Main Memory Evaluation of Monitoring Queries Over Moving Objects

Kalashnikov, Dmitri V.; Prabhakar, Sunil; Hambrusch, Susanne E.

doi:10.1023/b:dapd.0000013068.25976.88

Cited by 93 publications

(82 citation statements)

References 31 publications

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“…Therefore we are considering data sets of up to 100,000,000 moving objects. This is 10 times larger than in the biggest study available [23] and by at least two orders of magnitude larger than in all other studies, e.g., [26,27,17,19,51,7]. We think it is important to scale to such large data sets in order to understand the limits of the different methods.…”

Section: Experimental Studiesmentioning

confidence: 78%

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Indexing Moving Objects Using Short-Lived Throwaway Indexes

Dittrich

Blunschi

Salles

2009

Advances in Spatial and Temporal Databases

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Abstract. With the exponential growth of moving objects data to the Gigabyte range, it has become critical to develop effective techniques for indexing, updating, and querying these massive data sets. To meet the high update rate as well as low query response time requirements of moving object applications, this paper takes a novel approach in moving object indexing. In our approach we do not require a sophisticated index structure that needs to be adjusted for each incoming update. Rather we construct conceptually simple short-lived throwaway indexes which we only keep for a very short period of time (sub-seconds) in main memory. As a consequence, the resulting technique MOVIES supports at the same time high query rates and high update rates and trades this for query result staleness. Moreover, MOVIES is the first main memory method supporting time-parameterized predictive queries. To support this feature we present two algorithms: non-predictive MOVIES and predictive MOVIES. We obtain the surprising result that a predictive indexing approach -considered state-of-the-art in an external-memory scenario -does not scale well in a main memory environment. In fact our results show that MOVIES outperforms state-of-the-art moving object indexes like a main-memory adapted B x -tree by orders of magnitude w.r.t. update rates and query rates. Finally, our experimental evaluation uses a workload unmatched by any previous work. We index the complete road network of Germany consisting of 40,000,000 road segments and 38,000,000 nodes. We scale our workload up to 100,000,000 moving objects, 58,000,000 updates per second and 10,000 queries per second which is unmatched by any previous work.

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Section: Experimental Studiesmentioning

confidence: 78%

“…It is orthogonal to the techniques presented here and can be applied on top of any moving objects index. [19,51,26] do neither provide any means how to scale for cases when the main memory is exhausted nor provide any parallelization scheme. In contrast MOVIES provides solutions for all of these issues.…”

Section: Methods With Tp Queriesmentioning

confidence: 99%

Indexing Moving Objects Using Short-Lived Throwaway Indexes

Dittrich

Blunschi

Salles

2009

Advances in Spatial and Temporal Databases

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“…None of these techniques deal with the issue of spatio-temporal data streams where only in-memory solutions are allowed. Memory-based data structures have been proposed in [31,32,59] to deal with reasonable size of data that can fit in memory, but it is not scalable to large data sizes or streaming environments.…”

Section: Spatio-temporal Databasesmentioning

confidence: 99%

“…The high arrival rates of spatio-temporal data streams along with its massive data sizes make it infeasible for traditional spatio-temporal data management techniques to store, query, or index incoming spatio-temporal data. Unfortunately, most of the exiting techniques for spatiotemporal databases (e.g., see [27][28][29]31,[33][34][35]39,43,46,48,51,52,57]) rely mainly on the basic assumption that all incoming spatio-temporal data can be stored on disk. Thus, continuous query processing techniques (e.g, [27,39,52,57]) aim to utilize the disk storage to produce incremental results of continuous queries.…”

Section: Introductionmentioning

confidence: 99%

SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams

Mokbel

Aref

2007

The VLDB Journal

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This paper presents the Scalable On-Line Execution algorithm (SOLE, for short) for continuous and on-line evaluation of concurrent continuous spatiotemporal queries over data streams. Incoming spatiotemporal data streams are processed in-memory against a set of outstanding continuous queries. The SOLE algorithm utilizes the scarce memory resource efficiently by keeping track of only the significant objects. In-memory stored objects are expired (i.e., dropped) from memory once they become insignificant. SOLE is a scalable algorithm where all the continuous outstanding queries share the same buffer pool. In addition, SOLE is presented as a spatio-temporal join between two input streams, a stream of spatio-temporal objects and a stream of spatio-temporal queries. To cope with intervals of high arrival rates of objects and/or queries, SOLE utilizes a load-shedding approach where some of the stored objects are dropped from memory. SOLE is implemented as a pipelined query operator that can be combined with traditional query operators in a query execution plan to support a wide variety of continuous queries. Performance experiments based on a real implementation of SOLE inside a prototype of a data stream management system show the scalability and efficiency of SOLE in highly dynamic environments.

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“…Main-memory optimization of disk-based index structures has been explored recently for Bþ-trees [20] and multidimensional indexes [11][12][13]. Both studies investigate the redesign of the nodes in order to improve cache performance.…”

Section: Related Workmentioning

confidence: 99%

Fast similarity join for multi-dimensional data

Kalashnikov

Prabhakar

2007

Information Systems

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The efficient processing of multidimensional similarity joins is important for a large class of applications. The dimensionality of the data for these applications ranges from low to high. Most existing methods have focused on the execution of high-dimensional joins over large amounts of disk-based data. The increasing sizes of main memory available on current computers, and the need for efficient processing of spatial joins suggest that spatial joins for a large class of problems can be processed in main memory. In this paper, we develop two new in-memory spatial join algorithms, the Grid-join and EGO*-join, and study their performance. Through evaluation, we explore the domain of applicability of each approach and provide recommendations for the choice of a join algorithm depending upon the dimensionality of the data as well as the expected selectivity of the join. We show that the two new proposed join techniques substantially outperform the state-of-the-art join algorithm, the EGO-join. r

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Main Memory Evaluation of Monitoring Queries Over Moving Objects

Cited by 93 publications

References 31 publications

Indexing Moving Objects Using Short-Lived Throwaway Indexes

Indexing Moving Objects Using Short-Lived Throwaway Indexes

SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams

Fast similarity join for multi-dimensional data

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