Search (142 results, page 1 of 8)

0.086345725 = product of:
  0.19427788 = sum of:
    0.10067343 = weight(_text_:applications in 6783) [ClassicSimilarity], result of:
      0.10067343 = score(doc=6783,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.5836958 = fieldWeight in 6783, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.09375 = fieldNorm(doc=6783)
    0.012701439 = weight(_text_:of in 6783) [ClassicSimilarity], result of:
      0.012701439 = score(doc=6783,freq=2.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.20732689 = fieldWeight in 6783, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.09375 = fieldNorm(doc=6783)
    0.0490556 = weight(_text_:systems in 6783) [ClassicSimilarity], result of:
      0.0490556 = score(doc=6783,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.4074492 = fieldWeight in 6783, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.09375 = fieldNorm(doc=6783)
    0.031847417 = product of:
      0.063694835 = sum of:
        0.063694835 = weight(_text_:22 in 6783) [ClassicSimilarity], result of:
          0.063694835 = score(doc=6783,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = queryNorm
            0.46428138 = fieldWeight in 6783, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=6783)
      0.5 = coord(1/2)
  0.44444445 = coord(4/9)

Footnote

A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997

Source

Knowledge-based systems. 10(1998) no.7, S.401-470

0.049125947 = product of:
  0.11053338 = sum of:
    0.04745791 = weight(_text_:applications in 362) [ClassicSimilarity], result of:
      0.04745791 = score(doc=362,freq=4.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.27515686 = fieldWeight in 362, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03125 = fieldNorm(doc=362)
    0.012701438 = weight(_text_:of in 362) [ClassicSimilarity], result of:
      0.012701438 = score(doc=362,freq=18.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.20732687 = fieldWeight in 362, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=362)
    0.023125032 = weight(_text_:systems in 362) [ClassicSimilarity], result of:
      0.023125032 = score(doc=362,freq=4.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.19207339 = fieldWeight in 362, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03125 = fieldNorm(doc=362)
    0.027249003 = weight(_text_:software in 362) [ClassicSimilarity], result of:
      0.027249003 = score(doc=362,freq=2.0), product of:
        0.15541996 = queryWeight, product of:
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.03917671 = queryNorm
        0.17532499 = fieldWeight in 362, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.03125 = fieldNorm(doc=362)
  0.44444445 = coord(4/9)

Abstract

Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.

Content

Inhalt: An Introduction to Text Mining.- Information Extraction from Text.- A Survey of Text Summarization Techniques.- A Survey of Text Clustering Algorithms.- Dimensionality Reduction and Topic Modeling.- A Survey of Text Classification Algorithms.- Transfer Learning for Text Mining.- Probabilistic Models for Text Mining.- Mining Text Streams.- Translingual Mining from Text Data.- Text Mining in Multimedia.- Text Analytics in Social Media.- A Survey of Opinion Mining and Sentiment Analysis.- Biomedical Text Mining: A Survey of Recent Progress.- Index.

LCSH

Multimedia systems

Subject

Multimedia systems

0.036399566 = product of:
  0.10919869 = sum of:
    0.041947264 = weight(_text_:applications in 5653) [ClassicSimilarity], result of:
      0.041947264 = score(doc=5653,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.2432066 = fieldWeight in 5653, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5653)
    0.019081537 = weight(_text_:of in 5653) [ClassicSimilarity], result of:
      0.019081537 = score(doc=5653,freq=26.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.31146988 = fieldWeight in 5653, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5653)
    0.048169892 = weight(_text_:software in 5653) [ClassicSimilarity], result of:
      0.048169892 = score(doc=5653,freq=4.0), product of:
        0.15541996 = queryWeight, product of:
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.03917671 = queryNorm
        0.30993375 = fieldWeight in 5653, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5653)
  0.33333334 = coord(3/9)

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.

