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'Big data' and foot research

Discussion in 'General Issues and Discussion Forum' started by NewsBot, Oct 6, 2015.

  1. NewsBot

    NewsBot The Admin that posts the news.


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    Application of big data analysis with decision tree for the foot disorder
    Jung-Kyu Choi, Keun-Hwan Jeon, Yonggwan Won, Jung-Ja Kim
    Cluster Computing; pp 1-6; First online: 08 September 2015
  2. NewsBot

    NewsBot The Admin that posts the news.


    Big data

    Growth of and digitization of global information-storage capacity[1]

    Big data is data sets that are so voluminous and complex that traditional data-processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. There are five concepts associated with big data: volume, variety, velocity and, the recently added, veracity and value[according to whom?].

    Lately, the term "big data" tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]

    Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] Based on an IDC report prediction, the global data volume will grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 to 2020.[10] By 2025, IDC predicts there will be 163 zettabytes of data.[11] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[12]

    Relational database management systems and desktop statistics[clarification needed] and software packages to visualize data often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[13] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."[14]

    1. ^ "The World's Technological Capacity to Store, Communicate, and Compute Information". MartinHilbert.net. Retrieved 13 April 2016. 
    2. ^ boyd, dana; Crawford, Kate (21 September 2011). "Six Provocations for Big Data". Social Science Research Network: A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society. doi:10.2139/ssrn.1926431. 
    3. ^ Cite error: The named reference Economist was invoked but never defined (see the help page).
    4. ^ "Community cleverness required". Nature. 455 (7209): 1. 4 September 2008. doi:10.1038/455001a. PMID 18769385. 
    5. ^ Reichman, O.J.; Jones, M.B.; Schildhauer, M.P. (2011). "Challenges and Opportunities of Open Data in Ecology". Science. 331 (6018): 703–5. doi:10.1126/science.1197962. PMID 21311007. 
    6. ^ Hellerstein, Joe (9 November 2008). "Parallel Programming in the Age of Big Data". Gigaom Blog. 
    7. ^ Segaran, Toby; Hammerbacher, Jeff (2009). Beautiful Data: The Stories Behind Elegant Data Solutions. O'Reilly Media. p. 257. ISBN 978-0-596-15711-1. 
    8. ^ Hilbert, Martin; López, Priscila (2011). "The World's Technological Capacity to Store, Communicate, and Compute Information". Science. 332 (6025): 60–65. doi:10.1126/science.1200970. PMID 21310967. 
    9. ^ "IBM What is big data? – Bringing big data to the enterprise". www.ibm.com. Retrieved 26 August 2013. 
    10. ^ Sh. Hajirahimova, Makrufa; Sciences, Institute of Information Technology of Azerbaijan National Academy of; str., B. Vahabzade; Baku; AZ1141; Azerbaijan; Aliyeva, Aybeniz S. "About Big Data Measurement Methodologies and Indicators". International Journal of Modern Education and Computer Science. 9 (10): 1–9. doi:10.5815/ijmecs.2017.10.01. 
    11. ^ Reinsel, David; Gantz, John; Rydning, John (13 April 2017). "Data Age 2025: The Evolution of Data to Life-Critical" (PDF). seagate.com. Framingham, MA, US: International Data Corporation. Retrieved 2 November 2017. 
    12. ^ Oracle and FSN, "Mastering Big Data: CFO Strategies to Transform Insight into Opportunity", December 2012
    13. ^ Jacobs, A. (6 July 2009). "The Pathologies of Big Data". ACMQueue. 
    14. ^ Magoulas, Roger; Lorica, Ben (February 2009). "Introduction to Big Data". Release 2.0. Sebastopol CA: O'Reilly Media (11). 

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