• DocumentCode
    1757350
  • Title

    Research Directions for Engineering Big Data Analytics Software

  • Author

    Otero, Carlos E. ; Peter, Adrian

  • Author_Institution
    Florida Inst. of Technol., Melbourne, FL, USA
  • Volume
    30
  • Issue
    1
  • fYear
    2015
  • fDate
    Jan.-Feb. 2015
  • Firstpage
    13
  • Lastpage
    19
  • Abstract
    Many software startups and research and development efforts are actively trying to harness the power of big data and create software with the potential to improve almost every aspect of human life. As these efforts continue to increase, full consideration needs to be given to the engineering aspects of big data software. Since these systems exist to make predictions on complex and continuous massive datasets, they pose unique problems during specification, design, and verification of software that needs to be delivered on time and within budget. But, given the nature of big data software, can this be done? Does big data software engineering really work? This article explores the details of big data software, discusses the main problems encountered when engineering big data software, and proposes avenues for future research.
  • Keywords
    data analysis; formal specification; formal verification; software engineering; big data software; complex massive datasets; continuous massive datasets; engineering big data analytics software; formal specification; formal verification; research directions; software startups; Big data; Data models; Intelligent systems; Mathematical model; Software engineering; Software reliability; big data; design; intelligent systems; quality; reliability; software engineering; testing;
  • fLanguage
    English
  • Journal_Title
    Intelligent Systems, IEEE
  • Publisher
    ieee
  • ISSN
    1541-1672
  • Type

    jour

  • DOI
    10.1109/MIS.2014.76
  • Filename
    6914469