Ranking in spatial databases

Hjaltason, Gı́sli R.; Samet, Hanan

doi:10.1007/3-540-60159-7_6

Cited by 239 publications

(160 citation statements)

References 7 publications

Supporting

Mentioning

156

Contrasting

Unclassified

Order By: Relevance

“…Incremental algorithms report the objects one by one to allow the algorithm to operate in a pipelined fashion. They are especially suitable for complex queries involving proximity [3].…”

Section: K-nearest Neighbor Searchmentioning

confidence: 99%

Search K Nearest Neighbors on Air

Zheng

Lee

2002

Mobile Data Management

View full text Add to dashboard Cite

Abstract. While the K-Nearest-Neighbor (KNN) problem is well studied in the traditional wired, disk-based client-server environment, it has not been tackled in a wireless broadcast environment. In this paper, the problem of organizing location dependent data and answering KNN queries on air are investigated. The linear property of wireless broadcast media and power conserving requirement of mobile devices make this problem particularly interesting and challenging. An efficient data organization, called sorted list, and the corresponding search algorithm are proposed and compared with the well-known spatial index, R-Tree. In addition, we develop an approximate search scope to guide the search at the very beginning of the search process and a learning algorithm to adapt the search scope during the search to improve energy and access efficiency. Simulation based performance evaluation is conducted to compare sorted list and R-Tree. The results show that the utilization of search scope and learning algorithm improves search efficiency of both index mechanisms significantly. While R-Tree is more power efficient when a large number of nearest neighbors is requested, the sorted list has better access efficiency and less power consumption when the number of nearest neighbors is small.

show abstract

“…Incremental algorithms report the objects one by one to allow the algorithm to operate in a pipelined fashion. They are especially suitable for complex queries involving proximity [3].…”

Section: K-nearest Neighbor Searchmentioning

confidence: 99%

Search K Nearest Neighbors on Air

Zheng

Lee

2002

Mobile Data Management

View full text Add to dashboard Cite

show abstract

“…A typical k-NN search involving R-trees follows a branchand-bound strategy traversing the index top down. It maintains a priority queue of "active" minimum bounding rectangles (MBR's) [8,9]. At each step, it maintains a bound to the k-NN distance, δ, from the query point.…”

Section: Utilizing Histograms In K-nn Queriesmentioning

confidence: 99%

“…Most of them use a high-dimensional indexing structure, such as an R-tree [7] or one of its variations. For instance, in the case of an R-tree, these algorithms use a branch-and-bound approach to traverse the tree top down, and use distance bounds between objects to prune branches (minimum-bounding rectangles, MBR's) that do not need to be considered [8,9]. A priority queue of interior nodes is maintained based on their distances to the query object.…”

Section: Introductionmentioning

confidence: 99%

NNH: Improving Performance of Nearest-Neighbor Searches Using Histograms

Jin

Koudas

2004

Advances in Database Technology - EDBT 2004

View full text Add to dashboard Cite

Abstract. Efficient search for nearest neighbors (NN) is a fundamental problem arising in a large variety of applications of vast practical interest. In this paper we propose a novel technique, called NNH ("Nearest Neighbor Histograms"), which uses specific histogram structures to improve the performance of NN search algorithms. A primary feature of our proposal is that such histogram structures can co-exist in conjunction with a plethora of NN search algorithms without the need to substantially modify them. The main idea behind our proposal is to choose a small number of pivot objects in the space, and pre-calculate the distances to their nearest neighbors. We provide a complete specification of such histogram structures and show how to make use of the information they provide towards more effective searching. In particular, we show how to construct them, how to decide the number of pivots, how to choose pivot objects, how to incrementally maintain them under dynamic updates, and how to utilize them in conjunction with a variety of NN search algorithms to improve the performance of NN searches. Our intensive experiments show that nearest neighbor histograms can be efficiently constructed and maintained, and when used in conjunction with a variety of algorithms for NN search, they can improve the performance dramatically.

show abstract

“…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].…”

Section: Closest Pair Queriesmentioning

confidence: 99%

“…Our k-NN similarity join algorithm used the Multipage Index [7] which allows a separate optimization of CPU and I/O performance. The competitive technique, the evaluation on top of single similarity queries, was also supported by the same index structure which is traversed using a variation of the nearest neighbor algorithm by Hjaltason and Samet [14] which has been shown to yield an optimal number of page accesses. All our experiments were carried out under Windows NT4.0 SP6 on Fujitsu-Siemens Celsius 400 machines equipped with a Pentium III 700 MHz processor and at least 128 MB main memory.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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

Ranking in spatial databases

Cited by 239 publications

References 7 publications

Search K Nearest Neighbors on Air

Search K Nearest Neighbors on Air

NNH: Improving Performance of Nearest-Neighbor Searches Using Histograms

Supporting KDD Applications by the k-Nearest Neighbor Join

Contact Info

Product

Resources

About