0.031649083 = product of:
  0.09494725 = sum of:
    0.050336715 = weight(_text_:applications in 4716) [ClassicSimilarity], result of:
      0.050336715 = score(doc=4716,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.2918479 = fieldWeight in 4716, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.046875 = fieldNorm(doc=4716)
    0.020082738 = weight(_text_:of in 4716) [ClassicSimilarity], result of:
      0.020082738 = score(doc=4716,freq=20.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.32781258 = fieldWeight in 4716, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=4716)
    0.0245278 = weight(_text_:systems in 4716) [ClassicSimilarity], result of:
      0.0245278 = score(doc=4716,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.2037246 = fieldWeight in 4716, 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=4716)
  0.33333334 = coord(3/9)

Abstract

In this paper we give a synoptic view of the growth of the text processing technology of informatione xtraction (IE) whose function is to extract information about a pre-specified set of entities, relations or events from natural language texts and to record this information in structured representations called templates. Here we describe the nature of the IE task, review the history of the area from its origins in AI work in the 1960s and 70s till the present, discuss the techniques being used to carry out the task, describe application areas where IE systems are or are about to be at work, and conclude with a discussion of the challenges facing the area. What emerges is a picture of an exciting new text processing technology with a host of new applications, both on its own and in conjunction with other technologies, such as information retrieval, machine translation and data mining

Source

Journal of documentation. 54(1998) no.1, S.70-105

0.029691901 = product of:
  0.13361356 = sum of:
    0.118644774 = weight(_text_:applications in 7458) [ClassicSimilarity], result of:
      0.118644774 = score(doc=7458,freq=4.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.68789214 = fieldWeight in 7458, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.078125 = fieldNorm(doc=7458)
    0.014968789 = weight(_text_:of in 7458) [ClassicSimilarity], result of:
      0.014968789 = score(doc=7458,freq=4.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.24433708 = fieldWeight in 7458, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.078125 = fieldNorm(doc=7458)
  0.22222222 = coord(2/9)

Abstract

Gives an overview of data mining and knowledge discovery in databases. Clarifies how they are related both to each other and to related fields. Mentions real world applications data mining techniques, challenges involved in real world applications of knowledge discovery, and current and future research directions

0.025231833 = product of:
  0.05677162 = sum of:
    0.016778905 = weight(_text_:applications in 1789) [ClassicSimilarity], result of:
      0.016778905 = score(doc=1789,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.09728264 = fieldWeight in 1789, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.015625 = fieldNorm(doc=1789)
    0.018332949 = weight(_text_:of in 1789) [ClassicSimilarity], result of:
      0.018332949 = score(doc=1789,freq=150.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.2992506 = fieldWeight in 1789, product of:
          12.247449 = tf(freq=150.0), with freq of:
            150.0 = termFreq=150.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.015625 = fieldNorm(doc=1789)
    0.016351866 = weight(_text_:systems in 1789) [ClassicSimilarity], result of:
      0.016351866 = score(doc=1789,freq=8.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.1358164 = fieldWeight in 1789, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.015625 = fieldNorm(doc=1789)
    0.0053079035 = product of:
      0.010615807 = sum of:
        0.010615807 = weight(_text_:22 in 1789) [ClassicSimilarity], result of:
          0.010615807 = score(doc=1789,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = queryNorm
            0.07738023 = fieldWeight in 1789, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.015625 = fieldNorm(doc=1789)
      0.5 = coord(1/2)
  0.44444445 = coord(4/9)

