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Efficient k-Closest-Pair Range-Queries in Spatial Databases

Shaojie Qiao, Changjie Tang, Jing Peng, Hongjun Li, Shengqiao Ni
2008 2008 The Ninth International Conference on Web-Age Information Management  
In order to efficiently retrieve the k closest pairs between two spatial data sets in a specified space, such as in GIS and CAD applications, we propose a novel algorithm to handle the k-closest-pair range-query  ...  In particular, this technique works well when two spatial data sets are identical. The Ninth International Conference on Web-Age Information Management 978-0-7695-3185-4/08 $25.00  ...  One important spatial database query is called "k Closest Pairs Query" (k-CPQ), which combines join and nearest neighbor queries in order to find the closest k pairs of spatial objects from two distinct  ... 
doi:10.1109/waim.2008.12 dblp:conf/waim/QiaoTPLN08 fatcat:3fe446m5bjg3lapx4clb2gh7ri

Processing Constrained k-Closest Pairs Queries in Crime Databases [chapter]

Shaojie Qiao, Changjie Tang, Huidong Jin, Shucheng Dai, Xingshu Chen, Michael Chau, Jian Hu
2009 Annals of Information Systems  
called Growing Window based Constrained k-Closest Pairs (GWCCP).  ...  Recently, spatial analysis in crime databases has attracted increased attention.  ...  The problem of "k-Closest Pairs Query" (k-CPQ) is an extension by combining nearest neighbor query with spatial join in order to find the k-closest pairs of spatial objects from two distinct data sets  ... 
doi:10.1007/978-1-4419-1325-8_4 fatcat:ttqp3gecxjfdfme65qqpxnb7iu

On Spatial-Range Closest-Pair Query [chapter]

Jing Shan, Donghui Zhang, Betty Salzberg
2003 Lecture Notes in Computer Science  
An important query for spatial database research is to find the closest pair of objects in a given space.  ...  Existing work assumes two objects of the closest pair come from two different data sets indexed by R-trees. The closest pair in the whole space will be found via an optimzed R-tree join technique.  ...  One important spatial database query is the closest pair (CP) query, which is to find the closest pair of objects among two data sets.  ... 
doi:10.1007/978-3-540-45072-6_15 fatcat:qkgyndzfonhilleqo6cmpri654

Enhancing SpatialHadoop with Closest Pair Queries [chapter]

Francisco García-García, Antonio Corral, Luis Iribarne, Michael Vassilakopoulos, Yannis Manolopoulos
2016 Lecture Notes in Computer Science  
Given two datasets P and Q, the K Closest Pair Query (KCPQ) finds the K closest pairs of objects from P ×Q. It is an operation widely adopted by many spatial and GIS applications.  ...  As a combination of the K Nearest Neighbor (KNN) and the spatial join queries, KCPQ is an expensive operation.  ...  X) in increasing order, and (2) combine one point (pivot) of one set with all the points of the other set satisfying point.x − pivot.x ≤ δ, where δ is the distance of the K-th closest pair found so far  ... 
doi:10.1007/978-3-319-44039-2_15 fatcat:ny5fte4l35g4ppjmqtfln25kqe

Closest Pair Queries with Spatial Constraints [chapter]

Apostolos N. Papadopoulos, Alexandros Nanopoulos, Yannis Manolopoulos
2005 Lecture Notes in Computer Science  
An extension to this problem is to generate the k closest pairs of objects (k-CPQ).  ...  In this work we focus on constrained closest-pair queries (CCPQ), between two distinct datasets D A and D B , where objects from DA must be enclosed by a spatial region R.  ...  In this study, we focus on the k-Semi-Closest-Pair Query (k-SCPQ), and more specifically, on an interesting variation which is derived by applying spatial constraints in the objects of the first dataset  ... 
doi:10.1007/11573036_1 fatcat:uqos5wuburcq3aj4krvmo3676u

Spatial Queries in the Presence of Obstacles [chapter]

Jun Zhang, Dimitris Papadias, Kyriakos Mouratidis, Manli Zhu
2004 Lecture Notes in Computer Science  
Despite the existence of obstacles in many database applications, traditional spatial query processing utilizes the Euclidean distance metric assuming that points in space are directly reachable.  ...  We propose efficient algorithms for the most important query types, namely, range search, nearest neighbors, e-distance joins and closest pairs, considering that both data objects and obstacles are indexed  ...  Several spatial join algorithms have been proposed for the case where only one of the inputs is indexed by an R-tree or no input is indexed.  ... 
doi:10.1007/978-3-540-24741-8_22 fatcat:fw53ai3dgzexnnsgrnj3zjtt4a

Query Processing in Spatial Network Databases [chapter]

Dimitris Papadias, Jun Zhang, Nikos Mamoulis, Yufei Tao
2003 Proceedings 2003 VLDB Conference  
These frameworks are successfully applied to the most popular spatial queries, namely nearest neighbors, range search, closest pairs and edistance joins, in the context of spatial network databases.  ...  Despite the importance of spatial networks in real-life applications, most of the spatial database literature focuses on Euclidean spaces.  ...  We would like to thank Qiongmao Shen and Manli Zhu for helping with the implementation.  ... 
doi:10.1016/b978-012722442-8/50076-8 dblp:conf/vldb/PapadiasZMT03 fatcat:ai2px5dttvhxfdtfc55tos7pyq

Supporting KDD Applications by the k-Nearest Neighbor Join [chapter]

