Search (105 results, page 1 of 6)

  • × type_ss:"a"
  • × theme_ss:"Data Mining"
  1. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.04 
    0.03592316 = product of:
  0.07184632 = sum of:
    0.024031956 = weight(_text_:information in 4577) [ClassicSimilarity], result of:
      0.024031956 = score(doc=4577,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.27153665 = fieldWeight in 4577, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.109375 = fieldNorm(doc=4577)
    0.047814365 = product of:
      0.09562873 = sum of:
        0.09562873 = weight(_text_:22 in 4577) [ClassicSimilarity], result of:
          0.09562873 = score(doc=4577,freq=2.0), product of:
            0.17654699 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050415643 = queryNorm
            0.5416616 = fieldWeight in 4577, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=4577)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Date
    2. 4.2000 18:01:22
  2. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.03 
    0.027393792 = product of:
  0.054787584 = sum of:
    0.027465092 = weight(_text_:information in 1737) [ClassicSimilarity], result of:
      0.027465092 = score(doc=1737,freq=8.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.3103276 = fieldWeight in 1737, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=1737)
    0.027322493 = product of:
      0.054644987 = sum of:
        0.054644987 = weight(_text_:22 in 1737) [ClassicSimilarity], result of:
          0.054644987 = score(doc=1737,freq=2.0), product of:
            0.17654699 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050415643 = queryNorm
            0.30952093 = fieldWeight in 1737, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1737)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Abstract
    Defines digital libraries and discusses the effects of new technology on librarians. Examines the different viewpoints of librarians and information technologists on digital libraries. Describes the development of a digital library at the National Drug Intelligence Center, USA, which was carried out in collaboration with information technology experts. The system is based on Web enabled search technology to find information, data visualization and data mining to visualize it and use of SGML as an information standard to store it
    Date
    22.11.1998 18:57:22
  3. Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997) 0.03 
    0.025387865 = product of:
  0.05077573 = sum of:
    0.026868545 = weight(_text_:information in 2908) [ClassicSimilarity], result of:
      0.026868545 = score(doc=2908,freq=10.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.3035872 = fieldWeight in 2908, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2908)
    0.023907183 = product of:
      0.047814365 = sum of:
        0.047814365 = weight(_text_:22 in 2908) [ClassicSimilarity], result of:
          0.047814365 = score(doc=2908,freq=2.0), product of:
            0.17654699 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050415643 = queryNorm
            0.2708308 = fieldWeight in 2908, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2908)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Abstract
    Focuses on the information modelling side of conceptual modelling. Deals with the exploitation of fact verbalisations after finishing the actual information system. Verbalisations are used as input for the design of the so-called information model. Exploits these verbalisation in 4 directions: considers their use for a conceptual query language, the verbalisation of instances, the description of the contents of a database and for the verbalisation of queries in a computer supported query environment. Provides an example session with an envisioned tool for end user query formulations that exploits the verbalisation
    Source
    Information systems. 22(1997) nos.5/6, S.349-385
  4. Haravu, L.J.; Neelameghan, A.: Text mining and data mining in knowledge organization and discovery : the making of knowledge-based products (2003) 0.03 
    0.025134712 = product of:
  0.050269425 = sum of:
    0.014865918 = weight(_text_:information in 5653) [ClassicSimilarity], result of:
      0.014865918 = score(doc=5653,freq=6.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.16796975 = fieldWeight in 5653, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5653)
    0.035403505 = product of:
      0.07080701 = sum of:
        0.07080701 = weight(_text_:organization in 5653) [ClassicSimilarity], result of:
          0.07080701 = score(doc=5653,freq=8.0), product of:
            0.17974974 = queryWeight, product of:
              3.5653565 = idf(docFreq=3399, maxDocs=44218)
              0.050415643 = queryNorm
            0.39391994 = fieldWeight in 5653, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.5653565 = idf(docFreq=3399, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5653)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Abstract
    Discusses the importance of knowledge organization in the context of the information overload caused by the vast quantities of data and information accessible on internal and external networks of an organization. Defines the characteristics of a knowledge-based product. Elaborates on the techniques and applications of text mining in developing knowledge products. Presents two approaches, as case studies, to the making of knowledge products: (1) steps and processes in the planning, designing and development of a composite multilingual multimedia CD product, with the potential international, inter-cultural end users in view, and (2) application of natural language processing software in text mining. Using a text mining software, it is possible to link concept terms from a processed text to a related thesaurus, glossary, schedules of a classification scheme, and facet structured subject representations. Concludes that the products of text mining and data mining could be made more useful if the features of a faceted scheme for subject classification are incorporated into text mining techniques and products.
