Thread-Level Parallel Indexing of Update Intensive Moving-Object Workloads

Sidlauskas, Darius; Ross, Kenneth A.; Jensen, Christian S.; Šaltenis, Simonas

doi:10.1007/978-3-642-22922-0_12

Cited by 26 publications

(42 citation statements)

References 12 publications

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“…We use a simple spatial partitioning method motivated by our distributed simulation platform [39]. While this method is unlikely to outperform special-purpose parallel join methods such as the TwinGrid index [32], it does not require modifying any of the join algorithms.…”

Section: Parallel Executionmentioning

confidence: 99%

“…The authors show that MOVIES outperformed existing index structures by more than an order-of-magnitude for a large distributed main-memory workload. Rather than rebuilding a single index, the TwinGrid index maintains separate indices for reads and writes and copies data between them using an efficient parallel memcpy [32]. Both structures may become out of date for short periods of time.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, several recent studies have argued that many movingobject applications, including more traditional location based services such as flight tracking, can tolerate some query staleness [9,32]. However, these applications require very low response times even in the presence of significant query and update rates.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: Parallel Executionmentioning

confidence: 99%

Section: Related Workmentioning

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.

View full text Add to dashboard Cite

show abstract

“…We have observed that all of the example applications above, as well as many others, can be run completely in main memory. Furthermore, several recent studies have argued that many moving-object applications, including traditional location based services such as flight tracking, can tolerate some query staleness [DBVS11,ŠRJŠ11]. However, these applications require very low response times even in the presence of significant query and update rates.…”

Section: Benchmarking Spatial Indexingmentioning

confidence: 99%

Parallelizing Data-Centric Programs

Gehrke¹

2013

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“…Finally, a few studies proposed hybrid approaches and devise elegant distributed moving-object index structures [8,21]. Both these studies observed the update-heavy workload of the application and tried to alleviate the problem by delaying updates and keeping track of them in a separate inmemory data structure: a one-dimensional z-ordered buffer in [8] and a two dimensional grid in [21], consistent with their underlying spatial index structure.…”

Section: Introductionmentioning

confidence: 99%

ToSS-it: A Cloud-Based Throwaway Spatial Index Structure for Dynamic Location Data

Akdogan

Shahabi

Demiryurek

2014

2014 IEEE 15th International Conference on Mobile Data Management

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Abstract-The widespread use of GPS-enabled devices have led to a number of emerging applications that require monitoring and querying a large number of moving objects, such as in location-based services, mobile phone social networking, UAV surveillance, and car navigation systems. In such applications, indexes for moving objects must support queries efficiently and also cope with frequent updates. In this paper, we propose a cloud-based throwaway index structure, dubbed ToSS-it, where we generate the index from scratch in a short period of time rather than updating it with every location change of the moving objects.ToSS-it employs inter-node and intra-node multi-core parallelism paradigm to rapidly construct a distributed Voronoi Diagram. ToSS-it scales out by using a voronoi partitioning technique that minimizes the network message exchanges between the nodes (i.e., the major overhead in parallel generation of Voronoi Diagrams), and scales up since it fully exploits the multi-core CPUs available on each server. As a comparison point, with the state-of-the-art cloud-based spatial index structure (RT-CAN), if at least 7% of the objects are moving and issue updates to the index, it is faster to recreate ToSS-it from scratch than updating RT-CAN.

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Thread-Level Parallel Indexing of Update Intensive Moving-Object Workloads

Cited by 26 publications

References 12 publications

An experimental analysis of iterated spatial joins in main memory

An experimental analysis of iterated spatial joins in main memory

Parallelizing Data-Centric Programs

ToSS-it: A Cloud-Based Throwaway Spatial Index Structure for Dynamic Location Data

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