Finding the optimal location and keywords in obstructed and unobstructed space

Choudhury, Farhana Murtaza; Culpepper, J. Shane; Bao, Zhifeng; Sellis, Timos

doi:10.1007/s00778-018-0504-y

Cited by 9 publications

(5 citation statements)

References 30 publications

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“…In Eq. 1, f g (u, p) denotes the geographical proximity measured by the shortest path distance between u and p in G r , f t (u, p) represents the textual similarity computed by the TF-IDF metric [9], [31], and f s (u, p) is the social relevance whose formulation follows the literature [44], [45]. The parameter α ∈ [0, 1] balances the importance of social relevance and textual similarity.…”

Section: Preliminariesmentioning

confidence: 99%

“…We also note that existing related studies [9], [10], [13], [20] aim at enhancing the BRkNN results for a single target. Whereas, for sellers with multiple stores or services, a combined promotion for multiple targets is likely to be preferable over a single promotion target.…”

Section: Introductionmentioning

confidence: 96%

“…The set of BRkNN results is also referred as the influence set of p, and can be utilized for finding prospective customers [45]. Based on BRkNN, the MaxBRkNN query and its variants are proposed to maximize the size of BRkNN results via optimal location selection [40] or geo-textual tags recommendation [9], [10], and thus target the marketing and promotion of geo-located services.…”

Section: Introductionmentioning

confidence: 99%

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Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors in Geo-Social Networks

Jin¹,

Chen²,

Gao³

et al. 2022

Preprint

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Geo-social networks offer opportunities for the marketing and promotion of geo-located services. In this setting, we explore a new problem, called Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors (MaxInfBRkNN). The objective is to find a set of points of interest (POIs), which are geo-textually and socially attractive to social influencers who are expected to largely promote the POIs through online influence propagation. In other words, the problem is to detect an optimal set of POIs with the largest word-of-mouth (WOM) marketing potential. This functionality is useful in various reallife applications, including social advertising, location-based viral marketing, and personalized POI recommendation. However, solving MaxInfBRkNN with theoretical guarantees is challenging, because of the prohibitive overheads on BRkNN retrieval in geo-social networks, and the NP and #P-hardness in searching the optimal POI set. To achieve practical solutions, we present a framework with carefully designed indexes, efficient batch BRkNN processing algorithms, and alternative POI selection policies that support both approximate and heuristic solutions. Extensive experiments on real and synthetic datasets demonstrate the good performance of our proposed methods.

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Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors in Geo-Social Networks

Jin¹,

Chen²,

Gao³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Maximized Bichromatic Reverse k Nearest Neighbor (MaxBRkNN). The MaxBRkNN queries [9,20,28,29] aim to find the optimal location to establish a new store such that it is a kNN of the maximum number of users based on the spatial distance between the store and users' locations. Different spatial properties are exploited to develop efficient algorithms, such as space partitioning [29], intersecting geometric shapes [28], and sweep-line techniques [20].…”

Section: Related Workmentioning

confidence: 99%

Towards an Optimal Outdoor Advertising Placement

Zhang¹,

Bao

et al. 2020

ACM Trans. Knowl. Discov. Data

Self Cite

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In this article, we propose and study the problem of trajectory-driven influential billboard placement: given a set of billboards U (each with a location and a cost), a database of trajectories T , and a budget L , we find a set of billboards within the budget to influence the largest number of trajectories. One core challenge is to identify and reduce the overlap of the influence from different billboards to the same trajectories, while keeping the budget constraint into consideration. We show that this problem is NP-hard and present an enumeration based algorithm with (1-1/e) approximation ratio. However, the enumeration would be very costly when | U | is large. By exploiting the locality property of billboards’ influence, we propose a partition-based framework PartSel. PartSel partitions U into a set of small clusters, computes the locally influential billboards for each cluster, and merges them to generate the global solution. Since the local solutions can be obtained much more efficiently than the global one, PartSel would reduce the computation cost greatly; meanwhile it achieves a non-trivial approximation ratio guarantee. Then we propose a LazyProbe method to further prune billboards with low marginal influence, while achieving the same approximation ratio as PartSel. Next, we propose a branch-and-bound method to eliminate unnecessary enumerations in both PartSel and LazyProbe, as well as an aggregated index to speed up the computation of marginal influence. Experiments on real datasets verify the efficiency and effectiveness of our methods.

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“…Specific spatial operations Existing work in this category has mainly focused on implementing specific spatial operations as MapReduce jobs in Hadoop. Examples of this work have focused on R-tree construction [9], range queries over spatial points [55], range queries over trajectory data [36], k-nearest-neighbor (kNN) queries [2,20,39,55], all nearest-neighbor (ANN) queries [50], reverse nearestneighbor (RNN) queries [2], spatial join [55], exact kNN Join [35], approximate kNN Join [53], and optimal location selection/searching algorithms [10,48]. In all these algorithms, the underlying Hadoop system is used as-is, and the spatial query processing is provided by implementing the spatial query processing as map and reduce functions.…”

Section: Related Workmentioning

confidence: 99%

Scalable computational geometry in MapReduce

Eldawy

Xue

et al. 2019

The VLDB Journal

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Hadoop, employing the MapReduce programming paradigm, has been widely accepted as the standard framework for analyzing big data in distributed environments. Unfortunately, this rich framework has not been exploited for processing large-scale computational geometry operations. This paper introduces CG_Hadoop; a suite of scalable and efficient MapReduce algorithms for various fundamental computational geometry operations, namely polygon union, Voronoi diagram, skyline, convex hull, farthest pair, and closest pair, which present a set of key components for other geometric algorithms. For each computational geometry operation, CG_Hadoop has two versions, one for the Apache Hadoop system and one for the SpatialHadoop system, a Hadoop-based system that is more suited for spatial operations. These proposed algorithms form the nucleus of a comprehensive MapReduce library of computational geometry operations. Extensive experimental results run on a cluster of 25 machines over datasets of size up to 3.8B records show that CG_Hadoop achieves up to 14x and 115x better performance than traditional algorithms when using Hadoop and SpatialHadoop systems, respectively.

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Finding the optimal location and keywords in obstructed and unobstructed space

Cited by 9 publications

References 30 publications

Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors in Geo-Social Networks

Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors in Geo-Social Networks

Towards an Optimal Outdoor Advertising Placement

Scalable computational geometry in MapReduce

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