    Content
    Beitrag eines Themenheftes "Knowledge organization and classification in international information retrieval"
  5. Ohly, H.P.: Bibliometric mining : added value from document analysis and retrieval (2008) 0.02 
    0.022303218 = product of:
  0.044606436 = sum of:
    0.014565565 = weight(_text_:information in 2386) [ClassicSimilarity], result of:
      0.014565565 = score(doc=2386,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.16457605 = fieldWeight in 2386, 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=2386)
    0.030040871 = product of:
      0.060081743 = sum of:
        0.060081743 = weight(_text_:organization in 2386) [ClassicSimilarity], result of:
          0.060081743 = score(doc=2386,freq=4.0), product of:
            0.17974974 = queryWeight, product of:
              3.5653565 = idf(docFreq=3399, maxDocs=44218)
              0.050415643 = queryNorm
            0.33425218 = fieldWeight in 2386, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5653565 = idf(docFreq=3399, maxDocs=44218)
              0.046875 = fieldNorm(doc=2386)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Abstract
    Bibliometrics is understood as statistical analysis of scientific structures and processes. The analyzed data result from information and administrative actions. The demand for quality judgments or the discovering of new structures and information means that Bibliometrics takes on the role of being exploratory and decision supporting. To the extent that it has acquired important features of Data Mining, the analysis of text and internet material can be viewed as an additional challenge. In the sense of an evaluative approach Bibliometrics can also be seen to apply inference procedures as well as navigation tools.
    Source
    Kompatibilität, Medien und Ethik in der Wissensorganisation - Compatibility, Media and Ethics in Knowledge Organization: Proceedings der 10. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation Wien, 3.-5. Juli 2006 - Proceedings of the 10th Conference of the German Section of the International Society of Knowledge Organization Vienna, 3-5 July 2006. Ed.: H.P. Ohly, S. Netscher u. K. Mitgutsch
  6. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.02 
    0.02052752 = product of:
  0.04105504 = sum of:
    0.013732546 = weight(_text_:information in 1270) [ClassicSimilarity], result of:
      0.013732546 = score(doc=1270,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.1551638 = fieldWeight in 1270, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=1270)
    0.027322493 = product of:
      0.054644987 = sum of:
        0.054644987 = weight(_text_:22 in 1270) [ClassicSimilarity], result of:
          0.054644987 = score(doc=1270,freq=2.0), product of:
            0.17654699 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050415643 = queryNorm
            0.30952093 = fieldWeight in 1270, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1270)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Source
    Information systems. 22(1997) nos.5/6, S.333-347
  7. Wei, C.-P.; Lee, Y.-H.; Chiang, Y.-S.; Chen, C.-T.; Yang, C.C.C.: Exploiting temporal characteristics of features for effectively discovering event episodes from news corpora (2014) 0.02 
    0.017433718 = product of:
  0.034867436 = sum of:
    0.017165681 = weight(_text_:information in 1225) [ClassicSimilarity], result of:
      0.017165681 = score(doc=1225,freq=8.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.19395474 = fieldWeight in 1225, 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=1225)
    0.017701752 = product of:
      0.035403505 = sum of:
        0.035403505 = weight(_text_:organization in 1225) [ClassicSimilarity], result of:
          0.035403505 = score(doc=1225,freq=2.0), product of:
            0.17974974 = queryWeight, product of:
              3.5653565 = idf(docFreq=3399, maxDocs=44218)
              0.050415643 = queryNorm
            0.19695997 = fieldWeight in 1225, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5653565 = idf(docFreq=3399, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1225)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Abstract
    An organization performing environmental scanning generally monitors or tracks various events concerning its external environment. One of the major resources for environmental scanning is online news documents, which are readily accessible on news websites or infomediaries. However, the proliferation of the World Wide Web, which increases information sources and improves information circulation, has vastly expanded the amount of information to be scanned. Thus, it is essential to develop an effective event episode discovery mechanism to organize news documents pertaining to an event of interest. In this study, we propose two new metrics, Term Frequency × Inverse Document FrequencyTempo (TF×IDFTempo) and TF×Enhanced-IDFTempo, and develop a temporal-based event episode discovery (TEED) technique that uses the proposed metrics for feature selection and document representation. Using a traditional TF×IDF-based hierarchical agglomerative clustering technique as a performance benchmark, our empirical evaluation reveals that the proposed TEED technique outperforms its benchmark, as measured by cluster recall and cluster precision. In addition, the use of TF×Enhanced-IDFTempo significantly improves the effectiveness of event episode discovery when compared with the use of TF×IDFTempo.
