Processing Distance Join Queries with Constraints

Papadopoulos, Apostolos N.; Νανόπουλος, Αλέξανδρος; Manolopoulos, Yannis

doi:10.1093/comjnl/bxl002

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…-implement other DBJQs in SpatialHadoop, like the KNN join query framework [15] and distance join queries with spatial constraints [63], -implement other complex spatial queries in SpatialHadoop, like multi-way spatial joins [64] and multiway distance joins queries [65], -implement other partitioning techniques [66,67] in SpatialHadoop, because this is an important factor for processing distance-based join queries, as we have demonstrated. -implement KCPQs and εDJQs in Spark-based distributed spatial data management systems, like LocationSpark [31].…”

Section: Concluding Remarks and Future Workmentioning

confidence: 99%

Efficient large-scale distance-based join queries in spatialhadoop

et al. 2017

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Efficient processing of Distance-Based Join Queries (DBJQs) in spatial databases is of paramount importance in many application domains. The most representative and known DBJQs are the K Closest Pairs Query (KCPQ) and the ε Distance Join Query (εDJQ). These types of join queries are characterized by a number of desired pairs (K) or a distance threshold (ε) between the components of the pairs in the final result, over two spatial datasets. Both are expensive operations, since two spatial datasets are combined with additional constraints. Given the increasing volume of spatial data originating from multiple sources and stored in distributed servers, it is not always efficient to perform DBJQs on a centralized server. For this reason, this paper addresses the problem of computing DBJQs on big spatial datasets in SpatialHadoop, an extension of Hadoop that supports efficient processing of spatial queries in a cloud-based setting. We propose novel algorithms, based on plane-sweep, to perform efficient parallel DBJQs on large-scale spatial datasets in SpatialHadoop. We evaluate the performance of the proposed algorithms in several situations with large real-world as well as synthetic datasets. The experiments demon-A preliminary partial version of this work appeared in [1].

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Section: Concluding Remarks and Future Workmentioning

confidence: 99%

Efficient large-scale distance-based join queries in spatialhadoop

et al. 2017

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“…Corral et al [8] introduce the k-multiway distance join, which involves n spatial data sets, a query graph QG (i.e., a weighted directed graph that defines directed itineraries between the n input data sets), and a cardinality threshold k; the answer is a set of k distinct n-tuples (i.e., tuples of n data objects from the n data sets obeying the QG) with the k smallest D distance -value which is the value of a linear function of distances of the n data objects that constitute this n-tuple, according to the edges of the QG. Recently, the problem of processing distance join queries with spatial region constraints has also been investigated in [32], [34].…”

Section: Distance Join Queriesmentioning

confidence: 99%

Optimal-Location-Selection Query Processing in Spatial Databases

Gao

Zheng

Chen

et al. 2009

IEEE Trans. Knowl. Data Eng.

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Abstract-This paper introduces and solves a novel type of spatial queries, namely, Optimal-Location-Selection (OLS) search, which has many applications in real life. Given a data object set D A , a target object set D B , a spatial region R, and a critical distance d c in a multidimensional space, an OLS query retrieves those target objects in D B that are outside R but have maximal optimality. Here, the optimality of a target object b 2 D B located outside R is defined as the number of the data objects from D A that are inside R and meanwhile have their distances to b not exceeding d c . When there is a tie, the accumulated distance from the data objects to b serves as the tie breaker, and the one with smaller distance has the better optimality. In this paper, we present the optimality metric, formalize the OLS query, and propose several algorithms for processing OLS queries efficiently. A comprehensive experimental evaluation has been conducted using both real and synthetic data sets to demonstrate the efficiency and effectiveness of the proposed algorithms.

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“…For the KCPQ in spatial databases using R-trees, [19,33,15,16,29] are the most representative references in the literature. In [15,16] non-incremental recursive (DFBnB) and non-recursive (BFS) algorithms were presented for solving the KCPQ in spatial databases.…”

Section: Distance-based Queries Using R-treesmentioning

confidence: 99%

“…In [15,16] non-incremental recursive (DFBnB) and non-recursive (BFS) algorithms were presented for solving the KCPQ in spatial databases. Recently, in [29], the KCPQ with spatial constraints is addressed from the non-incremental processing point of view. The main issue of the non-incremental variant is to separate the treatment of the terminal candidates (the elements of the leaf nodes) from the rest of the candidates (internal nodes).…”

Section: Distance-based Queries Using R-treesmentioning

confidence: 99%

“…Most these algorithms are focused in the KNNQ over R-trees as spatial access method, from the incremental [20,31] or nonincremental [30,10] point of view. For the KCPQ in spatial databases using R-trees, [19,33,15,16,29] are the most representative references in the literature. In [19,33], incremental algorithms based on Best-First Search and additional priority queues were presented; and in [15,16], non-incremental algorithms following DepthFirst and Best-First Searches were proposed for solving the KCPQ in spatial databases (in [19] is studied KCPQ with spatial constraints).…”

Section: Introductionmentioning

confidence: 99%

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A performance comparison of distance-based query algorithms using R-trees in spatial databases

Corral

Almendros-Jiménez

2007

Information Sciences

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Efficient processing of distance-based queries (DBQs) is of great importance in spatial databases due to the wide area of applications that may address such queries. The most representative and known DBQs are the K Nearest Neighbors Query (KNNQ), q Distance Range Query (qDRQ), K Closest Pairs Query (KCPQ) and q Distance Join Query (qDJQ). In this paper, we propose new pruning mechanism to apply them in the design of new Recursive Best-First Search (RBFS) algorithms for DBQs between spatial objects indexed in R-trees. RBFS is a general search algorithm that runs in linear space and expands nodes in best-first order, but it can suffer from node re-expansion overhead (i.e. to expand nodes in best-first order, some nodes can be considered more than once). The R-tree and its variations are commonly cited spatial access methods that can be used for answering such spatial queries. Moreover, an exhaustive experimental study was also included using R-trees, which resulted to several conclusions about the efficiency of proposed RBFS algorithm and its comparison with respect to other search algorithms (Best-First Search (BFS) and Depth-First Branch-and-Bound (DFBnB)), in terms of disk accesses, response time and main memory requirements, taking into account several important parameters as maximum branching factor (Cmax), cardinality of the final query result (K), distance threshold (q) and size of a global LRU buffer (B). In general RBFS is competitive for KNNQ and KCPQ where the maximum branching factor (Cmax) is large enough (even better than DFBnB and very close to BFS), and it is a good alternative when we have main memory limitations in our computer due to high process overload in our system, since it is linear space consuming with respect to the height of the R-trees. Nevertheless, RBFS is the worst alternative for qDRQ and qDJQ. DFBnB is also a linear space algorithm and it obtains the same behavior as BFS for qDRQ and qDJQ; and it is the best when an LRU buffer was included. Finally, we have been able to check experimentally that BFS is the best for all DBQs, but it can consume many main memory resources to perform spatial queries.

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Processing Distance Join Queries with Constraints

Cited by 12 publications

References 25 publications

Efficient large-scale distance-based join queries in spatialhadoop

Efficient large-scale distance-based join queries in spatialhadoop

Optimal-Location-Selection Query Processing in Spatial Databases

A performance comparison of distance-based query algorithms using R-trees in spatial databases

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