A benchmark for evaluating moving object indexes

Su, Chen; Jensen, Christian S.; Lin, Dan

doi:10.14778/1454159.1454229

Cited by 72 publications

(74 citation statements)

References 21 publications

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“…We tune all of the algorithms for main memory, and evaluate them on uniform and skewed random workloads as well as two workloads motivated by high-performance spatial applications: a behavioral simulation of schooling fish [7] and a simple model of motion on road networks, which has previously been used to evaluate moving object indices [6]. We experiment with a wide range of parameters so that our results can be applied to many different scenarios.…”

Section: Experiments With Multiple Moving Object Workloadsmentioning

confidence: 99%

“…Those that explicitly addressed main-memory execution have informed our choice of data structures, but they focused on a small number of static spatial indices rather than the full range of spatial join algorithms evaluated in this study [15,20,38]. Another recent study focused exclusively on moving object indices for non-predictive range and nearest neighbor queries [6]. However, that study only considered disk-resident data and only addressed monitoring scenarios in which there are a large number of moving objects but relatively few queriers.…”

Section: Introductionmentioning

confidence: 99%

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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: Experiments With Multiple Moving Object Workloadsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An experimental analysis of iterated spatial joins in main memory

Sowell¹,

Salles

Cao

et al. 2013

Proc. VLDB Endow.

View full text Add to dashboard Cite

show abstract

“…Due to the 4-page limit, we show results only for synthetic workloads (based on [2]), but the same performance trends also hold for the simulation workloads.…”

Section: Methodsmentioning

confidence: 66%

“…For example, two dimensional coordinates are often encoded as two 4-byte single-precision or integer values [2,3,7,8].…”

Section: Introductionmentioning

confidence: 99%

Spatial joins in main memory

Sidlauskas

Jensen

2014

Proc. VLDB Endow.

Self Cite

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A recent PVLDB paper reports on experimental analyses of ten spatial join techniques in main memory. We build on this comprehensive study to raise awareness of the fact that empirical running time performance findings in main-memory settings are results of not only the algorithms and data structures employed, but also their implementation, which complicates the interpretation of the results.In particular, we re-implement the worst performing technique without changing the underlying high-level algorithm, and we then offer evidence that the resulting re-implementation is capable of outperforming all the other techniques. This study demonstrates that in main memory, where no time-consuming I/O can mask variations in implementation, implementation details are very important; and it offers a concrete illustration of how it is difficult to make conclusions from empirical running time performance findings in main-memory settings about data structures and algorithms studied.

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“…Some of these approaches had been evaluated independently in the literature, but to our knowledge no existing study had compared them all. Experiments with Multiple Moving Object Workloads: We tuned all of the algorithms for main memory, and evaluated them on uniform and skewed random workloads as well as two workloads motivated by high-performance spatial applications: a behavioral simulation of schooling fish [CKFL05] and a simple model of motion on road networks, which had previously been used to evaluate moving object indices [CJL08]. We experimented with a wide range of parameters so that our results can be applied to many different scenarios.…”

Section: Benchmarking Spatial Indexingmentioning

confidence: 99%

Parallelizing Data-Centric Programs

Gehrke¹

2013

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A benchmark for evaluating moving object indexes

Cited by 72 publications

References 21 publications

An experimental analysis of iterated spatial joins in main memory

An experimental analysis of iterated spatial joins in main memory

Spatial joins in main memory

Parallelizing Data-Centric Programs

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