Search (3 results, page 1 of 1)

  • × classification_ss:"025.04"
  • × classification_ss:"06.74 / Informationssysteme"
  • × year_i:[2000 TO 2010}
  1. Hare, C.E.; McLeod, J.: How to manage records in the e-environment : 2nd ed. (2006) 0.04
    0.042009234 = product of:
      0.06301385 = sum of:
        0.017470727 = weight(_text_:information in 1749) [ClassicSimilarity], result of:
          0.017470727 = score(doc=1749,freq=4.0), product of:
            0.09099081 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0518325 = queryNorm
            0.1920054 = fieldWeight in 1749, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1749)
        0.045543127 = product of:
          0.09108625 = sum of:
            0.09108625 = weight(_text_:management in 1749) [ClassicSimilarity], result of:
              0.09108625 = score(doc=1749,freq=8.0), product of:
                0.17470726 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.0518325 = queryNorm
                0.521365 = fieldWeight in 1749, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=1749)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    A practical approach to developing and operating an effective programme to manage hybrid records within an organization. This title positions records management as an integral business function linked to the organisation's business aims and objectives. The authors also address the records requirements of new and significant pieces of legislation, such as data protection and freedom of information, as well as exploring strategies for managing electronic records. Bullet points, checklists and examples assist the reader throughout, making this a one-stop resource for information in this area.
    Footnote
    1. Aufl. u.d.T.: Developing a records management programme
    LCSH
    Records / Management
    Subject
    Records / Management
  2. Hermans, J.: Ontologiebasiertes Information Retrieval für das Wissensmanagement (2008) 0.02
    0.024283446 = product of:
      0.03642517 = sum of:
        0.023412848 = weight(_text_:information in 506) [ClassicSimilarity], result of:
          0.023412848 = score(doc=506,freq=22.0), product of:
            0.09099081 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0518325 = queryNorm
            0.25731003 = fieldWeight in 506, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.03125 = fieldNorm(doc=506)
        0.013012322 = product of:
          0.026024643 = sum of:
            0.026024643 = weight(_text_:management in 506) [ClassicSimilarity], result of:
              0.026024643 = score(doc=506,freq=2.0), product of:
                0.17470726 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.0518325 = queryNorm
                0.14896142 = fieldWeight in 506, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.03125 = fieldNorm(doc=506)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    Unternehmen sehen sich heutzutage regelmäßig der Herausforderung gegenübergestellt, aus umfangreichen Mengen an Dokumenten schnell relevante Informationen zu identifizieren. Dabei zeigt sich jedoch, dass Suchverfahren, die lediglich syntaktische Abgleiche von Informationsbedarfen mit potenziell relevanten Dokumenten durchführen, häufig nicht die an sie gestellten Erwartungen erfüllen. Viel versprechendes Potenzial bietet hier der Einsatz von Ontologien für das Information Retrieval. Beim ontologiebasierten Information Retrieval werden Ontologien eingesetzt, um Wissen in einer Form abzubilden, die durch Informationssysteme verarbeitet werden kann. Eine Berücksichtigung des so explizierten Wissens durch Suchalgorithmen führt dann zu einer optimierten Deckung von Informationsbedarfen. Jan Hermans stellt in seinem Buch ein adaptives Referenzmodell für die Entwicklung von ontologiebasierten Information Retrieval-Systemen vor. Zentrales Element seines Modells ist die einsatzkontextspezifische Adaption des Retrievalprozesses durch bewährte Techniken, die ausgewählte Aspekte des ontologiebasierten Information Retrievals bereits effektiv und effizient unterstützen. Die Anwendung des Referenzmodells wird anhand eines Fallbeispiels illustriert, bei dem ein Information Retrieval-System für die Suche nach Open Source-Komponenten entwickelt wird. Das Buch richtet sich gleichermaßen an Dozenten und Studierende der Wirtschaftsinformatik, Informatik und Betriebswirtschaftslehre sowie an Praktiker, die die Informationssuche im Unternehmen verbessern möchten. Jan Hermans, Jahrgang 1978, studierte Wirtschaftsinformatik an der Westfälischen Wilhelms-Universität in Münster. Seit 2003 war er als Wissenschaftlicher Mitarbeiter am European Research Center for Information Systems der WWU Münster tätig. Seine Forschungsschwerpunkte lagen in den Bereichen Wissensmanagement und Information Retrieval. Im Mai 2008 erfolgte seine Promotion zum Doktor der Wirtschaftswissenschaften.
