The B dual -Tree: indexing moving objects by space filling curves in the dual space

Yiu, Man Lung; Tao, Yufei; Mamoulis, Nikos

doi:10.1007/s00778-006-0013-2

Cited by 102 publications

(84 citation statements)

References 17 publications

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“…This includes STRIPES [33] and [21] which transform d-dimensional space to 2d-dimensional Hough-X space [15]. The recently proposed B dual -tree [50] uses the same idea; however it maps the Hough-X space back to a one-dimensional space using a Hilbert curve. [43] presents a study on dual methods concluding that if query efficiency is required (as required in this paper), dual methods are not competitive.…”

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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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: 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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“…The second type of methods are based on the dimension reduction technique [11] and a low-dimensional index [7,16] (e.g. B + -tree).…”

Section: Related Workmentioning

confidence: 99%

“…B + -tree). The B x -tree [7,16] combine the linearization technique with a single B + -tree to efficiently update the index structure. They uses space filling curves and a pre-defined time interval to partition the representation of the locations of the moving objects.…”

Section: Related Workmentioning

confidence: 99%

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Update-efficient indexing of moving objects in road networks

Chen

Meng

2008

Geoinformatica

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Recent advances in wireless sensor networks and positioning technologies have boosted new applications that manage moving objects. In such applications, a dynamic index is often built to expedite evaluation of spatial queries. However, development of efficient indexes is a challenge due to frequent object movement. In this paper, we propose a new update-efficient index method for moving objects in road networks. We introduce a dynamic data structure, called adaptive unit, to group neighboring objects with similar movement patterns. To reduce updates, an adaptive unit captures the movement bounds of the objects based on a prediction method, which considers the road-network constraints and stochastic traffic behavior. A spatial index (e.g., R-tree) for the road network is then built over the adaptive unit structures. Simulation experiments, carried on two different datasets, show that an adaptive-unit based index is efficient for both updating and querying performance.

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“…There have been found many applications in a variety of fields including image processing and compression [9,21], spatial query [7], wireless sensor networks [1], wireless broadcast system [26], neural networks [5], genome visualization [11], index of multi-dimensional data [20], spatiotemporal index [25] and bandwidth compression [2].…”

Section: Introductionmentioning

confidence: 99%

Quad-splitting algorithm for a window query on a Hilbert curve

Chang

2009

IET Image Processing

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The B dual -Tree: indexing moving objects by space filling curves in the dual space

Cited by 102 publications

References 17 publications

Indexing Moving Objects Using Short-Lived Throwaway Indexes

Indexing Moving Objects Using Short-Lived Throwaway Indexes

Update-efficient indexing of moving objects in road networks

Quad-splitting algorithm for a window query on a Hilbert curve

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