Search (23 results, page 1 of 2)

0.036927395 = product of:
  0.07385479 = sum of:
    0.07385479 = sum of:
      0.038503684 = weight(_text_:systems in 7469) [ClassicSimilarity], result of:
        0.038503684 = score(doc=7469,freq=4.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = 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)
      0.0353511 = weight(_text_:22 in 7469) [ClassicSimilarity], result of:
        0.0353511 = score(doc=7469,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = 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)

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

0.014293764 = product of:
  0.028587528 = sum of:
    0.028587528 = product of:
      0.057175055 = sum of:
        0.057175055 = weight(_text_:systems in 3232) [ClassicSimilarity], result of:
          0.057175055 = score(doc=3232,freq=18.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.014147157 = product of:
  0.028294314 = sum of:
    0.028294314 = product of:
      0.056588627 = sum of:
        0.056588627 = weight(_text_:systems in 1247) [ClassicSimilarity], result of:
          0.056588627 = score(doc=1247,freq=6.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.011551105 = product of:
  0.02310221 = sum of:
    0.02310221 = product of:
      0.04620442 = sum of:
        0.04620442 = weight(_text_:systems in 1610) [ClassicSimilarity], result of:
          0.04620442 = score(doc=1610,freq=4.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.011551105 = product of:
  0.02310221 = sum of:
    0.02310221 = product of:
      0.04620442 = sum of:
        0.04620442 = weight(_text_:systems in 501) [ClassicSimilarity], result of:
          0.04620442 = score(doc=501,freq=4.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.010605331 = product of:
  0.021210661 = sum of:
    0.021210661 = product of:
      0.042421322 = sum of:
        0.042421322 = weight(_text_:22 in 2733) [ClassicSimilarity], result of:
          0.042421322 = score(doc=2733,freq=2.0), product of:
            0.1827397 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052184064 = 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)
  0.5 = coord(1/2)

Date

22. 3.2009 17:57:50

0.009625921 = product of:
  0.019251842 = sum of:
    0.019251842 = product of:
      0.038503684 = sum of:
        0.038503684 = weight(_text_:systems in 1615) [ClassicSimilarity], result of:
          0.038503684 = score(doc=1615,freq=4.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.009625921 = product of:
  0.019251842 = sum of:
    0.019251842 = product of:
      0.038503684 = sum of:
        0.038503684 = weight(_text_:systems in 5054) [ClassicSimilarity], result of:
          0.038503684 = score(doc=5054,freq=4.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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

0.009625921 = product of:
  0.019251842 = sum of:
    0.019251842 = product of:
      0.038503684 = sum of:
        0.038503684 = weight(_text_:systems in 2699) [ClassicSimilarity], result of:
          0.038503684 = score(doc=2699,freq=4.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.009529176 = product of:
  0.019058352 = sum of:
    0.019058352 = product of:
      0.038116705 = sum of:
        0.038116705 = weight(_text_:systems in 2657) [ClassicSimilarity], result of:
          0.038116705 = score(doc=2657,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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

0.008837775 = product of:
  0.01767555 = sum of:
    0.01767555 = product of:
      0.0353511 = sum of:
        0.0353511 = weight(_text_:22 in 5259) [ClassicSimilarity], result of:
          0.0353511 = score(doc=5259,freq=2.0), product of:
            0.1827397 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 7.2006 14:26:01

0.008837775 = product of:
  0.01767555 = sum of:
    0.01767555 = product of:
      0.0353511 = sum of:
        0.0353511 = weight(_text_:22 in 5276) [ClassicSimilarity], result of:
          0.0353511 = score(doc=5276,freq=2.0), product of:
            0.1827397 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052184064 = 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)
  0.5 = coord(1/2)

Date

22. 7.2006 16:14:37

0.008837775 = product of:
  0.01767555 = sum of:
    0.01767555 = product of:
      0.0353511 = sum of:
        0.0353511 = weight(_text_:22 in 2753) [ClassicSimilarity], result of:
          0.0353511 = score(doc=2753,freq=2.0), product of:
            0.1827397 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 3.2009 18:50:30

