Continuous Reverse k-Nearest-Neighbor Monitoring

Abstract-Given a set of objects and a query q, a point p is called the reverse k nearest neighbor (RkNN) of q if q is one of the k closest objects of p. In this paper, we introduce the concept of influence zone which is the area such that every point inside this area is the RkNN of q and every point outside this area is not the RkNN. The influence zone has several applications in location based services, marketing and decision support systems. It can also be used to efficiently process RkNN queries. First, we present efficient algorithm to compute the influence zone. Then, based on the influence zone, we present efficient algorithms to process RkNN queries that significantly outperform existing best known techniques for both the snapshot and continuous RkNN queries. We also present a detailed theoretical analysis to analyse the area of the influence zone and IO costs of our RkNN processing algorithms. Our experiments demonstrate the accuracy of our theoretical analysis.

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“…Wu et al [15] propose the first technique to monitor RkNNs. Their technique is based on the six-regions based RNN monitoring presented in [14].…”

Section: A Problem Definitionmentioning

confidence: 99%

Influence zone: Efficiently processing reverse k nearest neighbors queries

Cheema

Lin

Zhang

et al. 2011

2011 IEEE 27th International Conference on Data Engineering

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“…Tian Xia [36] utilized SAA with a grid index to answer continuous RNN. Wei Wu [37] proposed an algorithm for answering continuous reverse k-nearest-neighbour monitoring. His approach was based on SAA with the use of a grid index structure.…”

Section: B Rnn Querymentioning

confidence: 99%

Continuous Mutual Nearest Neighbour Processing on Moving Objects in Spatiotemporal Datasets

Ghorbani¹,

Mobini²,

Minaei‐Bidgoli³

2017

IJIET

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Abstract-This paper proposed a new algorithm for answering a novel kind of nearest neighbour search, that is, continuous mutual nearest neighbour (CMNN) search. In this kind of query, by providing a set of objects O and a query object q, CMNN continuously returns the set of objects from O, which is among the k 1 nearest neighbours of q; meanwhile, q is one of their k 2 nearest neighbours. CMNN queries are important in many applications such as decision making, pattern recognition and although it is useful in service providing systems, such as police patrol, taxi drivers, mobile car repairs and so forth. In this paper, we have proposed the first work for handling CMNN queries efficiently, without any assumption on object movements. The most important feature of this work is incremental evaluation and scalability. Utilizing an incremental evaluation technique led to a significant decrease in processing time.Index Terms-Moving objects, nearest neighbor, query processing, spatio-temporal.

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“…However, the advantage of the six-regions based pruning is that it is computationally less expensive. Six-region [2] and Slice [10] are the most notable algorithms that use six-regions based pruning whereas TPL [5], FINCH [20], InfZone [8,21], and TPL++ [6] are some of the remarkable algorithms that employ half-space based pruning. The details of these algorithms can be found in a recent survey paper [6].…”

Section: Related Workmentioning

confidence: 99%

“…In the absence of a well-known competitor, readers may find it harder to evaluate the efficiency of an algorithm. Therefore, we compare our algorithm with the most well-known RNN algorithms, namely Slice [10], InfZone [8], TPL [5], FINCH [20] and six-regions [2]. For our algorithm, we set x = 1 + 10 −6 because we note that the results of an RRNN query is the same as those of an RNN query if x is very close to 1.…”

Section: Evaluating Performancementioning

confidence: 99%

Relaxed Reverse Nearest Neighbors Queries

Hidayat

Cheema

Taniar

2015

Advances in Spatial and Temporal Databases

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Abstract. Given a set of users U , a set of facilities F , and a query facility q, a reverse nearest neighbors (RNN) query retrieves every user u for which q is its closest facility. Since q is the closest facility of u, the user u is said to be influenced by q. In this paper, we propose a relaxed definition of influence where a user u is said to be influenced by not only its closest facility but also every other facility that is almost as close to u as its closest facility is. Based on this definition of influence, we propose relaxed reverse nearest neighbors (RRNN) queries. Formally, given a value of x > 1, an RRNN query q returns every user u for which dist(u, q) ≤ x × N N Dist (u) where N N Dist(u) denotes the distance between a user u and its nearest facility. Based on effective pruning techniques and several non-trivial observations, we propose an efficient RRNN query processing algorithm. Our extensive experimental study conducted on several real and synthetic data sets demonstrates that our algorithm is several orders of magnitude better than a naïve algorithm as well as a significantly improved version of the naïve algorithm.

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Continuous Reverse k-Nearest-Neighbor Monitoring

Abstract: The processing of a Continuous Reverse k- NearestNeighbor (CRkNN)

Cited by 46 publications

References 13 publications

Influence zone: Efficiently processing reverse k nearest neighbors queries

Influence zone: Efficiently processing reverse k nearest neighbors queries

Continuous Mutual Nearest Neighbour Processing on Moving Objects in Spatiotemporal Datasets

Relaxed Reverse Nearest Neighbors Queries

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