Analysis and evaluation of V*-kNN: an efficient algorithm for moving kNN queries

Nutanong, Sarana; Zhang, Rui; Tanin, Egemen; Kulik, Lars

doi:10.1007/s00778-009-0163-0

Cited by 40 publications

(36 citation statements)

References 26 publications

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“…Based on the principle of spatial locality of references, we can cache data objects around the query point and attempt to reproduce query results using the cached data objects as the query point moves (Nutanong et al 2010;Song and Roussopoulos 2001). Once the cached data can no longer produce accurate query results, the cache is updated.…”

Section: Predefined Query Trajectoriesmentioning

confidence: 99%

“…The main difference between the samplingbased method (Song and Roussopoulos 2001) and the safe region-based method (Li et al 2014;Nutanong et al 2010) can be described as follows. The sampling-based method (Song and Roussopoulos 2001) does not produce truly continuous query answers; its main objective is to reduce the cost of each kNN query execution in order to accommodate a higher query frequency.…”

Section: Predefined Query Trajectoriesmentioning

confidence: 99%

“…The sampling-based method (Song and Roussopoulos 2001) does not produce truly continuous query answers; its main objective is to reduce the cost of each kNN query execution in order to accommodate a higher query frequency. On the other hand, the safe region-based method (Li et al 2014;Nutanong et al 2010) can produce continuous query results by keeping track of locations at which the query point crosses a safe region boundary.…”

Section: Predefined Query Trajectoriesmentioning

confidence: 99%

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Dual Space-Time Representation

2017

Encyclopedia of GIS

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Section: Predefined Query Trajectoriesmentioning

confidence: 99%

Section: Predefined Query Trajectoriesmentioning

confidence: 99%

Section: Predefined Query Trajectoriesmentioning

confidence: 99%

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Dual Space-Time Representation

2017

Encyclopedia of GIS

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“…The state-of-the-art MkNN algorithm is V*-Diagram [16,17]. We use this algorithm as the baseline algorithm in the experiments and will detail it in Section 3.2.…”

Section: Related Workmentioning

confidence: 99%

Processing moving k NN queries using influential neighbor sets

et al. 2014

Proc. VLDB Endow.

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The moving k nearest neighbor query, which computes one's k nearest neighbor set and maintains it while at move, is gaining importance due to the prevalent use of smart mobile devices such as smart phones. Safe region is a popular technique in processing the moving k nearest neighbor query. It is a region where the movement of the query object does not cause the current k nearest neighbor set to change. Processing a moving k nearest neighbor query is a continuing process of checking the validity of the safe region and recomputing it if invalidated. The size of the safe region largely decides the frequency of safe region recomputation and hence query processing efficiency. Existing moving k nearest neighbor algorithms lack efficiency due to either computing small safe regions and have to recompute frequently or computing large safe regions (i.e., an order-k Voronoi cell) with a high cost.In this paper, we take a third approach. Instead of safe regions, we use a small set of safe guarding objects. We prove that, as long as the the current k nearest neighbors are closer to the query object than the safe guarding objects, the current k nearest neighbors stay valid and no recomputation is required. This way, we avoid the high cost of safe region recomputation. We also prove that, the region defined by the safe guarding objects is the largest possible safe region. This means that the recomputation frequency of our method is also minimized. We conduct extensive experiments comparing our method with the state-of-the-art method on both real and synthetic data sets. The results confirm the superiority of our method.

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“…Their approximate algorithm is based on the correlation statistics between pairs of streams, which prunes more data objects than the exact algorithm while keeping the necessary accuracy. Nutanong et al [2010] present an algorithm for efficient processing of moving k-NN queries which uses an incremental technique based on safe regions called the V * diagram. This article also surveys different approaches to the problem of static top-k/w queries over moving data objects.…”

Section: Related Workmentioning

confidence: 99%

Time- and Space-Efficient Sliding Window Top-k Query Processing

Pripužić

Žarko

Aberer

2015

ACM Trans. Database Syst.

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A sliding window top-k (top-k/w) query monitors incoming data stream objects within a sliding window of size w to identify the k highest-ranked objects with respect to a given scoring function over time. Processing of such queries is challenging because, even when an object is not a top-k/w object at the time when it enters the processing system, it might become one in the future. Thus a set of potential top-k/w objects has to be stored in memory while its size should be minimized to efficiently cope with high data streaming rates. Existing approaches typically store top-k/w and candidate sliding window objects in a k-skyband over a two-dimensional score-time space. However, due to continuous changes of the k-skyband, its maintenance is quite costly. Probabilistic k-skyband is a novel data structure storing data stream objects from a sliding window with significant probability to become top-k/w objects in future. Continuous probabilistic k-skyband maintenance offers considerably improved runtime performance compared to k-skyband maintenance, especially for large values of k, at the expense of a small and controllable error rate. We propose two possible probabilistic k-skyband usages: (i) When it is used to process all sliding window objects, the resulting top-k/w algorithm is approximate and adequate for processing random-order data streams. (ii) When probabilistic k-skyband is used to process only a subset of most recent sliding window objects, it can improve the runtime performance of continuous k-skyband maintenance, resulting in a novel exact top-k/w algorithm. Our experimental evaluation systematically compares different top-k/w processing algorithms and shows that while competing algorithms offer either time efficiency at the expanse of space efficiency or vice-versa, our algorithms based on the probabilistic k-skyband are both time and space efficient.

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Analysis and evaluation of V*-kNN: an efficient algorithm for moving kNN queries

Cited by 40 publications

References 26 publications

Dual Space-Time Representation

Dual Space-Time Representation

Processing moving k NN queries using influential neighbor sets

Time- and Space-Efficient Sliding Window Top-k Query Processing

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