0.008167865 = product of:
  0.01633573 = sum of:
    0.01633573 = product of:
      0.03267146 = sum of:
        0.03267146 = weight(_text_:systems in 2203) [ClassicSimilarity], result of:
          0.03267146 = score(doc=2203,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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

0.008167865 = product of:
  0.01633573 = sum of:
    0.01633573 = product of:
      0.03267146 = sum of:
        0.03267146 = weight(_text_:systems in 1148) [ClassicSimilarity], result of:
          0.03267146 = score(doc=1148,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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

0.008167865 = product of:
  0.01633573 = sum of:
    0.01633573 = product of:
      0.03267146 = sum of:
        0.03267146 = weight(_text_:systems in 3233) [ClassicSimilarity], result of:
          0.03267146 = score(doc=3233,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.0068065543 = product of:
  0.013613109 = sum of:
    0.013613109 = product of:
      0.027226217 = sum of:
        0.027226217 = weight(_text_:systems in 2918) [ClassicSimilarity], result of:
          0.027226217 = score(doc=2918,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.1697705 = fieldWeight in 2918, 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=2918)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Reports an algorithmic approach to the automatic generation of thesauri for electronic community systems. The techniques used included terms filtering, automatic indexing, and cluster analysis. The testbed for the research was the Worm Community System, which contains a comprehensive library of specialized community data and literature, currently in use by molecular biologists who study the nematode worm. The resulting worm thesaurus included 2709 researchers' names, 798 gene names, 20 experimental methods, and 4302 subject descriptors. On average, each term had about 90 weighted neighbouring terms indicating relevant concepts. The thesaurus was developed as an online search aide. Tests the worm thesaurus in an experiment with 6 worm researchers of varying degrees of expertise and background. The experiment showed that the thesaurus was an excellent 'memory jogging' device and that it supported learning and serendipitous browsing. Despite some occurrences of obvious noise, the system was useful in suggesting relevant concepts for the researchers' queries and it helped improve concept recall. With a simple browsing interface, an automatic thesaurus can become a useful tool for online search and can assist researchers in exploring and traversing a dynamic and complex electronic community system

0.0068065543 = product of:
  0.013613109 = sum of:
    0.013613109 = product of:
      0.027226217 = sum of:
        0.027226217 = weight(_text_:systems in 6849) [ClassicSimilarity], result of:
          0.027226217 = score(doc=6849,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.0068065543 = product of:
  0.013613109 = sum of:
    0.013613109 = product of:
      0.027226217 = sum of:
        0.027226217 = weight(_text_:systems in 2943) [ClassicSimilarity], result of:
          0.027226217 = score(doc=2943,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.1697705 = fieldWeight in 2943, 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=2943)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Biomedical research is critical to biodefense, which is drawing increasing attention from governments globally as well as from various research communities. The U.S. government has been closely monitoring and regulating biomedical research activities, particularly those studying or involving bioterrorism agents or diseases. Effective surveillance requires comprehensive understanding of extant biomedical research and timely detection of new developments or emerging trends. The rapid knowledge expansion, technical breakthroughs, and spiraling collaboration networks demand greater support for literature search and sharing, which cannot be effectively supported by conventional literature search mechanisms or systems. In this study, we propose an integrated approach that integrates advanced techniques for content analysis, network analysis, and information visualization. We design and implement Arizona Literature Mapper, a Web-based portal that allows users to gain timely, comprehensive understanding of bioterrorism research, including leading scientists, research groups, institutions as well as insights about current mainstream interests or emerging trends. We conduct two user studies to evaluate Arizona Literature Mapper and include a well-known system for benchmarking purposes. According to our results, Arizona Literature Mapper is significantly more effective for supporting users' search of bioterrorism publications than PubMed. Users consider Arizona Literature Mapper more useful and easier to use than PubMed. Users are also more satisfied with Arizona Literature Mapper and show stronger intentions to use it in the future. Assessments of Arizona Literature Mapper's analysis functions are also positive, as our subjects consider them useful, easy to use, and satisfactory. Our results have important implications that are also discussed in the article.

0.0068065543 = product of:
  0.013613109 = sum of:
    0.013613109 = product of:
      0.027226217 = sum of:
        0.027226217 = weight(_text_:systems in 3325) [ClassicSimilarity], result of:
          0.027226217 = score(doc=3325,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.