Search (60 results, page 1 of 3)

0.0023597742 = product of:
  0.03539661 = sum of:
    0.03539661 = sum of:
      0.011839852 = weight(_text_:information in 2733) [ClassicSimilarity], result of:
        0.011839852 = score(doc=2733,freq=8.0), product of:
          0.050870337 = queryWeight, product of:
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.028978055 = queryNorm
          0.23274569 = fieldWeight in 2733, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.046875 = fieldNorm(doc=2733)
      0.023556758 = weight(_text_:22 in 2733) [ClassicSimilarity], result of:
        0.023556758 = score(doc=2733,freq=2.0), product of:
          0.101476215 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.028978055 = 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)
  0.06666667 = coord(1/15)

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

Theme

Information Gateway

0.0019664785 = product of:
  0.029497176 = sum of:
    0.029497176 = sum of:
      0.009866543 = weight(_text_:information in 7469) [ClassicSimilarity], result of:
        0.009866543 = score(doc=7469,freq=8.0), product of:
          0.050870337 = queryWeight, product of:
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.028978055 = queryNorm
          0.19395474 = fieldWeight in 7469, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.0390625 = fieldNorm(doc=7469)
      0.019630633 = weight(_text_:22 in 7469) [ClassicSimilarity], result of:
        0.019630633 = score(doc=7469,freq=2.0), product of:
          0.101476215 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.028978055 = 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)
  0.06666667 = coord(1/15)

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

0.0018163302 = product of:
  0.013622476 = sum of:
    0.009436456 = weight(_text_:und in 5202) [ClassicSimilarity], result of:
      0.009436456 = score(doc=5202,freq=2.0), product of:
        0.06422601 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.028978055 = queryNorm
        0.14692576 = fieldWeight in 5202, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.046875 = fieldNorm(doc=5202)
    0.0041860198 = product of:
      0.0083720395 = sum of:
        0.0083720395 = weight(_text_:information in 5202) [ClassicSimilarity], result of:
          0.0083720395 = score(doc=5202,freq=4.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.16457605 = fieldWeight in 5202, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=5202)
      0.5 = coord(1/2)
  0.13333334 = coord(2/15)

Abstract

In this article, we report research on an algorithmic approach to alleviating search uncertainty in a large information space. Grounded on object filtering, automatic indexing, and co-occurence analysis, we performed a large-scale experiment using a parallel supercomputer (SGI Power Challenge) to analyze 400.000+ abstracts in an INSPEC computer engineering collection. Two system-generated thesauri, one based on a combined object filtering and automatic indexing method, and the other based on automatic indexing only, were compaed with the human-generated INSPEC subject thesaurus. Our user evaluation revealed that the system-generated thesauri were better than the INSPEC thesaurus in 'concept recall', but in 'concept precision' the 3 thesauri were comparable. Our analysis also revealed that the terms suggested by the 3 thesauri were complementary and could be used to significantly increase 'variety' in search terms the thereby reduce search uncertainty

Source

Journal of the American Society for Information Science. 49(1998) no.3, S.206-216

Theme

Konzeption und Anwendung des Prinzips Thesaurus

0.0016775922 = product of:
  0.025163881 = sum of:
    0.025163881 = weight(_text_:und in 7623) [ClassicSimilarity], result of:
      0.025163881 = score(doc=7623,freq=2.0), product of:
        0.06422601 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.028978055 = queryNorm
        0.39180204 = fieldWeight in 7623, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.125 = fieldNorm(doc=7623)
  0.06666667 = coord(1/15)

Theme

Konzeption und Anwendung des Prinzips Thesaurus

0.0016528559 = product of:
  0.0123964185 = sum of:
    0.009436456 = weight(_text_:und in 2203) [ClassicSimilarity], result of:
      0.009436456 = score(doc=2203,freq=2.0), product of:
        0.06422601 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.028978055 = queryNorm
        0.14692576 = fieldWeight in 2203, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.046875 = fieldNorm(doc=2203)
    0.002959963 = product of:
      0.005919926 = sum of:
        0.005919926 = weight(_text_:information in 2203) [ClassicSimilarity], result of:
          0.005919926 = score(doc=2203,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.116372846 = fieldWeight in 2203, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=2203)
      0.5 = coord(1/2)
  0.13333334 = coord(2/15)

Source

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

Theme

Konzeption und Anwendung des Prinzips Thesaurus

0.0016375937 = product of:
  0.024563905 = sum of:
    0.024563905 = sum of:
      0.0049332716 = weight(_text_:information in 5259) [ClassicSimilarity], result of:
        0.0049332716 = score(doc=5259,freq=2.0), product of:
          0.050870337 = queryWeight, product of:
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.028978055 = queryNorm
          0.09697737 = fieldWeight in 5259, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5259)
      0.019630633 = weight(_text_:22 in 5259) [ClassicSimilarity], result of:
        0.019630633 = score(doc=5259,freq=2.0), product of:
          0.101476215 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.028978055 = queryNorm
          0.19345059 = fieldWeight in 5259, 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=5259)
  0.06666667 = coord(1/15)