Date

23. 3.2008 19:10:22

Footnote

Rez. in: JASIST 54(2003) no.9, S.905-906 (C.A. Badurek): "Visual approaches for knowledge discovery in very large databases are a prime research need for information scientists focused an extracting meaningful information from the ever growing stores of data from a variety of domains, including business, the geosciences, and satellite and medical imagery. This work presents a summary of research efforts in the fields of data mining, knowledge discovery, and data visualization with the goal of aiding the integration of research approaches and techniques from these major fields. The editors, leading computer scientists from academia and industry, present a collection of 32 papers from contributors who are incorporating visualization and data mining techniques through academic research as well application development in industry and government agencies. Information Visualization focuses upon techniques to enhance the natural abilities of humans to visually understand data, in particular, large-scale data sets. It is primarily concerned with developing interactive graphical representations to enable users to more intuitively make sense of multidimensional data as part of the data exploration process. It includes research from computer science, psychology, human-computer interaction, statistics, and information science. Knowledge Discovery in Databases (KDD) most often refers to the process of mining databases for previously unknown patterns and trends in data. Data mining refers to the particular computational methods or algorithms used in this process. The data mining research field is most related to computational advances in database theory, artificial intelligence and machine learning. This work compiles research summaries from these main research areas in order to provide "a reference work containing the collection of thoughts and ideas of noted researchers from the fields of data mining and data visualization" (p. 8). It addresses these areas in three main sections: the first an data visualization, the second an KDD and model visualization, and the last an using visualization in the knowledge discovery process. The seven chapters of Part One focus upon methodologies and successful techniques from the field of Data Visualization. Hoffman and Grinstein (Chapter 2) give a particularly good overview of the field of data visualization and its potential application to data mining. An introduction to the terminology of data visualization, relation to perceptual and cognitive science, and discussion of the major visualization display techniques are presented. Discussion and illustration explain the usefulness and proper context of such data visualization techniques as scatter plots, 2D and 3D isosurfaces, glyphs, parallel coordinates, and radial coordinate visualizations. Remaining chapters present the need for standardization of visualization methods, discussion of user requirements in the development of tools, and examples of using information visualization in addressing research problems.
In 13 chapters, Part Two provides an introduction to KDD, an overview of data mining techniques, and examples of the usefulness of data model visualizations. The importance of visualization throughout the KDD process is stressed in many of the chapters. In particular, the need for measures of visualization effectiveness, benchmarking for identifying best practices, and the use of standardized sample data sets is convincingly presented. Many of the important data mining approaches are discussed in this complementary context. Cluster and outlier detection, classification techniques, and rule discovery algorithms are presented as the basic techniques common to the KDD process. The potential effectiveness of using visualization in the data modeling process are illustrated in chapters focused an using visualization for helping users understand the KDD process, ask questions and form hypotheses about their data, and evaluate the accuracy and veracity of their results. The 11 chapters of Part Three provide an overview of the KDD process and successful approaches to integrating KDD, data mining, and visualization in complementary domains. Rhodes (Chapter 21) begins this section with an excellent overview of the relation between the KDD process and data mining techniques. He states that the "primary goals of data mining are to describe the existing data and to predict the behavior or characteristics of future data of the same type" (p. 281). These goals are met by data mining tasks such as classification, regression, clustering, summarization, dependency modeling, and change or deviation detection. Subsequent chapters demonstrate how visualization can aid users in the interactive process of knowledge discovery by graphically representing the results from these iterative tasks. Finally, examples of the usefulness of integrating visualization and data mining tools in the domain of business, imagery and text mining, and massive data sets are provided. This text concludes with a thorough and useful 17-page index and lengthy yet integrating 17-page summary of the academic and industrial backgrounds of the contributing authors. A 16-page set of color inserts provide a better representation of the visualizations discussed, and a URL provided suggests that readers may view all the book's figures in color on-line, although as of this submission date it only provides access to a summary of the book and its contents. The overall contribution of this work is its focus an bridging two distinct areas of research, making it a valuable addition to the Morgan Kaufmann Series in Database Management Systems. The editors of this text have met their main goal of providing the first textbook integrating knowledge discovery, data mining, and visualization. Although it contributes greatly to our under- standing of the development and current state of the field, a major weakness of this text is that there is no concluding chapter to discuss the contributions of the sum of these contributed papers or give direction to possible future areas of research. "Integration of expertise between two different disciplines is a difficult process of communication and reeducation. Integrating data mining and visualization is particularly complex because each of these fields in itself must draw an a wide range of research experience" (p. 300). Although this work contributes to the crossdisciplinary communication needed to advance visualization in KDD, a more formal call for an interdisciplinary research agenda in a concluding chapter would have provided a more satisfying conclusion to a very good introductory text.
With contributors almost exclusively from the computer science field, the intended audience of this work is heavily slanted towards a computer science perspective. However, it is highly readable and provides introductory material that would be useful to information scientists from a variety of domains. Yet, much interesting work in information visualization from other fields could have been included giving the work more of an interdisciplinary perspective to complement their goals of integrating work in this area. Unfortunately, many of the application chapters are these, shallow, and lack complementary illustrations of visualization techniques or user interfaces used. However, they do provide insight into the many applications being developed in this rapidly expanding field. The authors have successfully put together a highly useful reference text for the data mining and information visualization communities. Those interested in a good introduction and overview of complementary research areas in these fields will be satisfied with this collection of papers. The focus upon integrating data visualization with data mining complements texts in each of these fields, such as Advances in Knowledge Discovery and Data Mining (Fayyad et al., MIT Press) and Readings in Information Visualization: Using Vision to Think (Card et. al., Morgan Kauffman). This unique work is a good starting point for future interaction between researchers in the fields of data visualization and data mining and makes a good accompaniment for a course focused an integrating these areas or to the main reference texts in these fields."

