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Computer Science - Database Management & Information Retrieval | Big Data Analytics - First International Conference, BDA 2012, New Delhi, India, December 24-26,

Big Data Analytics

First International Conference, BDA 2012, New Delhi, India, December 24-26, 2012, Proceedings

Srinivasa, Srinath, Bhatnagar, Vasudha (Eds.)

2012, XIV, 181 p. 83 illus.

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This book constitutes the refereed proceedings of the First International Conference on Big Data Analytics, BDA 2012, held in New Delhi, India, in December 2012.
The 5 regular papers and 5 short papers presented were carefully reviewed and selected from 42 submissions. The volume also contains two tutorial papers in the section perspectives on big data analytics. The regular contributions are organized in topical sections on: data analytics applications; knowledge discovery through information extraction; and data models in analytics.

Content Level » Research

Keywords » active learning - data warehouse - large-scale data analysis - probability - social engineering

Related subjects » Artificial Intelligence - Database Management & Information Retrieval - Theoretical Computer Science

Table of contents 

Perspectives on Big Data Analytics.- Scalable Analytics – Algorithms and Systems.- Big-Data – Theoretical, Engineering and Analytics Perspective.- Data Analytics Applications.- A Comparison of Statistical Machine Learning Methods in Heartbeat Detection and Classification.- Enhanced Query-By-Object Approach for Information Requirement Elicitation in Large Databases.- Cloud Computing and Big Data Analytics: What Is New from Databases Perspective?.- A Model of Virtual Crop Labs as a Cloud Computing Application for Enhancing Practical Agricultural Education.- Knowledge Discovery through Information Extraction.- Exploiting Schema and Documentation for Summarizing Relational Databases.- Faceted Browsing over Social Media.- Analog Textual Entailment and Spectral Clustering (ATESC) Based Summarization.- Economics of Gold Price Movement-Forecasting Analysis Using Macro-economic, Investor Fear and Investor Behavior Features.- Data Models in Analytics.- An Efficient Method of Building an Ensemble of Classifiers in Streaming Data.- I/O Efficient Algorithms for Block Hessenberg Reduction Using Panel Approach.- Luring Conditions and Their Proof of Necessity through Mathematical Modelling.- Efficient Recommendation for Smart TV Contents.- Materialized View Selection Using Simulated Annealing.

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