Search (137 results, page 1 of 7)

0.10871318 = product of:
  0.18118863 = sum of:
    0.02251335 = weight(_text_:of in 5481) [ClassicSimilarity], result of:
      0.02251335 = score(doc=5481,freq=12.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.29624295 = fieldWeight in 5481, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5481)
    0.09616026 = weight(_text_:subject in 5481) [ClassicSimilarity], result of:
      0.09616026 = score(doc=5481,freq=8.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.5532265 = fieldWeight in 5481, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5481)
    0.06251501 = product of:
      0.12503003 = sum of:
        0.12503003 = weight(_text_:headings in 5481) [ClassicSimilarity], result of:
          0.12503003 = score(doc=5481,freq=4.0), product of:
            0.23569997 = queryWeight, product of:
              4.849944 = idf(docFreq=940, maxDocs=44218)
              0.04859849 = queryNorm
            0.5304626 = fieldWeight in 5481, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.849944 = idf(docFreq=940, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5481)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.

0.036733653 = product of:
  0.09183413 = sum of:
    0.07213905 = weight(_text_:list in 1867) [ClassicSimilarity], result of:
      0.07213905 = score(doc=1867,freq=2.0), product of:
        0.25191793 = queryWeight, product of:
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.04859849 = queryNorm
        0.2863593 = fieldWeight in 1867, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1867)
    0.019695079 = weight(_text_:of in 1867) [ClassicSimilarity], result of:
      0.019695079 = score(doc=1867,freq=18.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.25915858 = fieldWeight in 1867, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1867)
  0.4 = coord(2/5)

Abstract

This research creates an intelligent agent for automated collection development in a digital library setting. It uses a predictive model based an facets of each Web page to select scholarly works. The criteria came from the academic library selection literature, and a Delphi study was used to refine the list to 41 criteria. A Perl program was designed to analyze a Web page for each criterion and applied to a large collection of scholarly and nonscholarly Web pages. Bibliomining, or data mining for libraries, was then used to create different classification models. Four techniques were used: logistic regression, nonparametric discriminant analysis, classification trees, and neural networks. Accuracy and return were used to judge the effectiveness of each model an test datasets. In addition, a set of problematic pages that were difficult to classify because of their similarity to scholarly research was gathered and classified using the models. The resulting models could be used in the selection process to automatically create a digital library of Webbased scholarly research works. In addition, the technique can be extended to create a digital library of any type of structured electronic information.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.12, S.1081-1090

0.036733653 = product of:
  0.09183413 = sum of:
    0.07213905 = weight(_text_:list in 3607) [ClassicSimilarity], result of:
      0.07213905 = score(doc=3607,freq=2.0), product of:
        0.25191793 = queryWeight, product of:
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.04859849 = queryNorm
        0.2863593 = fieldWeight in 3607, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3607)
    0.019695079 = weight(_text_:of in 3607) [ClassicSimilarity], result of:
      0.019695079 = score(doc=3607,freq=18.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.25915858 = fieldWeight in 3607, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3607)
  0.4 = coord(2/5)

Abstract

The abundance of medical resources has encouraged the development of systems that allow for efficient searches of information in large medical image data sets. State-of-the-art image retrieval models are classified into three categories: content-based (visual) models, textual models, and combined models. Content-based models use visual features to answer image queries, textual image retrieval models use word matching to answer textual queries, and combined image retrieval models, use both textual and visual features to answer queries. Nevertheless, most of previous works in this field have used the same image retrieval model independently of the query type. In this article, we define a list of generic and specific medical query features and exploit them in an association rule mining technique to discover correlations between query features and image retrieval models. Based on these rules, we propose to use an associative classifier (NaiveClass) to find the best suitable retrieval model given a new textual query. We also propose a second associative classifier (SmartClass) to select the most appropriate default class for the query. Experiments are performed on Medical ImageCLEF queries from 2008 to 2012 to evaluate the impact of the proposed query features on the classification performance. The results show that combining our proposed specific and generic query features is effective in query classification.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.5, S.1323-1334

0.036283102 = product of:
  0.09070775 = sum of:
    0.07213905 = weight(_text_:list in 597) [ClassicSimilarity], result of:
      0.07213905 = score(doc=597,freq=2.0), product of:
        0.25191793 = queryWeight, product of:
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.04859849 = queryNorm
        0.2863593 = fieldWeight in 597, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.0390625 = fieldNorm(doc=597)
    0.0185687 = weight(_text_:of in 597) [ClassicSimilarity], result of:
      0.0185687 = score(doc=597,freq=16.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.24433708 = fieldWeight in 597, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=597)
  0.4 = coord(2/5)

