Download e-book for iPad: Big Data Analytics: Third International Conference, BDA by Srinath Srinivasa, Sameep Mehta

By Srinath Srinivasa, Sameep Mehta

ISBN-10: 3319138197

ISBN-13: 9783319138190

ISBN-10: 3319138200

ISBN-13: 9783319138206

This publication constitutes the refereed convention court cases of the 3rd foreign convention on huge facts Analytics, BDA 2014, held in New Delhi, India, in December 2014. The eleven revised complete papers and six brief papers have been rigorously reviewed and chosen from 35 submissions and canopy themes on media analytics; geospatial mammoth information; semantics and information versions; seek and retrieval; pix and visualization; application-specific giant data.

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Additional resources for Big Data Analytics: Third International Conference, BDA 2014, New Delhi, India, December 20-23, 2014. Proceedings

Sample text

Sachdeva for each non-null attribute value whereas EAV stores three entries per non-null attribute value. This is not true every time. OEAV constructs 32 bit code for each value regardless of space taken by attribute name and value (in case of EAV). This may lead to wastage of space. Attribute Centric Queries. Comparison of the time taken to execute the attribute centric queries is shown through charts in fig. 6(b) and 6(c). When a single attribute is to be selected; Dynamic tables performed best as only one table was to be accessed.

In: IEEE 2012 International Conference on Advances in ICT for Emerging Regions, ICTer (2012) 11. : C4. 5: programs for machine learning, vol. 1. Morgan kaufmann (1993) 12. : Natural language processing with Python. O’Reilly Media, Inc. com Abstract. Most efforts towards analyzing Big Data assume data parallel applications and handle the large volumes of data using Hadoop–like systems. However, Big Data is actually characterized by the 4V’s – Volume, Variety, Velocity and Veracity.

It becomes important to normalize the text by applying a series of pre-processing steps. We have applied an extensive set of pre-processing steps to decrease the size of the feature set to make it suitable for learning algorithms. We have given an illustration of a tweet in Fig. 2. The frequency of such patterns per tweet, cut by datasets is given in Tab. 3. Following which we also give a brief description of pre-processing steps taken. Table 3. Frequency of Features per Tweet Features Handles Hashtags Urls Emoticons Words Twitter Sentiment Stanford Corpus Both Avg.

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Big Data Analytics: Third International Conference, BDA 2014, New Delhi, India, December 20-23, 2014. Proceedings by Srinath Srinivasa, Sameep Mehta


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