Continuous maximal reverse nearest neighbor query on spatial networks

Ghaemi, Parisa; Shahabi, Kaveh; Wilson, John P.; Banaei-Kashani, Farnoush

doi:10.1145/2424321.2424330

Cited by 13 publications

(4 citation statements)

References 23 publications

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“…Moreover, in continuous RNN [31], both query points and objects are movable, the aim is to find the reverse NN of a query point over time. Continuous maximal RNN [32] is most related to LS, which continuously finds the optimal network location at every timestamp based on RNN. In a sense, all these efforts can be regarded as multiple queries over static objects at different time instances.…”

Section: Querying Moving Objectsmentioning

confidence: 99%

PINOCCHIO: Probabilistic Influence-Based Location Selection over Moving Objects

Wang¹,

Li²,

Cui³

et al. 2016

IEEE Trans. Knowl. Data Eng.

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The location selection (LS) problem, which aims to mine the optimal location from a set of candidates to place a new facility such that a score (i.e., benefit or influence on some given objects) can be maximized, has drawn significant research attention in recent years. State-of-the-art LS techniques assume each object is static and can only be influenced by a single facility. However, in reality, objects (e.g., people, vehicles) are mobile and are influenced by multiple facilities, which prevents classical LS solutions from selecting accurate results. In this paper, we introduce a generalized LS problem called PRIME-LS which takes mobility and probability factors into consideration to address the aforementioned limitations. Specifically, given a set of candidate locations, PRIME-LS aims to mine the optimal location which can influence the most number of moving objects. Also, to address the problem we propose an efficient algorithm called PINOCCHIO that leverages two pruning rules based on a novel distance measure. These rules enable us to prune many inferior candidate locations prior to influence computation, paving the way to efficient and accurate solution. Furthermore, we extend PINOCCHIO (PINOCCHIO-VO) by incorporating two optimization strategies during candidate validation phase, which further reduce unnecessary computations. Experimental study over two real-world datasets demonstrates superiority of our framework in comparison to state-of-the-art LS techniques.

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Section: Querying Moving Objectsmentioning

confidence: 99%

PINOCCHIO: Probabilistic Influence-Based Location Selection over Moving Objects

Wang¹,

Li²,

Cui³

et al. 2016

IEEE Trans. Knowl. Data Eng.

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“…Many approaches use optimal location queries to evaluate the effectiveness of a location by placing higher priority on locations that are closer to the proposed customer base (Xiao et al, 2011;Ghaemi et al, 2010). The approach of Ghaemi et al (Ghaemi et al, 2012) uses nearest neighbors with results from past optimal location queries to address issues caused by moving locations and customers. Banaei et al (Banaei-Kashani et al, 2014) propose reverse skyline queries to allow optimal location queries to handle multiple criteria such as distance to location and distance to competitors.…”

Section: Related Workmentioning

confidence: 99%

Optimal Estimation of Census Block Group Clusters to Improve the Computational Efficiency of Drive Time Calculations

Gwinn

Helmick²,

Banerjee

et al. 2018

Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management

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Location selection determines the feasibility of a new location by evaluating factors such as the drive time of customers, the number of potential customers, and the number and proximity of competitors to the new location. Traditional location selection approaches use census block group data to determine average customer drive times by computing the drive time from each block group to the proposed location and comparing it to all competitors within the area. However, since companies need to evaluate on the order of hundreds of thousands of potential locations and competitors, traditional location selection approaches prove to be computationally infeasible. In this paper we present an approach that generates an optimal set of clusters to speed up drive time calculations. Our approach is based on the insight that in urban areas block groups are comprised of a few adjacent city blocks, making the differences in drive times between neighboring block groups negligible. We use affinity propagation to initially cluster the census block groups. We use population and average distance between the cluster centroid and all points to recursively re-cluster the initial clusters. Our approach reduces the census data for the United States by 80% which provides a 5× speed when computing drive times. We sample 200 randomly generated locations across the United States and show that there is no statistically significant difference in the drive times when using the raw census data and our recursively clustered data. Additionally, for further validation we select 300 random Walmart stores across the United States and show that there is no statistically significant difference in the drive times.

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“…This important concept has seen a series of remarkable applications and algorithms for processing different types of objects, in various contexts and under variations [Kang et al 2007], ], [Tran et al 2009], [Taniar et al 2011], [Shang et al 2011], [Cheema et al 2012], [Ghaemi et al 2012], [Li et al 2013], [Emrich et al 2014], [Cabello et al 2010], [Bhattacharya and Nandy 2013] of the problem parameters. The focus of this paper is monochromatic RkNN queries -in this version, all objects in the database and the query belong to the same category, unlike the bichromatic version in which the objects can belong to different categories.…”

Section: Introductionmentioning

confidence: 99%

Efficient Reverse k Nearest Neighbor evaluation for hierarchical index

Dawar,

Goyal,

Bera

2015

Preprint

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Reverse Nearest Neighbor" query finds applications in decision support systems, profile-based marketing, emergency services etc. In this paper, we point out a few flaws in the branch and bound algorithms proposed earlier for computing monochromatic RkNN queries over data points stored in hierarchical index. We give suitable counter examples to validate our claims and propose a correct algorithm for the corresponding problem. We show that our algorithm is correct by identifying necessary conditions behind correctness of algorithms for this problem.

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Continuous maximal reverse nearest neighbor query on spatial networks

Cited by 13 publications

References 23 publications

PINOCCHIO: Probabilistic Influence-Based Location Selection over Moving Objects

PINOCCHIO: Probabilistic Influence-Based Location Selection over Moving Objects

Optimal Estimation of Census Block Group Clusters to Improve the Computational Efficiency of Drive Time Calculations

Efficient Reverse k Nearest Neighbor evaluation for hierarchical index

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