Big Data Analytics: First International Conference, BDA by Srinivasan H. Sengamedu (auth.), Srinath Srinivasa, Vasudha

By Srinivasan H. Sengamedu (auth.), Srinath Srinivasa, Vasudha Bhatnagar (eds.)

This ebook constitutes the refereed lawsuits of the 1st foreign convention on mammoth facts Analytics, BDA 2012, held in New Delhi, India, in December 2012. The five ordinary papers and five brief papers provided have been rigorously reviewed and chosen from forty two submissions. the amount additionally includes instructional papers within the part views on gigantic info analytics. The usual contributions are prepared in topical sections on: information analytics purposes; wisdom discovery via info extraction; and knowledge versions in analytics.

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Extra info for Big Data Analytics: First International Conference, BDA 2012, New Delhi, India, December 24-26, 2012. Proceedings

Sample text

The users were familiar with the database domain, mainly eSaguT R service and agricultural domain. and can validate the query results more comfortably. Users were then briefed about the QBT prototype for 15 minutes along with a quick demonstration of a sample query. Before the experiments, users were allowed a 5 minutes practice session to get themselves acquainted with the tool before starting the experiments. Using Retrospective testing, user’s screen was recorded to monitor the navigation process.

Yasir et al. In general we observe good results for most claims. For Q1, we achieved slightly higher rations for QBT than QBO, which shows that although the QBT approach consists of additional steps to the QBO, it does not deters the ease of use significantly. The claim Q2 received mixed reviews for both QBT and QBO prototype, which indicated that users demanded more operators to be provided to them so that their information needs can be elicited easily. For Q3 again QBT gets a higher rating than QBO which again emphasizes the ease of use of the QBT prototype.

The reduction factor represents the number of operation pairs in the QBT approach as compared to the QBO approach (RFop ). If the number of operation pairs in QBT are OPqbt and in case of QBO are OPqbo , the reduction factor (RFop ) is defined as follows: RFop = 1 − OPqbt OPqbo (1) We illustrate the metric by referring to Figure 3, where the total number of tables are 8. When tables have been divided into two topics, the number of operation pairs are are follows: two 4 × 4 WT matrix and one diagonal BT matrix (2 pairs).

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