Date

22. 7.2006 14:26:01

Source

Journal of the American Society for Information Science and Technology. 56(2005) no.5, S.457-468

0.0016375937 = product of:
  0.024563905 = sum of:
    0.024563905 = sum of:
      0.0049332716 = weight(_text_:information in 5276) [ClassicSimilarity], result of:
        0.0049332716 = score(doc=5276,freq=2.0), product of:
          0.050870337 = queryWeight, product of:
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.028978055 = queryNorm
          0.09697737 = fieldWeight in 5276, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5276)
      0.019630633 = weight(_text_:22 in 5276) [ClassicSimilarity], result of:
        0.019630633 = score(doc=5276,freq=2.0), product of:
          0.101476215 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.028978055 = 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)
  0.06666667 = coord(1/15)

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

0.0016375937 = product of:
  0.024563905 = sum of:
    0.024563905 = sum of:
      0.0049332716 = weight(_text_:information in 2753) [ClassicSimilarity], result of:
        0.0049332716 = score(doc=2753,freq=2.0), product of:
          0.050870337 = queryWeight, product of:
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.028978055 = queryNorm
          0.09697737 = fieldWeight in 2753, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2753)
      0.019630633 = weight(_text_:22 in 2753) [ClassicSimilarity], result of:
        0.019630633 = score(doc=2753,freq=2.0), product of:
          0.101476215 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.028978055 = queryNorm
          0.19345059 = fieldWeight in 2753, 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=2753)
  0.06666667 = coord(1/15)

Date

22. 3.2009 18:50:30

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.4, S.655-665

0.00137738 = product of:
  0.010330349 = sum of:
    0.007863713 = weight(_text_:und in 2918) [ClassicSimilarity], result of:
      0.007863713 = score(doc=2918,freq=2.0), product of:
        0.06422601 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.028978055 = queryNorm
        0.12243814 = fieldWeight in 2918, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2918)
    0.0024666358 = product of:
      0.0049332716 = sum of:
        0.0049332716 = weight(_text_:information in 2918) [ClassicSimilarity], result of:
          0.0049332716 = score(doc=2918,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.09697737 = fieldWeight in 2918, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2918)
      0.5 = coord(1/2)
  0.13333334 = coord(2/15)

Source

Journal of the American Society for Information Science. 46(1995) no.3, S.175-193

Theme

Konzeption und Anwendung des Prinzips Thesaurus

5.9199263E-4 = product of:
  0.008879889 = sum of:
    0.008879889 = product of:
      0.017759778 = sum of:
        0.017759778 = weight(_text_:information in 1148) [ClassicSimilarity], result of:
          0.017759778 = score(doc=1148,freq=18.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.34911853 = fieldWeight in 1148, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=1148)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

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

5.9199263E-4 = product of:
  0.008879889 = sum of:
    0.008879889 = product of:
      0.017759778 = sum of:
        0.017759778 = weight(_text_:information in 1247) [ClassicSimilarity], result of:
          0.017759778 = score(doc=1247,freq=18.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.34911853 = fieldWeight in 1247, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=1247)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

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.

5.88327E-4 = product of:
  0.008824904 = sum of:
    0.008824904 = product of:
      0.017649809 = sum of:
        0.017649809 = weight(_text_:information in 8221) [ClassicSimilarity], result of:
          0.017649809 = score(doc=8221,freq=10.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.3469568 = fieldWeight in 8221, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0625 = fieldNorm(doc=8221)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Abstract

Attempts to identify the nature and causes of information management indeterminism in an online research environment and proposes solutions for alleviating this indeterminism. Conducts two empirical studies of information management activities. The first identified the types and nature of information management indeterminism by evaluating archived text. The second focused on four sources of indeterminism: subject area knowledge, classification knowledge, system knowledge, and collaboration knowledge. Proposes a knowledge based design for alleviating indeterminism, which contains a system generated thesaurus and an inferencing engine

Source

Information processing and management. 30(1994) no.4, S.557-577

5.6391995E-4 = product of:
  0.008458799 = sum of:
    0.008458799 = product of:
      0.016917598 = sum of:
        0.016917598 = weight(_text_:information in 2657) [ClassicSimilarity], result of:
          0.016917598 = score(doc=2657,freq=12.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.3325631 = fieldWeight in 2657, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2657)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

