Download Big Data Analytics: 4th International Conference, BDA 2015, by Naveen Kumar, Vasudha Bhatnagar PDF

By Naveen Kumar, Vasudha Bhatnagar

This ebook constitutes the refereed convention complaints of the Fourth overseas convention on immense information Analytics, BDA 2015, held in Hyderabad, India, in December 2015.

The nine revised complete papers and nine invited papers have been conscientiously reviewed and chosen from sixty one submissions and canopy issues on enormous information: defense and privateness; massive info in trade; enormous info: versions and algorithms; and large information in medicine.

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Additional info for Big Data Analytics: 4th International Conference, BDA 2015, Hyderabad, India, December 15-18, 2015, Proceedings

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The motivation behind extracting maximum value from data to facilitate maximal dollar value is a powerful motivator for many modern data collectors. However, as with any technology, reasonable constraints must be placed on its use to ensure that it is used appropriately. Although governments might ultimately intervene to impose those constraints if industry cannot find a way to manage itself, there are only surmountable technical challenges that will ultimately prevent us from achieving utility while protecting privacy.

R. ) ICDCIT 2015. LNCS, vol. 8956, pp. 431–442. Springer, Heidelberg (2015) Open Source Social Media Analytics for ISI Applications 37 8. : Mining YouTube metadata for detecting privacy invading harassment and misdemeanor videos. In: Privacy, Security and Trust (PST), pp. 84–93 (2014) 9. : Geoscope: online detection of geo-correlated information trends in social networks. Proc. VLDB Endow. 7, 229–240 (2013) 10. : Detecting future social unrest in unprocessed Twitter data: emerging phenomena and big data.

As shown in box 5 of Fig. 2. Users can also see the result in zoomed-in or zoomed-out view by using options—Detail, Image view—as shown in box 4. 6. Result Snippet: Since most datasets are high-dimensional, it is not possible to show all the attributes of each result tuple in the main result panel. Rather each result tuple is shown in the form of a snippet, where each snippet is a small subset of attribute values, as shown in box 6 of Fig. 2. For a database with homogeneous data, one can show the same subset of attributes for all the tuples as snippet.

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