Efficient Processing of Top-K Preference Queries on Incomplete Information
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 6
Abstract
Incomplete data is general, finding and scrutinizing these kind of data is basic starting late. The top k overwhelming (TKD) queries return k challenges that supersedes most extraordinary number of things in a given dataset. It joins the advantages of skyline and top k queries. This accept a basic part in various decision bolster applications. Incomplete data holds in honest to goodness datasets, in light of contraption dissatisfaction, security protection, data setback. Here, framework finish an efficient examination of TKD queries on incomplete data, which joins the data having missing dimensional value(s). We handle this issue, and present an algorithm for taking note of TKD queries over incomplete data. Our systems use a couple of strategies, for instance, upper bound score pruning, bitmap pruning, and midway score pruning, to ascend the capability of queries. Created test e valuation using both authentic and designed datasets shows the feasibility of the made pruning rules and avows execution of algorithms.
Authors and Affiliations
Mr. Maharudra Banale, Prof. Bhagwan Kurhe
Keywords
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