LCSH

Knowledge acquisition (Expert systems)

Series

Morgan Kaufmann series in data management systems

Subject

Knowledge acquisition (Expert systems)

0.023708593 = product of:
  0.071125776 = sum of:
    0.022453185 = weight(_text_:of in 5011) [ClassicSimilarity], result of:
      0.022453185 = score(doc=5011,freq=36.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.36650562 = fieldWeight in 5011, product of:
          6.0 = tf(freq=36.0), with freq of:
            36.0 = termFreq=36.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5011)
    0.03540283 = weight(_text_:systems in 5011) [ClassicSimilarity], result of:
      0.03540283 = score(doc=5011,freq=6.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.29405114 = fieldWeight in 5011, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5011)
    0.013269759 = product of:
      0.026539518 = sum of:
        0.026539518 = weight(_text_:22 in 5011) [ClassicSimilarity], result of:
          0.026539518 = score(doc=5011,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = 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)
  0.33333334 = coord(3/9)

Abstract

The present challenge faced by scientists working with Big Data comes in the overwhelming volume and level of detail provided by current data sets. Exceeding traditional empirical approaches, Big Data opens a new perspective on scientific work in which data comes to play a role in the development of the scientific problematic to be developed. Addressing this reconfiguration of our relationship with data through readings of Wittgenstein, Macherey, and Popper, we propose a picture of science that encourages scientists to engage with the data in a direct way, using the data itself as an instrument for scientific investigation. Using GIS as a theme, we develop the concept of cyber-human systems of thought and understanding to bridge the divide between representative (theoretical) thinking and (non-theoretical) data-driven science. At the foundation of these systems, we invoke the concept of the "semantic pixel" to establish a logical and virtual space linking data and the work of scientists. It is with this discussion of the relationship between analysts in their pursuit of knowledge and the rise of Big Data that this present discussion of the philosophical foundations of Big Data addresses the central questions raised by social informatics research.

