Multi-Entry Generalized Search Trees for Indexing Trajectories
Maxime Schoemans, Walid G. Aref, Esteban Zim\'anyi, Mahmoud Sakr

TL;DR
This paper introduces multi-entry generalized search trees, MGiST and MSP-GiST, which partition complex objects like trajectories into multiple entries to significantly improve query performance in spatial databases.
Contribution
The paper presents novel multi-entry generalized search trees that enable object partitioning during insertion, enhancing indexing efficiency for complex data like trajectories.
Findings
Up to tenfold improvement in query performance.
Effective trajectory indexing with domain-specific splitting algorithms.
Versatile application to R-Tree, Quad-Tree, and KD-Tree structures.
Abstract
The idea of generalized indices is one of the success stories of database systems research. It has found its way to implementation in common database systems. GiST (Generalized Search Tree) and SP-GiST (Space-Partitioned Generalized Search Tree) are two widely-used generalized indices that are typically used for multidimensional data. Currently, the generalized indices GiST and SP-GiST represent one database object using one index entry, e.g., a bounding box for each spatio-temporal object. However, when dealing with complex objects, e.g., moving object trajectories, a single entry per object is inadequate for creating efficient indices. Previous research has highlighted that splitting trajectories into multiple bounding boxes prior to indexing can enhance query performance as it leads to a higher index filter. In this paper, we introduce MGiST and MSP-GiST, the multi-entry generalized…
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Taxonomy
TopicsData Management and Algorithms · Advanced Database Systems and Queries · Data Mining Algorithms and Applications
