Search (6 results, page 1 of 1)

  • × theme_ss:"Data Mining"
  • × year_i:[2010 TO 2020}
  1. Carter, D.; Sholler, D.: Data science on the ground : hype, criticism, and everyday work (2016) 0.03 
    0.027950348 = product of:
  0.055900697 = sum of:
    0.055900697 = product of:
      0.11180139 = sum of:
        0.11180139 = weight(_text_:light in 3111) [ClassicSimilarity], result of:
          0.11180139 = score(doc=3111,freq=2.0), product of:
            0.2920221 = queryWeight, product of:
              5.7753086 = idf(docFreq=372, maxDocs=44218)
              0.050563898 = queryNorm
            0.3828525 = fieldWeight in 3111, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.7753086 = idf(docFreq=372, maxDocs=44218)
              0.046875 = fieldNorm(doc=3111)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Modern organizations often employ data scientists to improve business processes using diverse sets of data. Researchers and practitioners have both touted the benefits and warned of the drawbacks associated with data science and big data approaches, but few studies investigate how data science is carried out "on the ground." In this paper, we first review the hype and criticisms surrounding data science and big data approaches. We then present the findings of semistructured interviews with 18 data analysts from various industries and organizational roles. Using qualitative coding techniques, we evaluated these interviews in light of the hype and criticisms surrounding data science in the popular discourse. We found that although the data analysts we interviewed were sensitive to both the allure and the potential pitfalls of data science, their motivations and evaluations of their work were more nuanced. We conclude by reflecting on the relationship between data analysts' work and the discourses around data science and big data, suggesting how future research can better account for the everyday practices of this profession.
  2. Ekbia, H.; Mattioli, M.; Kouper, I.; Arave, G.; Ghazinejad, A.; Bowman, T.; Suri, V.R.; Tsou, A.; Weingart, S.; Sugimoto, C.R.: Big data, bigger dilemmas : a critical review (2015) 0.02 
    0.023291955 = product of:
  0.04658391 = sum of:
    0.04658391 = product of:
      0.09316782 = sum of:
        0.09316782 = weight(_text_:light in 2155) [ClassicSimilarity], result of:
          0.09316782 = score(doc=2155,freq=2.0), product of:
            0.2920221 = queryWeight, product of:
              5.7753086 = idf(docFreq=372, maxDocs=44218)
              0.050563898 = queryNorm
            0.31904373 = fieldWeight in 2155, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.7753086 = idf(docFreq=372, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2155)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    The recent interest in Big Data has generated a broad range of new academic, corporate, and policy practices along with an evolving debate among its proponents, detractors, and skeptics. While the practices draw on a common set of tools, techniques, and technologies, most contributions to the debate come either from a particular disciplinary perspective or with a focus on a domain-specific issue. A close examination of these contributions reveals a set of common problematics that arise in various guises and in different places. It also demonstrates the need for a critical synthesis of the conceptual and practical dilemmas surrounding Big Data. The purpose of this article is to provide such a synthesis by drawing on relevant writings in the sciences, humanities, policy, and trade literature. In bringing these diverse literatures together, we aim to shed light on the common underlying issues that concern and affect all of these areas. By contextualizing the phenomenon of Big Data within larger socioeconomic developments, we also seek to provide a broader understanding of its drivers, barriers, and challenges. This approach allows us to identify attributes of Big Data that require more attention-autonomy, opacity, generativity, disparity, and futurity-leading to questions and ideas for moving beyond dilemmas.
  3. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.01 
    0.008563388 = product of:
  0.017126776 = sum of:
    0.017126776 = product of:
      0.034253553 = sum of:
        0.034253553 = weight(_text_:22 in 668) [ClassicSimilarity], result of:
          0.034253553 = score(doc=668,freq=2.0), product of:
            0.17706616 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050563898 = queryNorm
            0.19345059 = fieldWeight in 668, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=668)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Date
    22. 3.2013 19:43:01
  4. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.01 
    0.008563388 = product of:
  0.017126776 = sum of:
    0.017126776 = product of:
      0.034253553 = sum of:
        0.034253553 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
          0.034253553 = score(doc=1605,freq=2.0), product of:
            0.17706616 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050563898 = queryNorm
            0.19345059 = fieldWeight in 1605, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1605)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  5. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.01 
    0.008563388 = product of:
  0.017126776 = sum of:
    0.017126776 = product of:
      0.034253553 = sum of:
        0.034253553 = weight(_text_:22 in 5011) [ClassicSimilarity], result of:
          0.034253553 = score(doc=5011,freq=2.0), product of:
            0.17706616 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050563898 = queryNorm
            0.19345059 = fieldWeight in 5011, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5011)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Date
    7. 3.2019 16:32:22
  6. Jäger, L.: Von Big Data zu Big Brother (2018) 0.01 
    0.0068507106 = product of:
  0.013701421 = sum of:
    0.013701421 = product of:
      0.027402842 = sum of:
        0.027402842 = weight(_text_:22 in 5234) [ClassicSimilarity], result of:
          0.027402842 = score(doc=5234,freq=2.0), product of:
            0.17706616 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050563898 = queryNorm
            0.15476047 = fieldWeight in 5234, 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=5234)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Date
    22. 1.2018 11:33:49

Authors

Languages

Types

Themes