Date

7. 3.2019 16:32:22

Footnote

Beitrag eines Special issue on social informatics of knowledge

Source

Journal of the Association for Information Science and Technology. 70(2019) no.4, S.402-411

0.023628347 = product of:
  0.07088504 = sum of:
    0.016735615 = weight(_text_:of in 668) [ClassicSimilarity], result of:
      0.016735615 = score(doc=668,freq=20.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.27317715 = fieldWeight in 668, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=668)
    0.040879667 = weight(_text_:systems in 668) [ClassicSimilarity], result of:
      0.040879667 = score(doc=668,freq=8.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.339541 = fieldWeight in 668, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=668)
    0.013269759 = product of:
      0.026539518 = sum of:
        0.026539518 = weight(_text_:22 in 668) [ClassicSimilarity], result of:
          0.026539518 = score(doc=668,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = 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)
  0.33333334 = coord(3/9)

Abstract

20th century massification of higher education and research in academia is said to have produced structurally stratified higher education systems in many countries. Most manifestly, the research mission of universities appears to be divisive. Authors have claimed that the Swedish system, while formally unified, has developed into a binary state, and statistics seem to support this conclusion. This article makes use of a comprehensive statistical data source on Swedish higher education institutions to illustrate stratification, and uses literature on Swedish research policy history to contextualize the statistics. Highlighting the opportunities as well as constraints of the data, the article argues that there is great merit in combining statistics with a qualitative analysis when studying the structural characteristics of national higher education systems. Not least the article shows that it is an over-simplification to describe the Swedish system as binary; the stratification is more complex. On basis of the analysis, the article also argues that while global trends certainly influence national developments, higher education systems have country-specific features that may enrich the understanding of how systems evolve and therefore should be analyzed as part of a broader study of the increasingly globalized academic system.

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

0.02226542 = product of:
  0.06679626 = sum of:
    0.01960283 = weight(_text_:of in 2908) [ClassicSimilarity], result of:
      0.01960283 = score(doc=2908,freq=14.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.31997898 = fieldWeight in 2908, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2908)
    0.028615767 = weight(_text_:systems in 2908) [ClassicSimilarity], result of:
      0.028615767 = score(doc=2908,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.23767869 = fieldWeight in 2908, 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=2908)
    0.018577661 = product of:
      0.037155323 = sum of:
        0.037155323 = weight(_text_:22 in 2908) [ClassicSimilarity], result of:
          0.037155323 = score(doc=2908,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = 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)
  0.33333334 = coord(3/9)

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

0.020800991 = product of:
  0.06240297 = sum of:
    0.008467626 = weight(_text_:of in 1270) [ClassicSimilarity], result of:
      0.008467626 = score(doc=1270,freq=2.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.13821793 = fieldWeight in 1270, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=1270)
    0.03270373 = weight(_text_:systems in 1270) [ClassicSimilarity], result of:
      0.03270373 = score(doc=1270,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.2716328 = fieldWeight in 1270, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0625 = fieldNorm(doc=1270)
    0.021231614 = product of:
      0.042463228 = sum of:
        0.042463228 = weight(_text_:22 in 1270) [ClassicSimilarity], result of:
          0.042463228 = score(doc=1270,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = 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)
  0.33333334 = coord(3/9)

Abstract

Current algorithms for finding associations among the attributes describing data in a database have a number of shortcomings. Presents a novel method for association generation, that answers all desiderata. The method is different from all existing algorithms and especially suitable to textual databases with binary attributes. Uses subword trees for quick indexing into the required database statistics. Tests the algorithm on the Reuters-22173 database with satisfactory results

Source

Information systems. 22(1997) nos.5/6, S.333-347

0.020093452 = product of:
  0.060280353 = sum of:
    0.03355781 = weight(_text_:applications in 354) [ClassicSimilarity], result of:
      0.03355781 = score(doc=354,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.19456528 = fieldWeight in 354, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03125 = fieldNorm(doc=354)
    0.010370681 = weight(_text_:of in 354) [ClassicSimilarity], result of:
      0.010370681 = score(doc=354,freq=12.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.16928169 = fieldWeight in 354, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=354)
    0.016351866 = weight(_text_:systems in 354) [ClassicSimilarity], result of:
      0.016351866 = score(doc=354,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.1358164 = fieldWeight in 354, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03125 = fieldNorm(doc=354)
  0.33333334 = coord(3/9)

