Join-queries between two spatial datasets indexed by a single R*-tree
SOFSEM'11 Proceedings of the 37th international conference on Current trends in theory and practice of computer science
Performance comparison of xBR-trees and R*-trees for single dataset spatial queries
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Cache based approach for improving location based query processing in mobile environment
Proceedings of the First International Conference on Security of Internet of Things
PL-Tree: an efficient indexing method for high-dimensional data
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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Multidimensional point indexing plays a critical role in a variety of data-centric applications, including image retrieval, sequence matching, and moving object database search. A common choice of indexing method for these applications is often the "ubiquitous” {\rm R}^{\ast}-tree. Choosing the right indexing method requires careful consideration of various factors such as query operations and index construction methods. In this work, we present an experimental study comparing the {\rm R}^{\ast}-tree and Quadtree using various criteria including the query operations and index construction methods. Although a variety of query operations can be performed using these index structures, previous work has largely focused only on the range search operation. We go beyond this previous work and compare the performance of these index structures using k-nearest neighbor (kNN) and distance join queries. In addition, we also consider the impact of index construction methods in evaluating these index structures. Our study sheds light on how the choice of the underlying index structure affects the performance of different query operations, and shows that the method used for constructing the index and the dynamic nature of the data set has a dramatic impact on the performance of these index structures.