NDLI: Classification of correlated subspaces using HoVer representation of Census Data

Content Provider	IEEE Xplore Digital Library
Author	Joe, J.F. Ravi, T. Justus, C.J.
Copyright Year	2011
Description	Author affiliation: Einstein College of Engg, Tirunelveli, India (Joe, J.F.) \|\| KCG College of Technology, Chennai, India (Ravi, T.) \|\| Nangil Infotech Pvt Ltd, Technopark, TVM, India (Justus, C.J.)
Abstract	Sparse data are becoming increasingly common and available in many real-life applications. However, relatively little attention has been paid to effectively model the sparse data and existing approaches such as the conventional “horizontal” and “vertical” representations fail to provide satisfactory performance for both storage and query processing, as such approaches are too rigid and generally do not consider the dimension correlations. So a new technique called HoVer was proposed by Bin Cui. This method holds better than both horizontal and vertical representations. In this paper Census Data in sparse form are taken. The variations in performance in time, space and transactions are measured. The parameters are then compared with the performance in time, space and transactions measured for the E-commerce datasets. The changes in parameters with the change in schema are analyzed and the variations are observed.
Starting Page	906
Ending Page	911
File Size	1126489
Page Count	6
File Format	PDF
ISBN	9781424479238
e-ISBN	9781424479269
DOI	10.1109/ICETECT.2011.5760248
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-23
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation HoVer Census Data Silicon Correlated Subspaces Sparse Data
Content Type	Text
Resource Type	Article

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