Abstract

Web mining aims to discover useful information and knowledge from the Web hyperlink structure, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semistructured and unstructured nature of the Web data and its heterogeneity. It has also developed many of its own algorithms and techniques. Liu has written a comprehensive text on Web data mining. Key topics of structure mining, content mining, and usage mining are covered both in breadth and in depth. His book brings together all the essential concepts and algorithms from related areas such as data mining, machine learning, and text processing to form an authoritative and coherent text. The book offers a rich blend of theory and practice, addressing seminal research ideas, as well as examining the technology from a practical point of view. It is suitable for students, researchers and practitioners interested in Web mining both as a learning text and a reference book. Lecturers can readily use it for classes on data mining, Web mining, and Web search. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.

Series

Data-centric systems and applications

0.01981098 = product of:
  0.08914941 = sum of:
    0.07118686 = weight(_text_:applications in 4242) [ClassicSimilarity], result of:
      0.07118686 = score(doc=4242,freq=4.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.41273528 = fieldWeight in 4242, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.046875 = fieldNorm(doc=4242)
    0.017962547 = weight(_text_:of in 4242) [ClassicSimilarity], result of:
      0.017962547 = score(doc=4242,freq=16.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.2932045 = fieldWeight in 4242, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=4242)
  0.22222222 = coord(2/9)

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

0.01925707 = product of:
  0.08665682 = sum of:
    0.0726548 = weight(_text_:applications in 5997) [ClassicSimilarity], result of:
      0.0726548 = score(doc=5997,freq=6.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.42124623 = fieldWeight in 5997, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5997)
    0.0140020205 = weight(_text_:of in 5997) [ClassicSimilarity], result of:
      0.0140020205 = score(doc=5997,freq=14.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.22855641 = fieldWeight in 5997, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5997)
  0.22222222 = coord(2/9)

Abstract

Given the huge amount of information in the internet and in practically every domain of knowledge that we are facing today, knowledge discovery calls for automation. The book deals with methods from classification and data analysis that respond effectively to this rapidly growing challenge. The interested reader will find new methodological insights as well as applications in economics, management science, finance, and marketing, and in pattern recognition, biology, health, and archaeology.

Content

Data Analysis, Statistics, and Classification.- Pattern Recognition and Automation.- Data Mining, Information Processing, and Automation.- New Media, Web Mining, and Automation.- Applications in Management Science, Finance, and Marketing.- Applications in Medicine, Biology, Archaeology, and Others.- Author Index.- Subject Index.

Series

Proceedings of the ... annual conference of the Gesellschaft für Klassifikation e.V. ; 24)(Studies in classification, data analysis, and knowledge organization

0.01599093 = product of:
  0.07195918 = sum of:
    0.012963352 = weight(_text_:of in 804) [ClassicSimilarity], result of:
      0.012963352 = score(doc=804,freq=12.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.21160212 = fieldWeight in 804, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=804)
    0.058995828 = weight(_text_:software in 804) [ClassicSimilarity], result of:
      0.058995828 = score(doc=804,freq=6.0), product of:
        0.15541996 = queryWeight, product of:
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.03917671 = queryNorm
        0.37958977 = fieldWeight in 804, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.0390625 = fieldNorm(doc=804)
  0.22222222 = coord(2/9)

Abstract

Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.

Classification

ST 270 Informatik / Monographien / Software und -entwicklung / Datenbanken, Datenbanksysteme, Data base management, Informationssysteme

RVK

ST 270 Informatik / Monographien / Software und -entwicklung / Datenbanken, Datenbanksysteme, Data base management, Informationssysteme

0.015177614 = product of:
  0.06829926 = sum of:
    0.050336715 = weight(_text_:applications in 3144) [ClassicSimilarity], result of:
      0.050336715 = score(doc=3144,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.2918479 = fieldWeight in 3144, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.046875 = fieldNorm(doc=3144)
    0.017962547 = weight(_text_:of in 3144) [ClassicSimilarity], result of:
      0.017962547 = score(doc=3144,freq=16.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.2932045 = fieldWeight in 3144, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3144)
  0.22222222 = coord(2/9)

