Efficient method for maximizing bichromatic reverse nearest neighbor

Wong, Raymond Chi-Wing; Özsu, M. Tamer; Yu, Philip S.; Fu, Ada Wai-Chee; Liu, Lian

doi:10.14778/1687627.1687754

Cited by 115 publications

(103 citation statements)

References 13 publications

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“…For each segment (i.e., sS ), HRSFMaxRS checks if the edge contains this segment. If the corresponding edge was processed (i.e., the edge is in the list called completeEdges), HRSFMaxRS checks the next segment (lines [11][12]. If the edge is not processed, HRSFMaxRS adds the edge to completeEdges, so that it will not process the edge in the future (line 14).…”

Section: Maxrs Query Processing Algorithmmentioning

confidence: 99%

“…Then, EAMaxRS generates segments of these facilities and stores the segments (lines 10-13). EAMaxRS only generates segments of facilities which have not been processed (i.e., the facilities are not in the list called f inishedFacs) (lines [11][12]. The generated segments lying on the same edge are grouped into one edge-record, and the edge-record is inserted into the in-memory HRT or the Fig.…”

Section: The Edge-area Based Algorithmmentioning

confidence: 99%

“…The aim of this facility location optimization problem is to find an optimal location in order to maximize/minimize an objective function. Cabello et al [11] introduced and investigated optimization problems according to the Bichromatic Reverse Nearest Neighbor (BRNN) rule, while Wong et al [12] studied a related problem called MaxBRNN: finding an optimal region that maximizes the size of BRNNs. These two problems studied in L 2 space.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Evaluation of Maximizing Range Sum Queries in a Road Network

Phan

Jung

Youn

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYGiven a set of positive-weighted points and a query rectangle r (specified by a client) of given extents, the goal of a maximizing range sum (MaxRS) query is to find the optimal location of r such that the total weights of all points covered by r is maximized. In this paper, we address the problem of processing MaxRS queries over road network databases and propose two new external memory methods. Through a set of simulations, we evaluate the performance of the proposed methods. key words: facility optimization location, location-based services, maximizing range sum query, road network database, spatial database

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Section: Maxrs Query Processing Algorithmmentioning

confidence: 99%

Section: The Edge-area Based Algorithmmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Evaluation of Maximizing Range Sum Queries in a Road Network

Phan

Jung

Youn

et al. 2016

IEICE Trans. Inf. & Syst.

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“…These objects are said to be influenced by q. Finding the region in space that maximizes the size of bichromatic reverse nearest neighbor is studied in [11]. The reverse skyline set of a query object q can be used to discover objects influenced by q based on dominance relationships [3].…”

Section: Related Workmentioning

confidence: 99%

Identifying the most influential data objects with reverse top-k queries

et al. 2010

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Top-k queries are widely applied for retrieving a ranked set of the k most interesting objects based on the individual user preferences. As an example, in online marketplaces, customers (users) typically seek a ranked set of products (objects) that satisfy their needs. Reversing top-k queries leads to a query type that instead returns the set of customers that find a product appealing (it belongs to the top-k result set of their preferences). In this paper, we address the challenging problem of processing queries that identify the top-m most influential products to customers, where influence is defined as the cardinality of the reverse top-k result set. This definition of influence is useful for market analysis, since it is directly related to the number of customers that value a particular product and, consequently, to its visibility and impact in the market. Existing techniques require processing a reverse top-k query for each object in the database, which is prohibitively expensive even for databases of moderate size. In contrast, we propose two algorithms, SB and BB , for identifying the most influential objects: SB restricts the candidate set of objects that need to be examined, while BB is a branch-and-bound algorithm that retrieves the result incrementally. Furthermore, we propose meaningful variations of the query for most influential objects that are supported by our algorithms. Our experiments demonstrate the efficiency of our algorithms both for synthetic and real-life datasets.

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“…Maximal reverse query processing has been studied as well for skyline (Li et al 2007; Li et al 2006) and kNN (Cabello et al 2005;Wong et al 2009) queries with numeric attributes.…”

Section: Introductionmentioning

confidence: 99%

Smallest Set for Reverse Selection Queries to Satisfy All

Miah¹

2017

IJSEA

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Abstract:In recent years, research has moved from traditional query processing (e.g., Selection, Nearest Neighbor (NN), Top-k, Skyline), to reverse query processing (e.g., Reverse NN, Reverse Top-k, Reverse Skyline), to maximal reverse query processing (e.g., find spatial points that maximize the number of Reverse NNs), and so on. This paper considers the Smallest Set Reverse Selection Queries Problem also known as the Multiple Tuple Design Problem: Given a set of selection queries with conjunctive conditions, where the task is to create the smallest set of tuples such that each query returns at least one of these tuples. The problem is an interesting variant of the Maximal Reverse Selection Queries Problem (also referred to as the Tuple Design Problem) introduced by Miah et al. (2016). The paper shows that the problem is NP-Complete and develops approximation algorithms with provable approximation guarantees, as well as carefully designed heuristics that work well in practice. The paper also designs efficient exact algorithm that are feasible for moderate instances. It provides extensive experiments that demonstrate the effectiveness of the proposed algorithms.

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Efficient method for maximizing bichromatic reverse nearest neighbor

Cited by 115 publications

References 13 publications

Efficient Evaluation of Maximizing Range Sum Queries in a Road Network

Efficient Evaluation of Maximizing Range Sum Queries in a Road Network

Identifying the most influential data objects with reverse top-k queries

Smallest Set for Reverse Selection Queries to Satisfy All

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