Loading...
Please wait, while we are loading the content...
Similar Documents
Parallel kNN Queries for Big Data Based on Voronoi Diagram Using MapReduce
| Content Provider | Semantic Scholar |
|---|---|
| Copyright Year | 2019 |
| Abstract | In cloud computing environments parallel kNN queries for big data is an important issue. The k nearest neighbor queries (kNN queries), designed to find k nearest neighbors from a dataset S for every object in another dataset R, is a primitive operator widely adopted by many applications including knowledge discovery, data mining, and spatial databases. This chapter proposes a parallel method of kNN queries for big data using MapReduce programming model. Firstly, this chapter proposes an approximate algorithm that is based on mapping multi-dimensional data sets into two-dimensional data sets, and transforming kNN queries into a sequence of two-dimensional point searches. Then, in two-dimensional space this chapter proposes a partitioning method using Voronoi diagram, which incorporates the Voronoi diagram into R-tree. Furthermore, this chapter proposes an efficient algorithm for processing kNN queries based on R-tree using MapReduce programming model. Finally, this chapter presents the results of extensive experimental evaluations which indicate efficiency of the proposed approach. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.igi-global.com/viewtitlesample.aspx?id=138706&ptid=127619&t=parallel+knn+queries+for+big+data+based+on+voronoi+diagram+using+mapreduce |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