Abstract

Data Mining describes a technology that discovers non-trivial hidden patterns in a large collection of data. Although this technology has a tremendous impact on our lives, the invaluable contributions of this invisible technology often go unnoticed. This paper discusses advances in data mining while focusing on the emerging data mining capability. Such data mining applications perform multidimensional mining on a wide variety of heterogeneous data sources, providing solutions to many unresolved problems. This paper also highlights the advantages and disadvantages arising from the ever-expanding scope of data mining. Data Mining augments human intelligence by equipping us with a wealth of knowledge and by empowering us to perform our daily tasks better. As the mining scope and capacity increases, users and organizations become more willing to compromise privacy. The huge data stores of the 'master miners' allow them to gain deep insights into individual lifestyles and their social and behavioural patterns. Data integration and analysis capability of combining business and financial trends together with the ability to deterministically track market changes will drastically affect our lives.

0.0147717865 = product of:
  0.06647304 = sum of:
    0.011975031 = weight(_text_:of in 3822) [ClassicSimilarity], result of:
      0.011975031 = score(doc=3822,freq=4.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.19546966 = fieldWeight in 3822, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=3822)
    0.054498006 = weight(_text_:software in 3822) [ClassicSimilarity], result of:
      0.054498006 = score(doc=3822,freq=2.0), product of:
        0.15541996 = queryWeight, product of:
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.03917671 = queryNorm
        0.35064998 = fieldWeight in 3822, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.0625 = fieldNorm(doc=3822)
  0.22222222 = coord(2/9)

Abstract

The volume presents 26 revised papers corresponding to the oral presentations given at the conference, also included are refereed papers corresponding to the 30 poster presentations. These papers were selected from a total of 73 full draft submissions. The papers are organized in topical sections on rule evaluation, visualization, association rules and text mining, KDD process and software, tree construction, sequential and spatial data mining, and attribute selection

0.01464726 = product of:
  0.06591267 = sum of:
    0.04745791 = weight(_text_:applications in 2222) [ClassicSimilarity], result of:
      0.04745791 = score(doc=2222,freq=4.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.27515686 = fieldWeight in 2222, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03125 = fieldNorm(doc=2222)
    0.018454762 = weight(_text_:of in 2222) [ClassicSimilarity], result of:
      0.018454762 = score(doc=2222,freq=38.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.30123898 = fieldWeight in 2222, product of:
          6.164414 = tf(freq=38.0), with freq of:
            38.0 = termFreq=38.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=2222)
  0.22222222 = coord(2/9)

Footnote

Rez. in: JASIST 55(2004) no.3, S.275-276 (C. Chen): "This is a book about finding significant statistical patterns on the Web - in particular, patterns that are associated with hypertext documents, topics, hyperlinks, and queries. The term pattern in this book refers to dependencies among such items. On the one hand, the Web contains useful information an just about every topic under the sun. On the other hand, just like searching for a needle in a haystack, one would need powerful tools to locate useful information an the vast land of the Web. Soumen Chakrabarti's book focuses an a wide range of techniques for machine learning and data mining an the Web. The goal of the book is to provide both the technical Background and tools and tricks of the trade of Web content mining. Much of the technical content reflects the state of the art between 1995 and 2002. The targeted audience is researchers and innovative developers in this area, as well as newcomers who intend to enter this area. The book begins with an introduction chapter. The introduction chapter explains fundamental concepts such as crawling and indexing as well as clustering and classification. The remaining eight chapters are organized into three parts: i) infrastructure, ii) learning and iii) applications.
Part I, Infrastructure, has two chapters: Chapter 2 on crawling the Web and Chapter 3 an Web search and information retrieval. The second part of the book, containing chapters 4, 5, and 6, is the centerpiece. This part specifically focuses an machine learning in the context of hypertext. Part III is a collection of applications that utilize the techniques described in earlier chapters. Chapter 7 is an social network analysis. Chapter 8 is an resource discovery. Chapter 9 is an the future of Web mining. Overall, this is a valuable reference book for researchers and developers in the field of Web mining. It should be particularly useful for those who would like to design and probably code their own Computer programs out of the equations and pseudocodes an most of the pages. For a student, the most valuable feature of the book is perhaps the formal and consistent treatments of concepts across the board. For what is behind and beyond the technical details, one has to either dig deeper into the bibliographic notes at the end of each chapter, or resort to more in-depth analysis of relevant subjects in the literature. lf you are looking for successful stories about Web mining or hard-way-learned lessons of failures, this is not the book."