    RSWK
    Information Retrieval / Ontologie <Wissensverarbeitung> / Wissensmanagement
    Series
    Advances in information systems and management science; 39
    Subject
    Information Retrieval / Ontologie <Wissensverarbeitung> / Wissensmanagement
  3. Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (2005) 0.02
    0.02112621 = product of:
      0.031689316 = sum of:
        0.018676993 = weight(_text_:information in 7) [ClassicSimilarity], result of:
          0.018676993 = score(doc=7,freq=14.0), product of:
            0.09099081 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0518325 = queryNorm
            0.20526241 = fieldWeight in 7, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.03125 = fieldNorm(doc=7)
        0.013012322 = product of:
          0.026024643 = sum of:
            0.026024643 = weight(_text_:management in 7) [ClassicSimilarity], result of:
              0.026024643 = score(doc=7,freq=2.0), product of:
                0.17470726 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.0518325 = queryNorm
                0.14896142 = fieldWeight in 7, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.03125 = fieldNorm(doc=7)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    The second edition of Understanding Search Engines: Mathematical Modeling and Text Retrieval follows the basic premise of the first edition by discussing many of the key design issues for building search engines and emphasizing the important role that applied mathematics can play in improving information retrieval. The authors discuss important data structures, algorithms, and software as well as user-centered issues such as interfaces, manual indexing, and document preparation. Significant changes bring the text up to date on current information retrieval methods: for example the addition of a new chapter on link-structure algorithms used in search engines such as Google. The chapter on user interface has been rewritten to specifically focus on search engine usability. In addition the authors have added new recommendations for further reading and expanded the bibliography, and have updated and streamlined the index to make it more reader friendly.
    Content
    Inhalt: Introduction Document File Preparation - Manual Indexing - Information Extraction - Vector Space Modeling - Matrix Decompositions - Query Representations - Ranking and Relevance Feedback - Searching by Link Structure - User Interface - Book Format Document File Preparation Document Purification and Analysis - Text Formatting - Validation - Manual Indexing - Automatic Indexing - Item Normalization - Inverted File Structures - Document File - Dictionary List - Inversion List - Other File Structures Vector Space Models Construction - Term-by-Document Matrices - Simple Query Matching - Design Issues - Term Weighting - Sparse Matrix Storage - Low-Rank Approximations Matrix Decompositions QR Factorization - Singular Value Decomposition - Low-Rank Approximations - Query Matching - Software - Semidiscrete Decomposition - Updating Techniques Query Management Query Binding - Types of Queries - Boolean Queries - Natural Language Queries - Thesaurus Queries - Fuzzy Queries - Term Searches - Probabilistic Queries Ranking and Relevance Feedback Performance Evaluation - Precision - Recall - Average Precision - Genetic Algorithms - Relevance Feedback Searching by Link Structure HITS Method - HITS Implementation - HITS Summary - PageRank Method - PageRank Adjustments - PageRank Implementation - PageRank Summary User Interface Considerations General Guidelines - Search Engine Interfaces - Form Fill-in - Display Considerations - Progress Indication - No Penalties for Error - Results - Test and Retest - Final Considerations Further Reading
    RSWK
    Suchmaschine / Information Retrieval
    Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)
    Subject
    Suchmaschine / Information Retrieval
    Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)