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.3, S.621-634
  8. Srinivasan, P.: Text mining in biomedicine : challenges and opportunities (2006) 0.02 
    0.015770756 = product of:
  0.03154151 = sum of:
    0.01029941 = weight(_text_:information in 1497) [ClassicSimilarity], result of:
      0.01029941 = score(doc=1497,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.116372846 = fieldWeight in 1497, 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=1497)
    0.021242103 = product of:
      0.042484205 = sum of:
        0.042484205 = weight(_text_:organization in 1497) [ClassicSimilarity], result of:
          0.042484205 = score(doc=1497,freq=2.0), product of:
            0.17974974 = queryWeight, product of:
              3.5653565 = idf(docFreq=3399, maxDocs=44218)
              0.050415643 = queryNorm
            0.23635197 = fieldWeight in 1497, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5653565 = idf(docFreq=3399, maxDocs=44218)
              0.046875 = fieldNorm(doc=1497)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Source
    Knowledge organization, information systems and other essays: Professor A. Neelameghan Festschrift. Ed. by K.S. Raghavan and K.N. Prasad
  9. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.01 
    0.014607265 = product of:
  0.02921453 = sum of:
    0.01213797 = weight(_text_:information in 1605) [ClassicSimilarity], result of:
      0.01213797 = score(doc=1605,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.13714671 = fieldWeight in 1605, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1605)
    0.01707656 = product of:
      0.03415312 = sum of:
        0.03415312 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
          0.03415312 = score(doc=1605,freq=2.0), product of:
            0.17654699 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050415643 = queryNorm
            0.19345059 = fieldWeight in 1605, 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=1605)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Abstract
    Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  10. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.01 
    0.0128297005 = product of:
  0.025659401 = sum of:
    0.008582841 = weight(_text_:information in 668) [ClassicSimilarity], result of:
      0.008582841 = score(doc=668,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.09697737 = fieldWeight in 668, 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=668)
    0.01707656 = product of:
      0.03415312 = sum of:
        0.03415312 = weight(_text_:22 in 668) [ClassicSimilarity], result of:
          0.03415312 = score(doc=668,freq=2.0), product of:
            0.17654699 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050415643 = queryNorm
            0.19345059 = fieldWeight in 668, 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=668)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Date
    22. 3.2013 19:43:01
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.574-586
  11. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.01 
    0.0128297005 = product of:
  0.025659401 = sum of:
    0.008582841 = weight(_text_:information in 5011) [ClassicSimilarity], result of:
      0.008582841 = score(doc=5011,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.09697737 = fieldWeight in 5011, 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=5011)
    0.01707656 = product of:
      0.03415312 = sum of:
        0.03415312 = weight(_text_:22 in 5011) [ClassicSimilarity], result of:
          0.03415312 = score(doc=5011,freq=2.0), product of:
            0.17654699 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050415643 = queryNorm
            0.19345059 = fieldWeight in 5011, 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=5011)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Date
    7. 3.2019 16:32:22
    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.4, S.402-411
  12. Lischka, K.: Spurensuche im Datenwust : Data-Mining-Software fahndet nach kriminellen Mitarbeitern, guten Kunden - und bald vielleicht auch nach Terroristen (2002) 0.01 
    0.008764358 = product of:
  0.017528716 = sum of:
    0.0072827823 = weight(_text_:information in 1178) [ClassicSimilarity], result of:
      0.0072827823 = score(doc=1178,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.08228803 = fieldWeight in 1178, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0234375 = fieldNorm(doc=1178)
    0.010245935 = product of:
      0.02049187 = sum of:
        0.02049187 = weight(_text_:22 in 1178) [ClassicSimilarity], result of:
          0.02049187 = score(doc=1178,freq=2.0), product of:
            0.17654699 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050415643 = queryNorm
            0.116070345 = fieldWeight in 1178, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0234375 = fieldNorm(doc=1178)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Content
    "Ob man als Terrorist einen Anschlag gegen die Vereinigten Staaten plant, als Kassierer Scheine aus der Kasse unterschlägt oder für bestimmte Produkte besonders gerne Geld ausgibt - einen Unterschied macht Data-Mining-Software da nicht. Solche Programme analysieren riesige Daten- mengen und fällen statistische Urteile. Mit diesen Methoden wollen nun die For- scher des "Information Awaren in den Vereinigten Staaten Spuren von Terroristen in den Datenbanken von Behörden und privaten Unternehmen wie Kreditkartenfirmen finden. 200 Millionen Dollar umfasst der Jahresetat für die verschiedenen Forschungsprojekte. Dass solche Software in der Praxis funktioniert, zeigen die steigenden Umsätze der Anbieter so genannter Customer-Relationship-Management-Software. Im vergangenen Jahr ist das Potenzial für analytische CRM-Anwendungen laut dem Marktforschungsinstitut IDC weltweit um 22 Prozent gewachsen, bis zum Jahr 2006 soll es in Deutschland mit einem jährlichen Plus von 14,1 Prozent so weitergehen. Und das trotz schwacher Konjunktur - oder gerade deswegen. Denn ähnlich wie Data-Mining der USRegierung helfen soll, Terroristen zu finden, entscheiden CRM-Programme heute, welche Kunden für eine Firma profitabel sind. Und welche es künftig sein werden, wie Manuela Schnaubelt, Sprecherin des CRM-Anbieters SAP, beschreibt: "Die Kundenbewertung ist ein zentraler Bestandteil des analytischen CRM. Sie ermöglicht es Unternehmen, sich auf die für sie wichtigen und richtigen Kunden zu fokussieren. Darüber hinaus können Firmen mit speziellen Scoring- Verfahren ermitteln, welche Kunden langfristig in welchem Maße zum Unternehmenserfolg beitragen." Die Folgen der Bewertungen sind für die Betroffenen nicht immer positiv: Attraktive Kunden profitieren von individuellen Sonderangeboten und besonderer Zuwendung. Andere hängen vielleicht so lauge in der Warteschleife des Telefonservice, bis die profitableren Kunden abgearbeitet sind. So könnte eine praktische Umsetzung dessen aussehen, was SAP-Spreche-rin Schnaubelt abstrakt beschreibt: "In vielen Unternehmen wird Kundenbewertung mit der klassischen ABC-Analyse durchgeführt, bei der Kunden anhand von Daten wie dem Umsatz kategorisiert werden. A-Kunden als besonders wichtige Kunden werden anders betreut als C-Kunden." Noch näher am geplanten Einsatz von Data-Mining zur Terroristenjagd ist eine Anwendung, die heute viele Firmen erfolgreich nutzen: Sie spüren betrügende Mitarbeiter auf. Werner Sülzer vom großen CRM-Anbieter NCR Teradata beschreibt die Möglichkeiten so: "Heute hinterlässt praktisch jeder Täter - ob Mitarbeiter, Kunde oder Lieferant - Datenspuren bei seinen wirtschaftskriminellen Handlungen. Es muss vorrangig darum gehen, einzelne Spuren zu Handlungsmustern und Täterprofilen zu verdichten. Das gelingt mittels zentraler Datenlager und hoch entwickelter Such- und Analyseinstrumente." Von konkreten Erfolgen sprich: Entlas-sungen krimineller Mitarbeiter-nach Einsatz solcher Programme erzählen Unternehmen nicht gerne. Matthias Wilke von der "Beratungsstelle für Technologiefolgen und Qualifizierung" (BTQ) der Gewerkschaft Verdi weiß von einem Fall 'aus der Schweiz. Dort setzt die Handelskette "Pick Pay" das Programm "Lord Lose Prevention" ein. Zwei Monate nach Einfüh-rung seien Unterschlagungen im Wert von etwa 200 000 Franken ermittelt worden. Das kostete mehr als 50 verdächtige Kassiererinnen und Kassierer den Job.
