In this paper, we propose an &cient solution to the problem of nearest neighbor query processing in decluste red spatial data. Recently a branch-and-bound nearest neighbor finding (BB-NNF) algorithm has been designed to process nearest neighbor queries in R-trees. However, this algorithm is strictly serial (branch-and-bound oriented) and its performance degrades if applied to a parallel environment, since it does not exploit any kind of parallelization.We develop an eEicient query processing strategy tir parallel nearest neighbor finding (P-NNF), assuming a shared nothing multi-processor architecture, where the processors i communicate via a network. In our method, the relevant : sites are activated simultaneously.In order to achieve this goal, statistical information is used. The dTiciency mcasurc is the response time of a given query. E2cperimental results, based on real-life and synthetic datasets, show that the proposed method outperforms the branch-andbound method by factors.We focus on Zd space but generalizations to higher dimensions are straightforward.

Consider a file that resides in a linear storage device with one read head. Suppose that several queries on the file must be answered simultaneously with no prespecified order. To satisfy the ith query the head must be located at point I., of the file and traverse the file up to point R, without interruptions, where 1~ L, < R, 4 N denote positions in the file. We wish to find the execution order that minimizes the total time to service all queries. measured as the total distance traversed by the head. Although this is obviously a special type of traveling salesman problem. we show that the optimum sequence can be determined by a simple algorithm in G(n log n) time. The case in which the head may traverse a file in reverse is similarly solved.

No abstract

In a previous study an ordered array o/N keys was considered and the problem of locating a batch ofM requested keys was investigated by assuming both batched sequential and batched binary searching. This paper introduces the idea of batched interpolation search, and two variations of the method are presented. Comparisons with the two previously defined methods are also made.

Modern applications requiring spatial network processing pose many interesting query optimization challenges. In many cases, query processing depends on the corresponding graph size (number of nodes and edges) and other graph parameters. In this paper, we present novel methods to estimate the number of nodes in regions of interest in spatial networks, towards predicting the space and time requirements of range queries. We examine all methods by using real-life and synthetic spatial networks. Experimental results show that the number of nodes can be estimated efficiently and accurately with small space requirements, thus providing useful information to the query optimizer.