Maximizing Reverse k-Nearest Neighbors for Trajectories

Rahat, Tamjid Al; Arman, Arif; Ali, Mohammed Eunus

doi:10.1007/978-3-319-92013-9_21

Cited by 5 publications

(7 citation statements)

References 14 publications

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“…Recently, inspired by profile-based marketing [40], the MaxBRkNN query that maximizes the result size of BRkNNs was studies. Many variants exist, e.g., MaxBRkNN for trajectories [32], MaxBRSTkNN for geo-textual data [10], [13], and MaxBRkNN for streaming geo-data [28], to name just a few. These studies all assign equal importance to the data points in BRkNN, and focus on maximizing the result size.…”

Section: A Brknn and Maxbrknn Queriesmentioning

confidence: 99%

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: A Brknn and Maxbrknn Queriesmentioning

confidence: 99%

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

Jin¹,

Chen²,

Gao³

et al. 2022

Preprint

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“…Given a set of user trajectories U , a set of facility (bus) trajectories F , and a new facility trajectory f ∈ F , an RkNN query returns the user trajectories from U for which f is one of the k nearest facilities. While addressing this problem, Wang et al [32] consider each user trajectory as transitions (trajectories with just pickup and drop-off points), and Rahat et al [23] consider multi-point user trajectories. In contrast to their work, we assume a user can be served by a facility if the trajectories (stop points) are sufficiently close.…”

Section: Related Workmentioning

confidence: 99%

The maximum trajectory coverage query in spatial databases

et al. 2018

Self Cite

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With the widespread use of GPS-enabled mobile devices, an unprecedented amount of trajectory data has become available from various sources such as Bikely, GPS-wayPoints, and Uber. The rise of smart transportation services and recent breakthroughs in autonomous vehicles increase our reliance on trajectory data in a wide variety of applications. Supporting these services in emerging platforms requires more efficient query processing in trajectory databases. In this paper, we propose two new coverage queries for trajectory databases: (i) k Best Facility Trajectory Search (kBFT); and (ii) k Best Coverage Facility Trajectory Search (kBCovFT). We propose a novel index structure, the Trajectory Quadtree (TQtree) that utilizes a quadtree to hierarchically organize trajectories into different nodes, and then applies a z-ordering to further organize the trajectories by spatial locality inside each node. This structure is highly effective in pruning the trajectory search space, which is of independent interest. By exploiting the TQ-tree, we develop a divide-and-conquer approach to efficiently process a kBFT query. To solve the kBCovFT, which is a non-submodular NP-hard problem, we propose a greedy approximation. We evaluate our algorithms through an extensive experimental study on several real datasets, and demonstrate that our algorithms outperform baselines by two to three orders of magnitude.

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“…The query processing on massive data usually takes a "filtering and refining" strategy [16,17]. The filtering stage identifies a set of trajectories from a large number of trajectories as candidate trajectories based on certain criteria (e.g., within a radius of a given trajectory).…”

Section: Computational Solutions To Similarity Queriesmentioning

confidence: 99%

“…Scientists have employed MBRs as a filtering method in the nearest neighbor (NN) search which identifies the nearest feature to a given feature [8]. MBR-based filtering process usually calculates the distances between the MBRs of features [17] (e.g., complex polygons) and sorts the features based on the distances. Because the distance computation is more efficient on MBRs, the filtering can quickly reduce the size of features for the fine level of computation.…”

Section: Mbr and Mindist For Efficient Searchingmentioning

confidence: 99%

“…To integrate the MBR with the filtering stage, we compute the MINDIST and sort the reference trajectories by the MINDIST. Existing studies [17,28] in the field of spatial databases have adopted the concepts of MINDIST and MBR to form a pruning strategy which groups trajectories into two categories. Similarly, we classify the trajectories into two groups based on whether the MINDIST value is 0 or not.…”

Section: Definitionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach

Wang

Zhang

et al. 2018

IJGI

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Performing similarity analysis on trajectories consisting of massive numbers of tracking points is computationally challenging. We introduce a progressive minimum bounding rectangle (MBR) and minimum distance (MINDIST) approach to process the K Best Connected Trajectory (K-BCT) query, which aims to find the top K similarity trajectories to a given query trajectory. Our approach has three unique features to speed up the query. First, the approach builds a series of progressive MBRs from the query trajectory to determine the order of reference trajectories to identify the target top K reference trajectories at an earlier stage. Second, this method introduces a grid-based search method to speed up the matched point detection between two trajectories for similarity measures. Third, this approach further leverages the many-core computing power of Graphical Processing Unit (GPU) devices to perform the query in a parallel manner. We have conducted tests with ship tracking data and human movement data using GPU instances from Amazon Web Services. Preliminary results indicate that (a) parallel computing has greatly improved the efficiency of the query, and (b) our optimized approach can further speedup the computation compared to parallel implementations.

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Maximizing Reverse k-Nearest Neighbors for Trajectories

Cited by 5 publications

References 14 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

The maximum trajectory coverage query in spatial databases

Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach

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