Abstract

With the overwhelming volume of information, the task of finding relevant information on a given topic on the Web is becoming increasingly difficult. Web search engines hence become one of the most popular solutions available on the Web. However, it has never been easy for novice users to organize and represent their information needs using simple queries. Users have to keep modifying their input queries until they get expected results. Therefore, it is often desirable for search engines to give suggestions on related queries to users. Besides, by identifying those related queries, search engines can potentially perform optimizations on their systems, such as query expansion and file indexing. In this work we propose a method that suggests a list of related queries given an initial input query. The related queries are based in the query log of previously submitted queries by human users, which can be identified using an enhanced model of association rules. Users can utilize the suggested related queries to tune or redirect the search process. Our method not only discovers the related queries, but also ranks them according to the degree of their relatedness. Unlike many other rival techniques, it also performs reasonably well on less frequent input queries.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1871-1883

0.028895525 = product of:
  0.07223881 = sum of:
    0.023670541 = weight(_text_:of in 5653) [ClassicSimilarity], result of:
      0.023670541 = score(doc=5653,freq=26.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = 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)
    0.048568267 = weight(_text_:subject in 5653) [ClassicSimilarity], result of:
      0.048568267 = score(doc=5653,freq=4.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.27942157 = fieldWeight in 5653, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5653)
  0.4 = coord(2/5)

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.

0.026449626 = product of:
  0.06612407 = sum of:
    0.024912525 = weight(_text_:of in 1142) [ClassicSimilarity], result of:
      0.024912525 = score(doc=1142,freq=20.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.32781258 = fieldWeight in 1142, 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=1142)
    0.041211538 = weight(_text_:subject in 1142) [ClassicSimilarity], result of:
      0.041211538 = score(doc=1142,freq=2.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.23709705 = fieldWeight in 1142, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.046875 = fieldNorm(doc=1142)
  0.4 = coord(2/5)

Abstract

We provide a brief, non-technical introduction to the text mining methodology known as "topic modeling." We summarize the theory and background of the method and discuss what kinds of things are found by topic models. Using a text corpus comprised of the eight articles from the special issue of Poetics on the subject of topic models, we run a topic model on these articles, both as a way to introduce the methodology and also to help summarize some of the ways in which social and cultural scientists are using topic models. We review some of the critiques and debates over the use of the method and finally, we link these developments back to some of the original innovations in the field of content analysis that were pioneered by Harold D. Lasswell and colleagues during and just after World War II.

0.02482194 = product of:
  0.06205485 = sum of:
    0.020843314 = weight(_text_:of in 720) [ClassicSimilarity], result of:
      0.020843314 = score(doc=720,freq=14.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.2742677 = fieldWeight in 720, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=720)
    0.041211538 = weight(_text_:subject in 720) [ClassicSimilarity], result of:
      0.041211538 = score(doc=720,freq=2.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.23709705 = fieldWeight in 720, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.046875 = fieldNorm(doc=720)
  0.4 = coord(2/5)

Abstract

This project brought together undergraduate students in Computer Science with librarians to mine abstracts of articles from the Texas A&M University Libraries' institutional repository, OAKTrust, in order to probe the creation of new metadata to improve discovery and use. The mining operation task consisted simply of classifying the articles into two categories of research type: basic research ("for understanding," "curiosity-based," or "knowledge-based") and applied research ("use-based"). These categories are fundamental especially for funders but are also important to researchers. The mining-to-classification steps took several iterations, but ultimately, we achieved good results with the toolkit BERT (Bidirectional Encoder Representations from Transformers). The project and its workflows represent a preview of what may lie ahead in the future of crafting metadata using text mining techniques to enhance discoverability.

Footnote

Teil eines Themenheftes: Artificial intelligence (AI) and automated processes for subject sccess

0.02420348 = product of:
  0.0605087 = sum of:
    0.019297158 = weight(_text_:of in 3884) [ClassicSimilarity], result of:
      0.019297158 = score(doc=3884,freq=12.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.25392252 = fieldWeight in 3884, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3884)
    0.041211538 = weight(_text_:subject in 3884) [ClassicSimilarity], result of:
      0.041211538 = score(doc=3884,freq=2.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.23709705 = fieldWeight in 3884, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.046875 = fieldNorm(doc=3884)
  0.4 = coord(2/5)

Abstract

Techniques for multidimensional scaling visualize objects as points in a low-dimensional metric map. As a result, the visualizations are subject to the fundamental limitations of metric spaces. These limitations prevent multidimensional scaling from faithfully representing non-metric similarity data such as word associations or event co-occurrences. In particular, multidimensional scaling cannot faithfully represent intransitive pairwise similarities in a visualization, and it cannot faithfully visualize "central" objects. In this paper, we present an extension of a recently proposed multidimensional scaling technique called t-SNE. The extension aims to address the problems of traditional multidimensional scaling techniques when these techniques are used to visualize non-metric similarities. The new technique, called multiple maps t-SNE, alleviates these problems by constructing a collection of maps that reveal complementary structure in the similarity data. We apply multiple maps t-SNE to a large data set of word association data and to a data set of NIPS co-authorships, demonstrating its ability to successfully visualize non-metric similarities.