Christian Böhm, Florian Krebs
2003 Lecture Notes in Computer Science  
to a k-closest pair operation in computational geometry, cf. [19] ).  ...  In this paper, we propose an important, third similarity join operation called k-nearest neighbor join which combines each point of one point set with its k nearest neighbors in the other set.  ...  Most related work on join processing using multidimensional index structures is based on the spatial join.  ... 
doi:10.1007/978-3-540-45227-0_50 fatcat:x76wu4j6ijcwhgx6e2cncsz42i

The k-Nearest Neighbour Join: Turbo Charging the KDD Process

Christian B�hm, Florian Krebs
2004 Knowledge and Information Systems  
In this paper, we propose an important, third similarity join operation called k-nearest neighbor join which combines each point of one point set with its k nearest neighbors in the other set.  ...  A similarity join combines two sets of complex objects such that the result contains all pairs of similar objects.  ...  Most related work on join processing using multidimensional index structures is based on the spatial join.  ... 
doi:10.1007/s10115-003-0122-9 fatcat:r3zytdqgoffxpethllx44o2kbe

Query processing in spatial databases containing obstacles

Jun Zhang, Dimitris Papadias, Kyriakos Mouratidis, Zhu Manli
2005 International Journal of Geographical Information Science  
Despite the existence of obstacles in many database applications, traditional spatial query processing assumes that points in space are directly reachable and utilizes the Euclidean distance metric.  ...  In this paper, we study spatial queries in the presence of obstacles, where the obstructed distance between two points is defined as the length of the shortest path that connects them without crossing  ...  Acknowledgements This research was supported by the grant HKUST 6178/04E from Hong Kong RGC and the grant RGg/05 form NTU URC/AcRF.  ... 
doi:10.1080/13658810500286935 fatcat:f5g74lqz2zcshnvvvhaeqwac5i

A Comparison of Distributed Spatial Data Management Systems for Processing Distance Join Queries [chapter]

Francisco García-García, Antonio Corral, Luis Iribarne, George Mavrommatis, Michael Vassilakopoulos
2017 Lecture Notes in Computer Science  
Two of the most studied distance join queries are the K Closest Pair Query (KCPQ) and the ε Distance Join Query (εDJQ).  ...  The KCPQ finds the K closest pairs of points from two datasets and the εDJQ finds all the possible pairs of points from two datasets, that are within a distance threshold ε of each other.  ...  value of the K-th closest pair (β).  ... 
doi:10.1007/978-3-319-66917-5_15 fatcat:7luj3dj4kvfbdf45sqxsxdpvu4

New plane-sweep algorithms for distance-based join queries in spatial databases

George Roumelis, Antonio Corral, Michael Vassilakopoulos, Yannis Manolopoulos
2016 Geoinformatica  
The most representative and studied DJQs are the K Closest Pairs Query (KCPQ) and εDistance Join Query (εDJQ).  ...  These spatial queries involve two spatial data sets and a distance function to measure the degree of closeness, along with a given number of pairs in the final result (K) or a distance threshold (ε).  ...  Acknowledgements Work of all authors funded by the Development of a GeoENvironmental information system for the region of CENtral Greece (GENCENG) project (SYN-ERGASIA 2011 action, supported by the European  ... 
doi:10.1007/s10707-016-0246-1 fatcat:7bwj4hwhsfepnhjzzbhdgbl2ui

Efficient large-scale distance-based join queries in spatialhadoop

Francisco García-García, Antonio Corral, Luis Iribarne, Michael Vassilakopoulos, Yannis Manolopoulos
2017 Geoinformatica  
The most representative and known DBJQs are the K Closest Pairs Query (KCPQ) and the ε Distance Join Query (εDJQ).  ...  Efficient processing of Distance-Based Join Queries (DBJQs) in spatial databases is of paramount importance in many application domains.  ...  K Closest Pairs Query The KCPQ discovers the K pairs of data formed from the elements of two datasets having the K smallest respective distances between them (i.e. it reports only the top K pairs).  ... 
doi:10.1007/s10707-017-0309-y fatcat:iiw3y3jxw5fhlj5xoimbbvi7ta

Multi-Way Distance Join Queries in Spatial Databases

Antonio Corral, Yannis Manolopoulos, Yannis Theodoridis, Michael Vassilakopoulos
2004 Geoinformatica  
This query can be viewed as an extension of K-Closest-Pairs Query (K-CPQ) [CMT + 01] for n inputs.  ...  This paper addresses the problem of finding the K n-tuples between n spatial datasets that have the smallest D distance -values, the so-called K-Multi-Way Distance Join Query (K-MWDJQ), where each set  ...  to consider as related work the cases where only one of the inputs is indexed [LoR94, PRS99, MaP03] or when both inputs are non-indexed [LoR96, PaD96, KoS97] .  ... 
doi:10.1023/b:gein.0000040832.25622.8d fatcat:3vbnkdhaubebxekdy67bubuu4i

Processing Distance Join Queries with Constraints

A. N. Papadopoulos
2005 Computer journal  
An extension to this problem is to generate the k closest pairs of objects (k-CPQ).  ...  In this work, we focus on constrained closest-pair queries, between two distinct datasets D A and D B , where objects from D A must be enclosed by a spatial region R.  ...  Given two spatial datasets D A and D B and a predicate , the output of the spatial join query is a set of pairs o a , o b such that o a 2 D A , o b 2 D B and (o a , o b ) is true. k Closest-pair query  ... 
doi:10.1093/comjnl/bxl002 fatcat:iu6fjwgiwzafnfoqwbq7sd4mbi
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