812 research outputs found
Speed Partitioning for Indexing Moving Objects
Indexing moving objects has been extensively studied in the past decades.
Moving objects, such as vehicles and mobile device users, usually exhibit some
patterns on their velocities, which can be utilized for velocity-based
partitioning to improve performance of the indexes. Existing velocity-based
partitioning techniques rely on some kinds of heuristics rather than
analytically calculate the optimal solution. In this paper, we propose a novel
speed partitioning technique based on a formal analysis over speed values of
the moving objects. We first show that speed partitioning will significantly
reduce the search space expansion which has direct impacts on query performance
of the indexes. Next we formulate the optimal speed partitioning problem based
on search space expansion analysis and then compute the optimal solution using
dynamic programming. We then build the partitioned indexing system where
queries are duplicated and processed in each index partition. Extensive
experiments demonstrate that our method dramatically improves the performance
of indexes for moving objects and outperforms other state-of-the-art
velocity-based partitioning approaches
Advance of the Access Methods
The goal of this paper is to outline the advance of the access methods in the last ten years as well as
to make review of all available in the accessible bibliography methods
TRANSFORMERS: Robust spatial joins on non-uniform data distributions
Spatial joins are becoming increasingly ubiquitous in many applications, particularly in the scientific domain. While several approaches have been proposed for joining spatial datasets, each of them has a strength for a particular type of density ratio among the joined datasets. More generally, no single proposed method can efficiently join two spatial datasets in a robust manner with respect to their data distributions. Some approaches do well for datasets with contrasting densities while others do better with similar densities. None of them does well when the datasets have locally divergent data distributions. In this paper we develop TRANSFORMERS, an efficient and robust spatial join approach that is indifferent to such variations of distribution among the joined data. TRANSFORMERS achieves this feat by departing from the state-of-the-art through adapting the join strategy and data layout to local density variations among the joined data. It employs a join method based on data-oriented partitioning when joining areas of substantially different local densities, whereas it uses big partitions (as in space-oriented partitioning) when the densities are similar, while seamlessly switching among these two strategies at runtime. We experimentally demonstrate that TRANSFORMERS outperforms state-of-the-art approaches by a factor of between 2 and 8
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