0.022105044 = product of:
  0.05526261 = sum of:
    0.015756065 = weight(_text_:of in 6783) [ClassicSimilarity], result of:
      0.015756065 = score(doc=6783,freq=2.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = 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)
    0.039506543 = product of:
      0.07901309 = sum of:
        0.07901309 = weight(_text_:22 in 6783) [ClassicSimilarity], result of:
          0.07901309 = score(doc=6783,freq=2.0), product of:
            0.17018363 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04859849 = 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)
  0.4 = coord(2/5)

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

0.020684952 = product of:
  0.05171238 = sum of:
    0.017369429 = weight(_text_:of in 5997) [ClassicSimilarity], result of:
      0.017369429 = score(doc=5997,freq=14.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = 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)
    0.034342952 = weight(_text_:subject in 5997) [ClassicSimilarity], result of:
      0.034342952 = score(doc=5997,freq=2.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.19758089 = fieldWeight in 5997, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5997)
  0.4 = coord(2/5)

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

0.018945074 = product of:
  0.047362685 = sum of:
    0.024317201 = weight(_text_:of in 2908) [ClassicSimilarity], result of:
      0.024317201 = score(doc=2908,freq=14.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = 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)
    0.023045486 = product of:
      0.04609097 = sum of:
        0.04609097 = weight(_text_:22 in 2908) [ClassicSimilarity], result of:
          0.04609097 = score(doc=2908,freq=2.0), product of:
            0.17018363 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04859849 = 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)
  0.4 = coord(2/5)

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

0.018938314 = product of:
  0.047345784 = sum of:
    0.021008085 = weight(_text_:of in 1737) [ClassicSimilarity], result of:
      0.021008085 = score(doc=1737,freq=8.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.27643585 = fieldWeight in 1737, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=1737)
    0.026337698 = product of:
      0.052675396 = sum of:
        0.052675396 = weight(_text_:22 in 1737) [ClassicSimilarity], result of:
          0.052675396 = score(doc=1737,freq=2.0), product of:
            0.17018363 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04859849 = 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)
  0.4 = coord(2/5)

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

0.017725645 = product of:
  0.04431411 = sum of:
    0.02785305 = weight(_text_:of in 5011) [ClassicSimilarity], result of:
      0.02785305 = score(doc=5011,freq=36.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = 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)
    0.016461061 = product of:
      0.032922123 = sum of:
        0.032922123 = weight(_text_:22 in 5011) [ClassicSimilarity], result of:
          0.032922123 = score(doc=5011,freq=2.0), product of:
            0.17018363 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04859849 = 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)
  0.4 = coord(2/5)

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

0.015681192 = product of:
  0.03920298 = sum of:
    0.022741921 = weight(_text_:of in 1605) [ClassicSimilarity], result of:
      0.022741921 = score(doc=1605,freq=24.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.2992506 = fieldWeight in 1605, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1605)
    0.016461061 = product of:
      0.032922123 = sum of:
        0.032922123 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
          0.032922123 = score(doc=1605,freq=2.0), product of:
            0.17018363 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04859849 = 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)
  0.4 = coord(2/5)

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

0.0148885995 = product of:
  0.0372215 = sum of:
    0.020760437 = weight(_text_:of in 668) [ClassicSimilarity], result of:
      0.020760437 = score(doc=668,freq=20.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = 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)
    0.016461061 = product of:
      0.032922123 = sum of:
        0.032922123 = weight(_text_:22 in 668) [ClassicSimilarity], result of:
          0.032922123 = score(doc=668,freq=2.0), product of:
            0.17018363 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04859849 = 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)
  0.4 = coord(2/5)

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

0.014736697 = product of:
  0.036841743 = sum of:
    0.010504043 = weight(_text_:of in 1270) [ClassicSimilarity], result of:
      0.010504043 = score(doc=1270,freq=2.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = 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)
    0.026337698 = product of:
      0.052675396 = sum of:
        0.052675396 = weight(_text_:22 in 1270) [ClassicSimilarity], result of:
          0.052675396 = score(doc=1270,freq=2.0), product of:
            0.17018363 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04859849 = 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)
  0.4 = coord(2/5)

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

0.011730539 = product of:
  0.029326346 = sum of:
    0.022741921 = weight(_text_:of in 1789) [ClassicSimilarity], result of:
      0.022741921 = score(doc=1789,freq=150.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = 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)
    0.0065844245 = product of:
      0.013168849 = sum of:
        0.013168849 = weight(_text_:22 in 1789) [ClassicSimilarity], result of:
          0.013168849 = score(doc=1789,freq=2.0), product of:
            0.17018363 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04859849 = 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)
  0.4 = coord(2/5)

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."

