], result of:
      0.0140020205 = score(doc=1615,freq=14.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.22855641 = fieldWeight in 1615, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1615)
    0.02890629 = weight(_text_:systems in 1615) [ClassicSimilarity], result of:
      0.02890629 = score(doc=1615,freq=4.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.24009174 = fieldWeight in 1615, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1615)
  0.22222222 = coord(2/9)

Abstract

The Medical professionals and researchers need information from reputable sources to accomplish their work. Unfortunately, the Web has a large number of documents that are irrelevant to their work, even those documents that purport to be "medically-related." This paper describes an architecture designed to integrate advanced searching and indexing algorithms, an automatic thesaurus, or "concept space," and Kohonen-based Self-Organizing Map (SOM) technologies to provide searchers with finegrained results. Initial results indicate that these systems provide complementary retrieval functionalities. HelpfulMed not only allows users to search Web pages and other online databases, but also allows them to build searches through the use of an automatic thesaurus and browse a graphical display of medical-related topics. Evaluation results for each of the different components are included. Our spidering algorithm outperformed both breadth-first search and PageRank spiders an a test collection of 100,000 Web pages. The automatically generated thesaurus performed as well as both MeSH and UMLS-systems which require human mediation for currency. Lastly, a variant of the Kohonen SOM was comparable to MeSH terms in perceived cluster precision and significantly better at perceived cluster recall.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.7, S.683-694

0.009184495 = product of:
  0.041330226 = sum of:
    0.016802425 = weight(_text_:of in 3233) [ClassicSimilarity], result of:
      0.016802425 = score(doc=3233,freq=14.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.2742677 = fieldWeight in 3233, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3233)
    0.0245278 = weight(_text_:systems in 3233) [ClassicSimilarity], result of:
      0.0245278 = score(doc=3233,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.2037246 = fieldWeight in 3233, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.046875 = fieldNorm(doc=3233)
  0.22222222 = coord(2/9)

Abstract

The importance of Intelligence and Security Informatics (ISI) has significantly increased with the rapid and largescale migration of local/national security information from physical media to electronic platforms, including the Internet and information systems. Motivated by the significance of ISI in law enforcement (particularly in the digital government context) and the limited investigations of officers' technology-acceptance decisionmaking, we developed and empirically tested a factor model for explaining law-enforcement officers' technology acceptance. Specifically, our empirical examination targeted the COPLINK technology and involved more than 280 police officers. Overall, our model shows a good fit to the data collected and exhibits satisfactory Power for explaining law-enforcement officers' technology acceptance decisions. Our findings have several implications for research and technology management practices in law enforcement, which are also discussed.

Source

Journal of the American Society for Information Science and Technology. 56(2005) no.3, S.235-244

0.009119614 = product of:
  0.041038264 = sum of:
    0.0063507194 = weight(_text_:of in 501) [ClassicSimilarity], result of:
      0.0063507194 = score(doc=501,freq=2.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.103663445 = fieldWeight in 501, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=501)
    0.034687545 = weight(_text_:systems in 501) [ClassicSimilarity], result of:
      0.034687545 = score(doc=501,freq=4.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.28811008 = fieldWeight in 501, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.046875 = fieldNorm(doc=501)
  0.22222222 = coord(2/9)

Abstract

Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customers' preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.

Source

Journal of the American Society for Information Science and technology. 55(2004) no.3, S.259-274

0.009053368 = product of:
  0.040740155 = sum of:
    0.011833867 = weight(_text_:of in 2699) [ClassicSimilarity], result of:
      0.011833867 = score(doc=2699,freq=10.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.19316542 = fieldWeight in 2699, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2699)
    0.02890629 = weight(_text_:systems in 2699) [ClassicSimilarity], result of:
      0.02890629 = score(doc=2699,freq=4.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.24009174 = fieldWeight in 2699, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2699)
  0.22222222 = coord(2/9)

Abstract

As the Web is used increasingly to share and disseminate information, business analysts and managers are challenged to understand stakeholder relationships. Traditional stakeholder theories and frameworks employ a manual approach to analysis and do not scale up to accommodate the rapid growth of the Web. Unfortunately, existing business intelligence (BI) tools lack analysis capability, and research on BI systems is sparse. This research proposes a framework for designing BI systems to identify and to classify stakeholders on the Web, incorporating human knowledge and machine-learned information from Web pages. Based on the framework, we have developed a prototype called Business Stakeholder Analyzer (BSA) that helps managers and analysts to identify and to classify their stakeholders on the Web. Results from our experiment involving algorithm comparison, feature comparison, and a user study showed that the system achieved better within-class accuracies in widespread stakeholder types such as partner/sponsor/supplier and media/reviewer, and was more efficient than human classification. The student and practitioner subjects in our user study strongly agreed that such a system would save analysts' time and help to identify and classify stakeholders. This research contributes to a better understanding of how to integrate information technology with stakeholder theory, and enriches the knowledge base of BI system design.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.1, S.59-74