0.014008479 = product of:
  0.063038155 = sum of:
    0.050336715 = weight(_text_:applications in 1611) [ClassicSimilarity], result of:
      0.050336715 = score(doc=1611,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.2918479 = fieldWeight in 1611, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.046875 = fieldNorm(doc=1611)
    0.012701439 = weight(_text_:of in 1611) [ClassicSimilarity], result of:
      0.012701439 = score(doc=1611,freq=8.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.20732689 = fieldWeight in 1611, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1611)
  0.22222222 = coord(2/9)

Abstract

"Garbage in, garbage out" is a well-known phrase in computer analysis, and one that comes to mind when mining Web data to draw conclusions about Web users. The challenge is that data analysts wish to infer patterns of client-side behavior from server-side data. However, because only a fraction of the user's actions ever reaches the Web server, analysts must rely an incomplete data. In this paper, we propose a client-side monitoring system that is unobtrusive and supports flexible data collection. Moreover, the proposed framework encompasses client-side applications beyond the Web browser. Expanding monitoring beyond the browser to incorporate standard office productivity tools enables analysts to derive a much richer and more accurate picture of user behavior an the Web.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.7, S.625-637

0.013784983 = product of:
  0.062032424 = sum of:
    0.05407057 = weight(_text_:software in 1178) [ClassicSimilarity], result of:
      0.05407057 = score(doc=1178,freq=14.0), product of:
        0.15541996 = queryWeight, product of:
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.03917671 = queryNorm
        0.34789976 = fieldWeight in 1178, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.0234375 = fieldNorm(doc=1178)
    0.007961854 = product of:
      0.015923709 = sum of:
        0.015923709 = weight(_text_:22 in 1178) [ClassicSimilarity], result of:
          0.015923709 = score(doc=1178,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = 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)
  0.22222222 = coord(2/9)

Abstract

US-Behörden wollen mit spezieller Software die Datenspuren von Terroristen finden. Wie das funktionieren könnte, zeigen Programmen, die heute schon für Unternehmen Kunden und Mitarbeiter analysieren.

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."

0.013763658 = product of:
  0.06193646 = sum of:
    0.021062955 = weight(_text_:of in 3704) [ClassicSimilarity], result of:
      0.021062955 = score(doc=3704,freq=22.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.34381276 = fieldWeight in 3704, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3704)
    0.040873505 = weight(_text_:software in 3704) [ClassicSimilarity], result of:
      0.040873505 = score(doc=3704,freq=2.0), product of:
        0.15541996 = queryWeight, product of:
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.03917671 = queryNorm
        0.2629875 = fieldWeight in 3704, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.046875 = fieldNorm(doc=3704)
  0.22222222 = coord(2/9)

Abstract

Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. The authors discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices (Thomson Reuters) and Scopus (Elsevier), on the one hand, and these various visualization tools on the other. At the level of city names, the global map can be drawn reliably on the basis of the available address information. At the level of the names of organizations and institutes, there are problems of unification both in the ISI databases and with Scopus. Pajek enables a combination of visualization and statistical analysis, whereas the Google Maps and its derivatives provide superior tools on the Internet.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1622-1634