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

By Naveen Kumar, Vasudha Bhatnagar

This publication constitutes the refereed convention complaints of the Fourth overseas convention on giant info 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 subject matters on great info: protection and privateness; immense info in trade; significant info: versions and algorithms; and massive info in medicine.

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

Sample text

Both the above types of snippets are static in the sense that they are not dependent on the users’ query. 3 Obstacles in Exploratory Search There are three factors that thwart exploratory search: (a) Limited feedback in the query panel, (b) Low adaptivity of the result panel, and (c) Information loss from hidden attributes. In this section, we explain each of these three obstacles. 1 Limited Feedback in the Query Panel As discussed in Sect. 2, faceted navigation combined with keyword search is currently the most popular frontend for accessing e-commerce databases.

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.

In our approach we present a case study on immigration related event- Christmas Island Hunger Strike. To investigate the effectiveness of our approach, we conduct experiments on real world dataset downloaded from Southampton WebObservatory. We train a multi-class classifier to filter event related tweets (crowd-buzz and mobilization) which certainly improves the accuracy the accuracy of prediction and reduces the computational cost. We develop a frequency based model on these event related tweets (semantically rich) and find those pairs of named entities (spatial location, topics and temporal) expressions that are significantly correlated.

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