0.008907516 = product of:
  0.04008382 = sum of:
    0.015556021 = weight(_text_:of in 2203) [ClassicSimilarity], result of:
      0.015556021 = score(doc=2203,freq=12.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.25392252 = fieldWeight in 2203, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2203)
    0.0245278 = weight(_text_:systems in 2203) [ClassicSimilarity], result of:
      0.0245278 = score(doc=2203,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.2037246 = fieldWeight in 2203, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.046875 = fieldNorm(doc=2203)
  0.22222222 = coord(2/9)

Abstract

Presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge based systems and to alleviate the limitation of the manual browsing approach, develops 2 spreading activation based algorithms for concept exploration in large, heterogeneous networks of concepts (eg multiple thesauri). One algorithm, which is based on the symbolic AI paradigma, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The 2nd algorithm, which is absed on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify 'convergent' concepts for some initial queries (a parallel, heuristic search process). Tests these 2 algorithms on a large text-based knowledge network of about 13.000 nodes (terms) and 80.000 directed links in the area of computing technologies

Source

Journal of the American Society for Information Science. 46(1995) no.5, S.348-369

0.008606319 = product of:
  0.03872844 = sum of:
    0.014200641 = weight(_text_:of in 1148) [ClassicSimilarity], result of:
      0.014200641 = score(doc=1148,freq=10.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.23179851 = fieldWeight in 1148, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1148)
    0.0245278 = weight(_text_:systems in 1148) [ClassicSimilarity], result of:
      0.0245278 = score(doc=1148,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.2037246 = fieldWeight in 1148, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.046875 = fieldNorm(doc=1148)
  0.22222222 = coord(2/9)

Abstract

Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to 'intelligent' information retrieval and indexing. More recently, information science researchers have tfurned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems. In this article, we first provide an overview of these newer techniques and their use in information retrieval research. In order to femiliarize readers with the techniques, we present 3 promising methods: the symbolic ID3 algorithm, evolution-based genetic algorithms, and simulated annealing. We discuss their knowledge representations and algorithms in the unique context of information retrieval

Source

Journal of the American Society for Information Science. 49(1998) no.8, S.693-705

0.0084427325 = product of:
  0.037992295 = sum of:
    0.017552461 = weight(_text_:of in 6849) [ClassicSimilarity], result of:
      0.017552461 = score(doc=6849,freq=22.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.28651062 = fieldWeight in 6849, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6849)
    0.020439833 = weight(_text_:systems in 6849) [ClassicSimilarity], result of:
      0.020439833 = score(doc=6849,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.1697705 = fieldWeight in 6849, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6849)
  0.22222222 = coord(2/9)

Abstract

It has become increasingly difficult to locate relevant information on the Web, even with the help of Web search engines. Two approaches to addressing the low precision and poor presentation of search results of current search tools are studied: meta-search and document categorization. Meta-search engines improve precision by selecting and integrating search results from generic or domain-specific Web search engines or other resources. Document categorization promises better organization and presentation of retrieved results. This article introduces MetaSpider, a meta-search engine that has real-time indexing and categorizing functions. We report in this paper the major components of MetaSpider and discuss related technical approaches. Initial results of a user evaluation study comparing Meta-Spider, NorthernLight, and MetaCrawler in terms of clustering performance and of time and effort expended show that MetaSpider performed best in precision rate, but disclose no statistically significant differences in recall rate and time requirements. Our experimental study also reveals that MetaSpider exhibited a higher level of automation than the other two systems and facilitated efficient searching by providing the user with an organized, comprehensive view of the retrieved documents.

Source

Journal of the American Society for Information Science and technology. 52(2001) no.13, S.1134-1147

0.0075302785 = product of:
  0.033886254 = sum of:
    0.017962547 = weight(_text_:of in 2733) [ClassicSimilarity], result of:
      0.017962547 = score(doc=2733,freq=16.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.2932045 = fieldWeight in 2733, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2733)
    0.015923709 = product of:
      0.031847417 = sum of:
        0.031847417 = weight(_text_:22 in 2733) [ClassicSimilarity], result of:
          0.031847417 = score(doc=2733,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = queryNorm
            0.23214069 = fieldWeight in 2733, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2733)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

While the Web has grown significantly in recent years, some portions of the Web remain largely underdeveloped, as shown in a lack of high-quality content and functionality. An example is the Arabic Web, in which a lack of well-structured Web directories limits users' ability to browse for Arabic resources. In this research, we proposed an approach to building Web directories for the underdeveloped Web and developed a proof-of-concept prototype called the Arabic Medical Web Directory (AMedDir) that supports browsing of over 5,000 Arabic medical Web sites and pages organized in a hierarchical structure. We conducted an experiment involving Arab participants and found that the AMedDir significantly outperformed two benchmark Arabic Web directories in terms of browsing effectiveness, efficiency, information quality, and user satisfaction. Participants expressed strong preference for the AMedDir and provided many positive comments. This research thus contributes to developing a useful Web directory for organizing the information in the Arabic medical domain and to a better understanding of how to support browsing on the underdeveloped Web.

Date

22. 3.2009 17:57:50

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.3, S.595-607