Data science[electronic resource] :s...
Clark Conference ((2005 :)

 

  • Data science[electronic resource] :second International Conference, ICDS 2015, Sydney, Australia, August 8-9, 2015 : proceedings /
  • Record Type: Language materials, printed : Monograph/item
    [NT 15000414]: 005.74
    Title/Author: Data science : second International Conference, ICDS 2015, Sydney, Australia, August 8-9, 2015 : proceedings // edited by Chengqi Zhang ... [et al.].
    remainder title: ICDS 2015
    other author: Zhang, Chengqi.
    corporate name: Clark Conference
    Published: Cham : : Springer International Publishing :, 2015.
    Description: x, 194 p. : : ill., digital ;; 24 cm.
    Contained By: Springer eBooks
    Subject: Databases
    Subject: Computer Science.
    Subject: Computer networks.
    Subject: Software engineering.
    Subject: Database management.
    Subject: Information storage and retrieval.
    Subject: Pattern perception.
    Subject: Information Storage and Retrieval.
    Subject: Information Systems Applications (incl. Internet)
    Subject: Pattern Recognition.
    Subject: Computer Communication Networks.
    ISBN: 9783319244747
    ISBN: 9783319244730
    [NT 15000228]: Mathematical issues in data science -- Big data issues and applications -- Data quality and data preparation -- Data-driven scientific research -- Evaluation and measurement in data service -- Big data mining and knowledge management -- case study of data science -- Social impacts of data science.
    [NT 15000229]: This book constitutes the refereed proceedings of the Second International Conference on Data Science, ICDS 2015, held in Sydney, Australia, during August 8-9, 2015. The 19 revised full papers and 5 short papers presented were carefully reviewed and selected from 31 submissions. The papers focus on the following topics: mathematical issues in data science; big data issues and applications; data quality and data preparation; data-driven scientific research; evaluation and measurement in data service; big data mining and knowledge management; case study of data science; social impacts of data science.
    Online resource: http://dx.doi.org/10.1007/978-3-319-24474-7
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