    Jede Kasse schickt die Daten zu Stornos, Rückgaben, Korrekturen und dergleichen an eine zentrale Datenbank. Aus den Informationen errechnet das Programm Kassiererprofile. Wessen Arbeit stark Durchschnitt abweicht, macht sich verdächtig. Die Kriterien" legen im Einzelnen die Revisionsabteilungen fest, doch generell gilt: "Bei Auffälligkeiten wie überdurchschnittlichvielenStornierungen, Off nen der Kassenschublade ohne Verkauf nach einem Storno oder Warenrücknahmen ohne Kassenbon, können die Vorgänge nachträglich einzelnen Personen zugeordnet werden", sagt Rene Schiller, Marketing-Chef des Lord-Herstellers Logware. Ein Kündigungsgrund ist eine solche Datensammlung vor Gericht nicht. Doch auf der Basis können Unternehmen gezielt Detektive einsetzen. Oder sie konfrontieren die Mitarbeiter mit dem Material; woraufhin Schuldige meist gestehen. Wilke sieht Programme wie Lord kritisch:"Jeder, der in dem Raster auffällt, kann ein potenzieller Betrüger oder Dieb sein und verdient besondere Beobachtung." Dabei könne man vom Standard abweichen, weil man unausgeschlafen und deshalb unkonzentriert sei. Hier tut sich für Wilke die Gefahr technisierter Leistungskontrolle auf. "Es ist ja nicht schwierig, mit den Programmen zu berechnen, wie lange beispielsweise das Kassieren eines Samstagseinkaufs durchschnittlich dauert." Die Betriebsräte - ihre Zustimmung ist beim Einsatz technischer Kon trolleinrichtungen nötig - verurteilen die wertende Software weniger eindeutig. Im Gegenteil: Bei Kaufhof und Edeka haben sie dem Einsatz zugestimmt. Denn: "Die wollen ja nicht, dass ganze Abteilungen wegen Inventurverlusten oder dergleichen unter Generalverdacht fallen", erklärt Gewerkschaftler Wilke: "Angesichts der Leistungen kommerzieller Data-Mining-Programme verblüfft es, dass in den Vereinigten Staaten das "Information Awareness Office" noch drei Jahre für Forschung und Erprobung der eigenen Programme veranschlagt. 2005 sollen frühe Prototypen zur Terroristensuche einesgetz werden. Doch schon jetzt regt sich Protest. Datenschützer wie Marc Botenberg vom Informationszentrum für Daten schutz sprechen vom "ehrgeizigsten öffentlichen Überwachungssystem, das je vorgeschlagen wurde". Sie warnen besonders davor, Daten aus der Internetnutzung und private Mails auszuwerten. Das Verteidigungsministerium rudert zurück. Man denke nicht daran, über die Software im Inland aktiv zu werden. "Das werden die Geheimdienste, die Spionageabwehr und die Strafverfolger tun", sagt Unterstaatssekretär Edward Aldridge. Man werde während der Entwicklung und der Tests mit konstruierten und einigen - aus Sicht der Datenschützer unbedenklichen - realen Informationen arbeiten. Zu denken gibt jedoch Aldriges Antwort auf die Frage, warum so viel Geld für die Entwicklung von Übersetzungssoftware eingeplant ist: Damit man Datenbanken in anderen Sprachen nutzen könne - sofern man auf sie rechtmäßigen Zugriff bekommt."
  13. Bath, P.A.: Data mining in health and medical information (2003) 0.01 
    0.0076767267 = product of:
  0.030706907 = sum of:
    0.030706907 = weight(_text_:information in 4263) [ClassicSimilarity], result of:
      0.030706907 = score(doc=4263,freq=10.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.3469568 = fieldWeight in 4263, 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=4263)
  0.25 = coord(1/4)
    Abstract
    Data mining (DM) is part of a process by which information can be extracted from data or databases and used to inform decision making in a variety of contexts (Benoit, 2002; Michalski, Bratka & Kubat, 1997). DM includes a range of tools and methods for extractiog information; their use in the commercial sector for knowledge extraction and discovery has been one of the main driving forces in their development (Adriaans & Zantinge, 1996; Benoit, 2002). DM has been developed and applied in numerous areas. This review describes its use in analyzing health and medical information.
