Optimal scenic spot and hotel pairing method based on measurement k closest pair

Abstract

The invention discloses an optimal scenic spot and hotel pairing method based on a measurement k closest pair. The method comprises the steps that an M tree is adopted for indexing a tourist area data set and a hotel data set, a query space is actively pruned by means of the estimated kth closest pair distance and k closest pair distance upper bound to obtain an initial query result, then the query result is complemented, and therefore closest k scenic spot and holt combinations are obtained. The index technology and k closest pair query technology are sufficiently utilized, and the k closest pair query processing method under the measurement space is enriched and optimized; the depth-first traversal and best-first traversal combined mode is adopted; by means of the triangle inequality of the k closest pair distance upper bound and the measurement space, a plurality of effective pruning strategies are developed for pruning the query space; an active pruning and complementing algorithm framework based on the estimated kth closest pair distance is provided, and therefore I / O and CPU time is greatly shortened.

Description

technical field [0001] The invention relates to database indexing and query technology, in particular to the best pairing method of scenic spots and hotels based on the measure k nearest pairs. Background technique [0002] Query / search is a fundamental problem in computer science that exists in almost all computer application areas today. Most of the existing query processing focuses on Euclidean space, and uses Euclidean distance to measure the proximity relationship between objects; but in many practical applications, such as geographic information systems, data mining, and pattern recognition, the proximity relationship between objects cannot be used Euclidean distance to measure, but need to use other distance measures. For example, the distance measure between two locations on the road network needs to use the road network distance, that is, the length of the shortest path connecting two points; the distance measure between strings needs to use the edit distance. [...

AI Technical Summary

Problems solved by technology

Therefore, these methods cannot be used to solve the metric k-recentest pair query

In addition, the existing metric k recently is a memory-based method for querying, that is, it is assumed that the entire data set can be stored in memory, so it has limited applicability and poor scalability, and cannot be used to process large-scale data

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