Search (5 results, page 1 of 1)

  • × classification_ss:"BCA (FH K)"
  1. White, R.W.; Roth, R.A.: Exploratory search : beyond the query-response paradigm (2009) 0.03
    0.02598055 = product of:
      0.1039222 = sum of:
        0.07508315 = weight(_text_:supported in 0) [ClassicSimilarity], result of:
          0.07508315 = score(doc=0,freq=2.0), product of:
            0.22949564 = queryWeight, product of:
              5.9223356 = idf(docFreq=321, maxDocs=44218)
              0.03875087 = queryNorm
            0.3271659 = fieldWeight in 0, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.9223356 = idf(docFreq=321, maxDocs=44218)
              0.0390625 = fieldNorm(doc=0)
        0.028839052 = weight(_text_:work in 0) [ClassicSimilarity], result of:
          0.028839052 = score(doc=0,freq=2.0), product of:
            0.14223081 = queryWeight, product of:
              3.6703904 = idf(docFreq=3060, maxDocs=44218)
              0.03875087 = queryNorm
            0.20276234 = fieldWeight in 0, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6703904 = idf(docFreq=3060, maxDocs=44218)
              0.0390625 = fieldNorm(doc=0)
      0.25 = coord(2/8)
    
    Abstract
    As information becomes more ubiquitous and the demands that searchers have on search systems grow, there is a need to support search behaviors beyond simple lookup. Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Exploratory search describes an information-seeking problem context that is open-ended, persistent, and multifaceted, and information-seeking processes that are opportunistic, iterative, and multitactical. Exploratory searchers aim to solve complex problems and develop enhanced mental capacities. Exploratory search systems support this through symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes. Exploratory search has gained prominence in recent years. There is an increased interest from the information retrieval, information science, and human-computer interaction communities in moving beyond the traditional turn-taking interaction model supported by major Web search engines, and toward support for human intelligence amplification and information use. In this lecture, we introduce exploratory search, relate it to relevant extant research, outline the features of exploratory search systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratory search. Exploratory search is a new frontier in the search domain and is becoming increasingly important in shaping our future world.
    Content
    Table of Contents: Introduction / Defining Exploratory Search / Related Work / Features of Exploratory Search Systems / Evaluation of Exploratory Search Systems / Future Directions and concluding Remarks
  2. Meadow, C.T.: Text information retrieval systems (1992) 0.01
    0.005098072 = product of:
      0.040784575 = sum of:
        0.040784575 = weight(_text_:work in 1519) [ClassicSimilarity], result of:
          0.040784575 = score(doc=1519,freq=4.0), product of:
            0.14223081 = queryWeight, product of:
              3.6703904 = idf(docFreq=3060, maxDocs=44218)
              0.03875087 = queryNorm
            0.28674924 = fieldWeight in 1519, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.6703904 = idf(docFreq=3060, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1519)
      0.125 = coord(1/8)
    
    Abstract
    This book's purpose is to teach people who will be searching or designing text retrieval systems how the systems work. For designers, it covers problems they will face and reviews currently available solutions to provide a basis for more advanced study. For the searcher its purpose is to describe why such systems work as they do. Text Information Retrieval Systems, Second Edition is primarily about computer-based retrieval systems, but the principles apply to non-mechanized ones as well. - Winner of the ASIS Best Information Science Book Award 2000!
  3. Grossman, D.A.; Frieder, O.: Information retrieval : algorithms and heuristics (2004) 0.00
    0.002883905 = product of:
      0.02307124 = sum of:
        0.02307124 = weight(_text_:work in 1486) [ClassicSimilarity], result of:
          0.02307124 = score(doc=1486,freq=2.0), product of:
            0.14223081 = queryWeight, product of:
              3.6703904 = idf(docFreq=3060, maxDocs=44218)
              0.03875087 = queryNorm
            0.16220987 = fieldWeight in 1486, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6703904 = idf(docFreq=3060, maxDocs=44218)
              0.03125 = fieldNorm(doc=1486)
      0.125 = coord(1/8)
    
    Abstract
    Interested in how an efficient search engine works? Want to know what algorithms are used to rank resulting documents in response to user requests? The authors answer these and other key information on retrieval design and implementation questions is provided. This book is not yet another high level text. Instead, algorithms are thoroughly described, making this book ideally suited for both computer science students and practitioners who work on search-related applications. As stated in the foreword, this book provides a current, broad, and detailed overview of the field and is the only one that does so. Examples are used throughout to illustrate the algorithms. The authors explain how a query is ranked against a document collection using either a single or a combination of retrieval strategies, and how an assortment of utilities are integrated into the query processing scheme to improve these rankings. Methods for building and compressing text indexes, querying and retrieving documents in multiple languages, and using parallel or distributed processing to expedite the search are likewise described. This edition is a major expansion of the one published in 1998. Neuaufl. 2005: Besides updating the entire book with current techniques, it includes new sections on language models, cross-language information retrieval, peer-to-peer processing, XML search, mediators, and duplicate document detection.
  4. Blair, D.C.: Language and representation in information retrieval (1991) 0.00
    0.002883905 = product of:
      0.02307124 = sum of:
        0.02307124 = weight(_text_:work in 1545) [ClassicSimilarity], result of:
          0.02307124 = score(doc=1545,freq=2.0), product of:
            0.14223081 = queryWeight, product of:
              3.6703904 = idf(docFreq=3060, maxDocs=44218)
              0.03875087 = queryNorm
            0.16220987 = fieldWeight in 1545, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6703904 = idf(docFreq=3060, maxDocs=44218)
              0.03125 = fieldNorm(doc=1545)
      0.125 = coord(1/8)
    
    Abstract
    Information or Document Retrieval is the subject of this book. It is not an introductory book, although it is self-contained in the sense that it is not necessary to have a background in the theory or practice of Information Retrieval in order to understand its arguments. The book presents, as clearly as possible, one particular perspective on Information Retrieval, and attempts to say that certain aspects of the theory or practice of the management of documents are more important than others. The majority of Information Retrieval research has been aimed at the more experimentally tractable small-scale systems, and although much of that work has added greatly to our understanding of Information Retrieval it is becoming increasingly apparent that retrieval systems with large data bases of documents are a fundamentally different genre of systems than small-scale systems. If this is so, which is the thesis of this book, then we must now study large information retrieval systems with the same rigor and intensity that we once studied small-scale systems. Hegel observed that the quantitative growth of any system caused qualitative changes to take place in its structure and processes.
  5. Gödert, W.; Hubrich, J.; Nagelschmidt, M.: Semantic knowledge representation for information retrieval (2014) 0.00
    0.0019688278 = product of:
      0.015750622 = sum of:
        0.015750622 = product of:
          0.031501245 = sum of:
            0.031501245 = weight(_text_:22 in 987) [ClassicSimilarity], result of:
              0.031501245 = score(doc=987,freq=2.0), product of:
                0.13569894 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.03875087 = queryNorm
                0.23214069 = fieldWeight in 987, 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=987)
          0.5 = coord(1/2)
      0.125 = coord(1/8)
    
    Date
    23. 7.2017 13:49:22