Incremental distance join algorithms for spatial databases

Hjaltason, Gı́sli R.; Samet, Hanan

doi:10.1145/276304.276326

Cited by 176 publications

(159 citation statements)

References 24 publications

(3 reference statements)

Supporting

Mentioning

157

Contrasting

Unclassified

Order By: Relevance

“…figure 1b) Closest pair queries do not only play an important role in the database research but have also a long history in computational geometry [19]. In the database context, the operation has been introduced by Hjaltason and Samet [15] using the term (k-) distance join. The (k-)closest pair query can be formally defined as follows:…”

Section: Closest Pair Queriesmentioning

confidence: 99%

“…It is also possible to change definition 2 such that the tie is broken non-deterministically by a random selection. [15] defines the closest pair query (non-deterministically) by the following SQL statement:…”

Section: Closest Pair Queriesmentioning

confidence: 99%

“…It is important to avoid computing the complete ranking in the initialization phase of the cursor, because determining the complete ranking is unnecessarily expensive if the user decides to stop the ranking after retrieving only a few result pairs. Hjaltason and Samet [15] also define the distance semijoin which performs a GROUP BY operation on the result of the distance join. All join operations, k-distance join, incremental distance join and the distance semijoin are evaluated using a pqueue data structure where node-pairs are ordered by increasing distance.…”

Section: Closest Pair Queriesmentioning

confidence: 99%

“…Analogously to the various strategies for single nearest neighbor queries such as [20] and [14], Corral et al propose 5 different strategies including recursive algorithms and an algorithm based on a pqueue [11]. Shin et al [22] proposed a plane sweep algorithm for the node expansion for the above mentioned pqueue algorithm [15,11]. In the same paper [22], Shim et al also propose the adaptive multi-stage algorithm which employs aggressive pruning and compensation methods based on statistical estimates of the expected distance values.…”

Section: Closest Pair Queriesmentioning

confidence: 99%

See 3 more Smart Citations

Supporting KDD Applications by the k-Nearest Neighbor Join

Böhm¹,

Krebs²

2003

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. The similarity join has become an important database primitive to support similarity search and data mining. A similarity join combines two sets of complex objects such that the result contains all pairs of similar objects. Well-known are two types of the similarity join, the distance range join where the user defines a distance threshold for the join, and the closest point query or k-distance join which retrieves the k most similar pairs. 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. We discover that many standard algorithms of Knowledge Discovery in Databases (KDD) such as k-means and k-medoid clustering, nearest neighbor classification, data cleansing, postprocessing of sampling-based data mining etc. can be implemented on top of the k-nn join operation to achieve performance improvements without affecting the quality of the result of these algorithms. Our list of possible applications includes standard methods for all stages of the KDD process including preprocessing, data mining, and postprocessing. Thus, our method is turbo charging the complete KDD process.

show abstract

Section: Closest Pair Queriesmentioning

confidence: 99%

Section: Closest Pair Queriesmentioning

confidence: 99%

Section: Closest Pair Queriesmentioning

confidence: 99%

Section: Closest Pair Queriesmentioning

confidence: 99%

See 2 more Smart Citations

Supporting KDD Applications by the k-Nearest Neighbor Join

Böhm¹,

Krebs²

2003

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…An application on their hint mechanism was to design a hybrid replacement strategy, combining the LRU and MRU page replacement policies. There are several studies on spatial queries involving more than one R-tree, and most of them examine the use of buffering to reduce the I/O activity [3,5,7,11,12,17].…”

Section: Related Work and Motivationmentioning

confidence: 99%

The Impact of Buffering on Closest Pairs Queries Using R-Trees

Corral

Vassilakopoulos

Manolopoulos

2001

Advances in Databases and Information Systems

View full text Add to dashboard Cite

Abstract. In this paper, the most appropriate buffer structure, page replacement policy and buffering scheme for closest pairs queries, where both spatial datasets are stored in R-trees, are investigated. Three buffer structures (i.e. single, hybrid and by levels) over two buffering schemes (i.e. local to each R-tree, and global to the query) using several page replacement algorithms (e.g. FIFO, LRU, 2Q, etc.) are studied. In order to answer K closest pair queries (K-CPQs, with K ≥ 1) we employ recursive and non-recursive (iterative) branch-and-bound algorithms. The outcome of this study is the derivation of the outperforming configuration (in terms of buffer structure, page replacement algorithm and buffering scheme) for CPQs. In all cases, the savings in disk accesses is larger for a recursive algorithm than for a non-recursive one, in the presence of buffer space. Also, the global buffering scheme is more appropriate for small or medium buffer sizes for recursive algorithms, whereas the local scheme is the best choice for large buffers. If we use non-recursive algorithms, the global buffering scheme is the best choice in all cases. Moreover, LRU is the most appropriate page replacement algorithm for small or medium buffer sizes for both types of branch-and-bound algorithms. FIFO and LRU are the best choices for recursive algorithms and 2Q for the non-recursive ones, when the buffer is large enough.

show abstract

k‐nearest reliable neighbor search in crowdsourced LBSs

Jang

Kim

Jung

2019

Int J Communication

View full text Add to dashboard Cite

Summary To improve the quality of spatial information in a location‐based services (LBS), crowdsourced LBS (cLBS) applications that receive additional information such as the visit time of static spatial objects from users have appeared. In this paper, we propose a new type of nearest neighbor (NN) query called the k‐nearest reliable neighbor (kNRN) query, which searches for objects that are likely to exist. Suppose that in cLBSs, the user wants to find a restaurant that is likely to exist and is close to the user. In such a case, a kNRN query is highly recommended. In this paper, we formally define a data model in cLBSs and define reliable objects and a kNRN problem. As a brute‐force approach to this problem in a massive dataset that has large computational and I/O costs, we propose a 3DR‐tree‐based baseline algorithm, 2DR‐tree‐based incremental algorithm, and an a3DR‐tree‐based branch‐and‐bound algorithm for kNRN queries. A performance study is conducted on both synthetic and real datasets. Our experimental results show the efficiency of our proposed methods.

show abstract

Incremental distance join algorithms for spatial databases

Cited by 176 publications

References 24 publications

Supporting KDD Applications by the k-Nearest Neighbor Join

Supporting KDD Applications by the k-Nearest Neighbor Join

The Impact of Buffering on Closest Pairs Queries Using R-Trees

k‐nearest reliable neighbor search in crowdsourced LBSs

Contact Info

Product

Resources

About