    Source
    Annual review of information science and technology. 38(2004), S.331-370
  14. Lam, W.; Yang, C.C.; Menczer, F.: Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007) 0.01 
    0.0073582535 = product of:
  0.029433014 = sum of:
    0.029433014 = weight(_text_:information in 600) [ClassicSimilarity], result of:
      0.029433014 = score(doc=600,freq=12.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.3325631 = fieldWeight in 600, 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=600)
  0.25 = coord(1/4)
    Abstract
    The amount of information on the Web has been expanding at an enormous pace. There are a variety of Web documents in different genres, such as news, reports, reviews. Traditionally, the information displayed on Web sites has been static. Recently, there are many Web sites offering content that is dynamically generated and frequently updated. It is also common for Web sites to contain information in different languages since many countries adopt more than one language. Moreover, content may exist in multimedia formats including text, images, video, and audio.
    Footnote
    Einführung in einen Themenschwerpunkt "Mining Web resources for enhancing information retrieval"
    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1791-1792
  15. Chen, S.Y.; Liu, X.: ¬The contribution of data mining to information science : making sense of it all (2005) 0.01 
    0.0072827823 = product of:
  0.02913113 = sum of:
    0.02913113 = weight(_text_:information in 4655) [ClassicSimilarity], result of:
      0.02913113 = score(doc=4655,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.3291521 = fieldWeight in 4655, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.09375 = fieldNorm(doc=4655)
  0.25 = coord(1/4)
    Source
    Journal of information science. 30(2005) no.6, S.550-
  16. Cardie, C.: Empirical methods in information extraction (1997) 0.01 
    0.006866273 = product of:
  0.027465092 = sum of:
    0.027465092 = weight(_text_:information in 3246) [ClassicSimilarity], result of:
      0.027465092 = score(doc=3246,freq=8.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.3103276 = fieldWeight in 3246, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=3246)
  0.25 = coord(1/4)
    Abstract
    Surveys the use of empirical, machine-learning methods for information extraction. Presents a generic architecture for information extraction systems and surveys the learning algorithms that have been developed to address the problems of accuracy, portability, and knowledge acquisition for each component of the architecture
    Footnote
    Contribution to a special section reviewing recent research in empirical methods in speech recognition, syntactic parsing, semantic processing, information extraction and machine translation
  17. Budzik, J.; Hammond, K.J.; Birnbaum, L.: Information access in context (2001) 0.01 
    0.006007989 = product of:
  0.024031956 = sum of:
    0.024031956 = weight(_text_:information in 3835) [ClassicSimilarity], result of:
      0.024031956 = score(doc=3835,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.27153665 = fieldWeight in 3835, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.109375 = fieldNorm(doc=3835)
  0.25 = coord(1/4)
  18. Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010) 0.01 
    0.006007989 = product of:
  0.024031956 = sum of:
    0.024031956 = weight(_text_:information in 4676) [ClassicSimilarity], result of:
      0.024031956 = score(doc=4676,freq=8.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.27153665 = fieldWeight in 4676, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4676)
  0.25 = coord(1/4)
    Abstract
    This paper discusses an approach of collecting qualitative data on human information behaviour that is based on mining web data using search engines. The approach is technically the same that has been used for some time in webometric research to make statistical inferences on web data, but the present paper shows how the same tools and data collecting methods can be used to gather data for qualitative data analysis on human information behaviour.
    Source
    Information und Wissen: global, sozial und frei? Proceedings des 12. Internationalen Symposiums für Informationswissenschaft (ISI 2011) ; Hildesheim, 9. - 11. März 2011. Hrsg.: J. Griesbaum, T. Mandl u. C. Womser-Hacker
  19. Blake, C.: Text mining (2011) 0.01 
    0.006007989 = product of:
  0.024031956 = sum of:
    0.024031956 = weight(_text_:information in 1599) [ClassicSimilarity], result of:
      0.024031956 = score(doc=1599,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.27153665 = fieldWeight in 1599, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.109375 = fieldNorm(doc=1599)
  0.25 = coord(1/4)
    Source
    Annual review of information science and technology. 45(2011) no.1, S.121-155
  20. Chen, H.; Chau, M.: Web mining : machine learning for Web applications (2003) 0.01 
    0.005757545 = product of:
  0.02303018 = sum of:
    0.02303018 = weight(_text_:information in 4242) [ClassicSimilarity], result of:
      0.02303018 = score(doc=4242,freq=10.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = 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)
  0.25 = coord(1/4)
    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

Authors

Years

Languages

  • e 95
  • d 9
  • sp 1
  • More… Less…

Themes

Subjects

Classifications