Abstract
Background: Reverse nearest neighbors query is an important means to solve many practical applications based on the concept of Influence Sets. It is widely used in various fields such as data mining, decision support, resources allocation, knowledge discovery, data flow, bioinformatics and so on.
Objective: This work aims to improve time efficiency of Reverse Nearest Neighbors query of moving objects with large data scale.
Methods: A new spatio - temporal index HRNN-tree is developed.Then an algorithm for reverse nearest neighbors query based on HRNN-tree is developed.
Results: Our algorithm is superior to the existing method in execution time. The performance of our algorithm is excellent especially for the queries with large data scale and small values of k.
Conclusion: This study devises a new spatio - temporal index HRNN-tree. Then an algorithm for reverse nearest neighbor search of moving objects is developed based on this index. This algorithm avoids that the query performance deteriorates rapidly as the data space grows and has a better performance fort the large data space. This work will be helpful to enrich and improve the abilities of intelligent analysis, mobile computing and quantitative query based on distance for spatio - temporal database.
Keywords: Reverse nearest neighbors, spatial analysis, moving objects, spatio-temporal database, RNN-tree, algorithm.
Graphical Abstract
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