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

5.453937E-4 = product of:
  0.008180905 = sum of:
    0.008180905 = product of:
      0.01636181 = sum of:
        0.01636181 = weight(_text_:information in 2505) [ClassicSimilarity], result of:
          0.01636181 = score(doc=2505,freq=22.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.32163754 = fieldWeight in 2505, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2505)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Abstract

To make good decisions, businesses try to gather good intelligence information. Yet managing and processing a large amount of unstructured information and data stand in the way of greater business knowledge. An effective business intelligence tool must be able to access quality information from a variety of sources in a variety of forms, and it must support people as they search for and analyze that information. The EBizPort system was designed to address information needs for the business/IT community. EBizPort's collection-building process is designed to acquire credible, timely, and relevant information. The user interface provides access to collected and metasearched resources using innovative tools for summarization, categorization, and visualization. The effectiveness, efficiency, usability, and information quality of the EBizPort system were measured. EBizPort significantly outperformed Brint, a business search portal, in search effectiveness, information quality, user satisfaction, and usability. Users particularly liked EBizPort's clean and user-friendly interface. Results from our evaluation study suggest that the visualization function added value to the search and analysis process, that the generalizable collection-building technique can be useful for domain-specific information searching an the Web, and that the search interface was important for Web search and browse support.

Source

Journal of the American Society for Information Science and Technology. 55(2004) no.10, S.873-891

5.274988E-4 = product of:
  0.007912481 = sum of:
    0.007912481 = product of:
      0.015824962 = sum of:
        0.015824962 = weight(_text_:information in 4276) [ClassicSimilarity], result of:
          0.015824962 = score(doc=4276,freq=42.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.31108427 = fieldWeight in 4276, product of:
              6.4807405 = tf(freq=42.0), with freq of:
                42.0 = termFreq=42.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02734375 = fieldNorm(doc=4276)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

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

5.2621565E-4 = product of:
  0.0078932345 = sum of:
    0.0078932345 = product of:
      0.015786469 = sum of:
        0.015786469 = weight(_text_:information in 5283) [ClassicSimilarity], result of:
          0.015786469 = score(doc=5283,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.3103276 = fieldWeight in 5283, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.125 = fieldNorm(doc=5283)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Source

Journal of information science. 18(1992), S.293-314

5.2208843E-4 = product of:
  0.007831326 = sum of:
    0.007831326 = product of:
      0.015662652 = sum of:
        0.015662652 = weight(_text_:information in 1880) [ClassicSimilarity], result of:
          0.015662652 = score(doc=1880,freq=14.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.3078936 = fieldWeight in 1880, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=1880)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Abstract

While the Web has become a worldwide platform for communication, terrorists share their ideology and communicate with members on the Dark Web - the reverse side of the Web used by terrorists. Currently, the problems of information overload and difficulty to obtain a comprehensive picture of terrorist activities hinder effective and efficient analysis of terrorist information on the Web. To improve understanding of terrorist activities, we have developed a novel methodology for collecting and analyzing Dark Web information. The methodology incorporates information collection, analysis, and visualization techniques, and exploits various Web information sources. We applied it to collecting and analyzing information of 39 Jihad Web sites and developed visualization of their site contents, relationships, and activity levels. An expert evaluation showed that the methodology is very useful and promising, having a high potential to assist in investigation and understanding of terrorist activities by producing results that could potentially help guide both policymaking and intelligence research.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.8, S.1347-1359

4.5571616E-4 = product of:
  0.006835742 = sum of:
    0.006835742 = product of:
      0.013671484 = sum of:
        0.013671484 = weight(_text_:information in 3845) [ClassicSimilarity], result of:
          0.013671484 = score(doc=3845,freq=6.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.2687516 = fieldWeight in 3845, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0625 = fieldNorm(doc=3845)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Abstract

2 studies were conducted to investigate the cognitive processes involved in online document-based information retrieval. These studies led to the development of 5 computerised models of online document retrieval. These models were incorporated into a design of an 'intelligent' document-based retrieval system. Following a discussion of this system, discusses the broader implications of the research for the design of information retrieval sysems

Source

Information processing and management. 27(1991) no.5, S.405-432

4.4124527E-4 = product of:
  0.0066186786 = sum of:
    0.0066186786 = product of:
      0.013237357 = sum of:
        0.013237357 = weight(_text_:information in 1610) [ClassicSimilarity], result of:
          0.013237357 = score(doc=1610,freq=10.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.2602176 = fieldWeight in 1610, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=1610)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

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

4.4124527E-4 = product of:
  0.0066186786 = sum of:
    0.0066186786 = product of:
      0.013237357 = sum of:
        0.013237357 = weight(_text_:information in 4242) [ClassicSimilarity], result of:
          0.013237357 = score(doc=4242,freq=10.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.2602176 = fieldWeight in 4242, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=4242)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

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