The COST Benchmark—Comparison and Evaluation of Spatio-temporal Indexes

Jensen, Christian S.; Tiešytė, Dalia; Tradišauskas, Nerius

doi:10.1007/11733836_11

Cited by 19 publications

(10 citation statements)

References 17 publications

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“…Unfortunately, CitySimulator is no longer available and these benchmarks are no longer usable. The COST Benchmark also defined a workload to evaluate several moving object indices, but it focused on scenarios in which the object locations are inaccurate [19].…”

Section: Related Workmentioning

confidence: 99%

An experimental analysis of iterated spatial joins in main memory

Sowell¹,

Salles

Cao

et al. 2013

Proc. VLDB Endow.

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Many modern applications rely on high-performance processing of spatial data. Examples include location-based services, games, virtual worlds, and scientific simulations such as molecular dynamics and behavioral simulations. These applications deal with large numbers of moving objects that continuously sense their environment, and their data access can often be abstracted as a repeated spatial join. Updates to object positions are interspersed with these join operations, and batched for performance. Even for the most demanding scenarios, the data involved in these joins fits comfortably in the main memory of a cluster of machines, and most applications run completely in main memory for performance reasons.Choosing appropriate spatial join algorithms is challenging due to the large number of techniques in the literature. In this paper, we perform an extensive evaluation of repeated spatial join algorithms for distance (range) queries in main memory. Our study is unique in breadth when compared to previous work: We implement, tune, and compare ten distinct algorithms on several workloads drawn from the simulation and spatial indexing literature. We explore the design space of both index nested loops algorithms and specialized join algorithms, as well as the use of moving object indices that can be incrementally maintained. Surprisingly, we find that when queries and updates can be batched, repeatedly re-computing the join result from scratch outperforms using a moving object index in all but the most extreme cases. This suggests that -given the code complexity of index structures for moving objects -specialized join strategies over simple index structures, such as Synchronous Traversal over R-Trees, should be the methods of choice for the above applications.

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Section: Related Workmentioning

confidence: 99%

An experimental analysis of iterated spatial joins in main memory

Sowell¹,

Salles

Cao

et al. 2013

Proc. VLDB Endow.

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“…Queries on near-future positions of the moving objects are considered. More recently, Jensen et al [12] proposed a benchmark, called COST. Their workload generation differs substantially from what is proposed in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…While a few benchmarks [15,12] exist that address the same indexing problem as does this paper, these man be improved with respect to the generation of datasets and workload, and they do not consider a multi-user environment. This paper aims to establish a more comprehensive benchmark than has been seen hitherto.…”

Section: Introductionmentioning

confidence: 99%

A benchmark for evaluating moving object indexes

Jensen

Lin

2008

Proc. VLDB Endow.

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Progress in science and engineering relies on the ability to measure, reliably and in detail, pertinent properties of artifacts under design. Progress in the area of database-index design thus relies on empirical studies based on prototype implementations of indexes. This paper proposes a benchmark that targets techniques for the indexing of the current and near-future positions of moving objects. This benchmark enables the comparison of existing and future indexing techniques. It covers important aspects of such indexes that have not previously been covered by any benchmark. Notable aspects covered include update efficiency, query efficiency, concurrency control, and storage requirements. Next, the paper applies the benchmark to half a dozen notable moving-object indexes, thus demonstrating the viability of the benchmark and offering new insight into the performance properties of the indexes.

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“…Recent benchmark studies [2,10] compare the performance of state-of-art moving objects indexes. Techniques that build on the B + -tree are capable of very good performance, and they offer ease of integration into real systems that do not support multidimensional structures such as the R-tree.…”

Section: Index Structures For Moving Objectsmentioning

confidence: 99%

Effectively indexing uncertain moving objects for predictive queries

et al. 2009

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Moving object indexing and query processing is a well studied research topic, with applications in areas such as intelligent transport systems and location-based services. While much existing work explicitly or implicitly assumes a deterministic object movement model, real-world objects often move in more complex and stochastic ways. This paper investigates the possibility of a marriage between moving-object indexing and probabilistic object modeling. Given the distributions of the current locations and velocities of moving objects, we devise an efficient inference method for the prediction of future locations. We demonstrate that such prediction can be seamlessly integrated into existing index structures designed for moving objects, thus improving the meaningfulness of range and nearest neighbor query results in highly dynamic and uncertain environments. The paper reports on extensive experiments on the Bx -tree that offer insights into the properties of the paper's proposal.

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The COST Benchmark—Comparison and Evaluation of Spatio-temporal Indexes

Cited by 19 publications

References 17 publications

An experimental analysis of iterated spatial joins in main memory

An experimental analysis of iterated spatial joins in main memory

A benchmark for evaluating moving object indexes

Effectively indexing uncertain moving objects for predictive queries

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