0.0092181945 = product of:
  0.04609097 = sum of:
    0.04609097 = product of:
      0.09218194 = sum of:
        0.09218194 = weight(_text_:22 in 4577) [ClassicSimilarity], result of:
          0.09218194 = score(doc=4577,freq=2.0), product of:
            0.17018363 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04859849 = 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)
  0.2 = coord(1/5)

Date

2. 4.2000 18:01:22

0.008238532 = product of:
  0.02059633 = sum of:
    0.00742748 = weight(_text_:of in 1507) [ClassicSimilarity], result of:
      0.00742748 = score(doc=1507,freq=4.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.09773483 = fieldWeight in 1507, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=1507)
    0.013168849 = product of:
      0.026337698 = sum of:
        0.026337698 = weight(_text_:22 in 1507) [ClassicSimilarity], result of:
          0.026337698 = score(doc=1507,freq=2.0), product of:
            0.17018363 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04859849 = queryNorm
            0.15476047 = fieldWeight in 1507, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=1507)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Wir werden einmal die Grundlagen des Text-Mining-Systems bei IBM darstellen, dann werden wir das Projekt etwas umfangreicher und deutlicher darstellen, da kennen wir uns aus. Von daher haben wir zwei Teile, einmal Heidelberg, einmal Hamburg. Noch einmal zur Technologie. Text-Mining ist eine von IBM entwickelte Technologie, die in einer besonderen Ausformung und Programmierung für uns zusammengestellt wurde. Das Projekt hieß bei uns lange Zeit DocText Miner und heißt seit einiger Zeit auf Vorschlag von IBM DocCat, das soll eine Abkürzung für Document-Categoriser sein, sie ist ja auch nett und anschaulich. Wir fangen an mit Text-Mining, das bei IBM in Heidelberg entwickelt wurde. Die verstehen darunter das automatische Indexieren als eine Instanz, also einen Teil von Text-Mining. Probleme werden dabei gezeigt, und das Text-Mining ist eben eine Methode zur Strukturierung von und der Suche in großen Dokumentenmengen, die Extraktion von Informationen und, das ist der hohe Anspruch, von impliziten Zusammenhängen. Das letztere sei dahingestellt. IBM macht das quantitativ, empirisch, approximativ und schnell. das muss man wirklich sagen. Das Ziel, und das ist ganz wichtig für unser Projekt gewesen, ist nicht, den Text zu verstehen, sondern das Ergebnis dieser Verfahren ist, was sie auf Neudeutsch a bundle of words, a bag of words nennen, also eine Menge von bedeutungstragenden Begriffen aus einem Text zu extrahieren, aufgrund von Algorithmen, also im Wesentlichen aufgrund von Rechenoperationen. Es gibt eine ganze Menge von linguistischen Vorstudien, ein wenig Linguistik ist auch dabei, aber nicht die Grundlage der ganzen Geschichte. Was sie für uns gemacht haben, ist also die Annotierung von Pressetexten für unsere Pressedatenbank. Für diejenigen, die es noch nicht kennen: Gruner + Jahr führt eine Textdokumentation, die eine Datenbank führt, seit Anfang der 70er Jahre, da sind z.Z. etwa 6,5 Millionen Dokumente darin, davon etwas über 1 Million Volltexte ab 1993. Das Prinzip war lange Zeit, dass wir die Dokumente, die in der Datenbank gespeichert waren und sind, verschlagworten und dieses Prinzip haben wir auch dann, als der Volltext eingeführt wurde, in abgespeckter Form weitergeführt. Zu diesen 6,5 Millionen Dokumenten gehören dann eben auch ungefähr 10 Millionen Faksimileseiten, weil wir die Faksimiles auch noch standardmäßig aufheben.

Date

22. 4.2003 11:45:36

0.006367738 = product of:
  0.03183869 = sum of:
    0.03183869 = weight(_text_:of in 2338) [ClassicSimilarity], result of:
      0.03183869 = score(doc=2338,freq=24.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.41895083 = fieldWeight in 2338, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2338)
  0.2 = coord(1/5)

Abstract

Hundreds of thousands of hashtags are generated every day on Twitter. Only a few will burst and become trending topics. In this article, we provide the definition of a bursting hashtag and conduct a systematic study of a series of challenging prediction problems that span the entire life cycles of bursting hashtags. Around the problem of "how to build a system to predict bursting hashtags," we explore different types of features and present machine learning solutions. On real data sets from Twitter, experiments are conducted to evaluate the effectiveness of the proposed solutions and the contributions of features.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2566-2579