Efficient probabilistic reverse nearest neighbor query processing on uncertain data

Bernecker, Thomas; Emrich, Tobias; Kriegel, Hans‐Peter; Renz, Matthias; Zankl, Stefan; Züfle, Andreas

doi:10.14778/2021017.2021024

Cited by 38 publications

(34 citation statements)

References 26 publications

(47 reference statements)

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“…The RkNN query has been extensively studied [3,14,15,2,7,5,16,17,4,8,9,18,19,10] ever since it was introduced in [1]. Below, we briefly describe two widely used pruning strategies.…”

Section: Related Workmentioning

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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“…The RkNN query has been extensively studied [3,14,15,2,7,5,16,17,4,8,9,18,19,10] ever since it was introduced in [1]. Below, we briefly describe two widely used pruning strategies.…”

Section: Related Workmentioning

confidence: 99%

Relaxed Reverse Nearest Neighbors Queries

Hidayat

Cheema

Taniar

2015

Advances in Spatial and Temporal Databases

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“…We follow the commonly used uncertainty model Block-Independent Disjoint Scheme [3] to define the problem of uncertainty co-location. A probabilistic spatial feature s is given by a set of uncertain events s = {e 1 , e 2 , .…”

Section: Problem Definitionmentioning

confidence: 99%

Mining Co-locations under Uncertainty

Zhi

Huang

2013

Lecture Notes in Computer Science

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Abstract. A co-location pattern represents a subset of spatial features whose events tend to locate together in spatial proximity. The certain case of the co-location pattern has been investigated. However, location information of spatial features is often imprecise, aggregated, or error prone. Because of the continuity nature of space, over-counting is a major problem. In the uncertain case, the problem becomes more challenging. In this paper, we propose a probabilistic participation index to measure co-location patterns based on the well-known possible world model. To avoid the exponential cost of calculating participation index from all possible worlds, we prove a lemma that allows for instance centric counting, avoids over-counting, and produces the same results as using possible world based counting. We use this property to develop efficient mining algorithms. We observed through both algebraic analysis and extensive experiments that the feature tree based algorithm outperforms uncertain Apriori algorithm by an order of magnitude not only for co-locations of large sizes but also for datasets with high level of uncertainty. This is an important insight in mining uncertainty co-locations.

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“…For instance, a location value, obtained by a GPS sensor, can be represented by a Gaussian distribution [15]; the temperature, humidity, and wind speed values obtained at a sensor node is a threedimensional attribute with some probability distribution [3]. Here, we adopt the discrete model [13], [14], where o's uncertainty pdf is represented by a set of d-dimensional points, or "instances". Each instance is assigned the probability of being the exact representation of o.…”

Section: Consider a D-dimensional Domain D Where D ⊆ ℜmentioning

confidence: 99%

“…Other variants of PNNQs, such as group NN [12] and reverse NN [13], [14], have also been studied. In all these works, the R-tree was used to support efficient object retrieval.…”

Section: • Formentioning

confidence: 99%

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Voronoi-based nearest neighbor search for multi-dimensional uncertain databases

Zhang

Cheng

Mamoulis

et al. 2013

2013 IEEE 29th International Conference on Data Engineering (ICDE)

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Abstract-In Voronoi-based nearest neighbor search, the Voronoi cell of every point p in a database can be used to check whether p is the closest to some query point q. We extend the notion of Voronoi cells to support uncertain objects, whose attribute values are inexact. Particularly, we propose the Possible Voronoi cell (or PV-cell). A PV-cell of a multi-dimensional uncertain object o is a region R, such that for any point p ∈ R, o may be the nearest neighbor of p. If the PV-cells of all objects in a database S are known, they can be used to identify objects that have a chance to be the nearest neighbor of q.However, there is no efficient algorithm for computing an exact PV-cell. We hence study how to derive an axis-parallel hyper-rectangle (called the Uncertain Bounding Rectangle, or UBR) that tightly contains a PV-cell. We further develop the PV-index, a structure that stores UBRs, to evaluate probabilistic nearest neighbor queries over uncertain data. An advantage of the PVindex is that upon updates on S, it can be incrementally updated. Extensive experiments on both synthetic and real datasets are carried out to validate the performance of the PV-index.

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Efficient probabilistic reverse nearest neighbor query processing on uncertain data

Cited by 38 publications

References 26 publications

Relaxed Reverse Nearest Neighbors Queries

Relaxed Reverse Nearest Neighbors Queries

Mining Co-locations under Uncertainty

Voronoi-based nearest neighbor search for multi-dimensional uncertain databases

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