By Srinivasan H. Sengamedu (auth.), Srinath Srinivasa, Vasudha Bhatnagar (eds.)
This ebook constitutes the refereed complaints of the 1st overseas convention on mammoth information Analytics, BDA 2012, held in New Delhi, India, in December 2012. The five common papers and five brief papers offered have been rigorously reviewed and chosen from forty two submissions. the quantity additionally comprises instructional papers within the part views on titanic facts analytics. The general contributions are equipped in topical sections on: facts analytics functions; wisdom discovery via details extraction; and information types in analytics.
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Extra resources for Big Data Analytics: First International Conference, BDA 2012, New Delhi, India, December 24-26, 2012. Proceedings
Feature Extraction We used a 13 dimensional feature vector, one for every heartbeat recorded in the 30 minute recording of each patient. The features consist of RR interval duration, the Rpeak amplitude, and 5 samples each to the left and right of the R-peak, from the preprocessed ECG signal recordings down-sampled to 114 Hz. The 114 Hz sampling rate is in the vicinity of the average sample rate reported by Alvarado et al . The RR interval features include the Pre-RR Interval and the Post-RR Interval.
The possible operations are deﬁned in Between-Topics-I matrix (BT[1,2]). 36 A. Yasir et al. Fig. 4. 1. 2. 3. – Provide a interface based on QBT, to allow object selection,instance selection and support closure property. QBT User Protocol. The user protocol for QBT is described in Figure 4. The main options in the QBT are as follows. 1 Select a topic Select granularity (a table, attribute and attribute values) Select another topic Select an operation Display result Extend query, if required Experiments Experimental Methodology To analyze the eﬀectiveness of the proposed approach, we conducted systemlevel experiments and also a usability study.
Tables of each topic are further represented as a, b, and so on. Table 3 describes the Within Topic matrix for the ﬁrst Topic (WT-I) and table 4 describes the Between Topic matrix (BT). The following scenarios come up in context of Figure 3, Table 3. Within-Topic Matrix 1(WT-I) Table 4. Between-Topic Matrix(BT) t T11 T12a T12b T12c T11 U,I,C R R R T12a R U,I,C R R T12b R R U,I,C R T12c R R R U,I,C t T11 T21 T11 [WT-I] R T21 R [WT-II] Scenario 1. The two selected objects belong to the same topic.