An Efficient Bayesian Nearest Neighbor Search Using Marginal Object Weight Ranking Scheme in Spatial Databases
- 1 Velalar College of Engineering and Technology, India
- 2 K.S. Rangasamy College of Technology, India
Copyright: © 2020 K. Balasaravanan and K. Duraiswamy. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Problem statement: A database that is optimized to store and query data that is related to objects in space, including points, lines and polygons is called spatial database. Identifying nearest neighbor object search is a vital part of spatial database. Many nearest neighbor search techniques such as Authenticated Multi-step NN (AMNN), Superseding Nearest Neighbor (SNN) search, Bayesian Nearest Neighbor (BNN) and so on are available. But they had some difficulties while performing NN in uncertain spatial database. AMNN does not process the queries from distributed server and it accesses the queries only from single server. In SNN, the high dimensional data structure could not be used in NN search and it accesses only low dimensional data for NN search. Approach: The previous works described the process of NN using SNN with marginal object weight ranking. The downside over the previous work is that the performance is poor when compared to another work which performed NN using BNN. To improve the NN search in spatial databases using BNN, we are going to present a new technique as BNN search using marginal object weight ranking. Based on events occurring in the nearest object, BNN starts its search using MOW. The MOW is done by computing the weight of each NN objects and rank each object based on its frequency and distance of NN object for an efficient NN search in spatial databases. Results: Marginal Object Weight (MOW) is introduced to all nearest neighbor object identified using BNN for any relevant query point. It processes the queries from distributed server using MOW. Conclusion: The proposed BNN using MOW framework is experimented with real data sets to show the performance improvement with the previous MOW using SNN in terms of execution time, memory consumption and query result accuracy.
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- Marginal Object Weight (MOW)
- Superseding Nearest Neighbor (SNN)
- Authenticated Multi-step NN (AMNN)
- Bayesian Nearest Neighbor (BNN)