A cost model and index architecture for the similarity join

Böhm, Christian; Kriegel, Hans‐Peter

doi:10.1109/icde.2001.914854

Cited by 39 publications

(64 citation statements)

References 36 publications

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“…For similarity joins on high-dimensional point datasets, the most representative papers are [18,14,10,4]. In [18] an index structure (ε-kdB tree) and an algorithm for similarity self-join on high-dimensional points was presented.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…In [10] a new algorithm (Generic External Space Sweep, GESS), which introduces a rate of data replication to reduce the number of distance computations as an enhancement of MSJ, was proposed. In [4], a complex and interesting index architecture (Multipage Index, MuX) and join algorithm (MuXjoin), which allows a separate optimization CPU time and I/O time, were presented. On the other hand, the K-CPQ has not been studied in-depth for high-dimensionality data.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…For SJ, if the sets P and Q coincide, then the DJQ is called similarity self-join (widely studied in [18,14,10,4]). Fig.…”

Section: Definition K Closest Pairs Query (K-cpq)mentioning

confidence: 99%

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VA-Files vs. R*-Trees in Distance Join Queries

Corral

D'Ermiliis

Manolopoulos

et al. 2005

Advances in Databases and Information Systems

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Abstract. In modern database applications the similarity of complex objects is examined by performing distance-based queries (e.g. nearest neighbour search) on data of high dimensionality. Most multidimensional indexing methods have failed to efficiently support these queries in arbitrary high-dimensional datasets (due to the dimensionality curse). Similarity join queries and K closest pairs queries are the most representative distance join queries, where two highdimensional datasets are combined. These queries are very expensive in terms of response time and I/O activity in case of high-dimensional spaces. On the other hand, the filtering-based approach, as applied by the VA-file, has turned out to be a very promising alternative for nearest neighbour search. In general, the filtering-based approach represents vectors as compact approximations, whereas by first scanning these approximations, only a small fraction of the real vectors is visited. Here, we elaborate on VA-files and develop VA-file based algorithms for answering similarity join and K closest pairs queries on highdimensional data. Also, performance-wise we compare the use of VA-files and R*-trees (a structure that has been proven to be of robust nature) for answering these queries. The results of the comparison do not lead to a clear winner.

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Section: Related Work and Motivationmentioning

confidence: 99%

Section: Related Work and Motivationmentioning

confidence: 99%

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VA-Files vs. R*-Trees in Distance Join Queries

Corral

D'Ermiliis

Manolopoulos

et al. 2005

Advances in Databases and Information Systems

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show abstract

“…When a pair of directory pages (P R , P S ) is under consideration, the algorithm forms all pairs of the child pages of P R and P S having distances Recently, index based similarity join methods have been analyzed from a theoretical point of view. [7] proposes a cost model based on the concept of the Minkowski sum [5] which can be used for optimizations such as page size optimization. The analysis reveals a serious optimization conflict between CPU and I/O time.…”

Section: Distance Range Based Similarity Joinmentioning

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.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Supporting KDD Applications by the k-Nearest Neighbor Join

Böhm¹,

Krebs²

2003

Lecture Notes in Computer Science

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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.

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Analysis of Nearest Neighbor Queries

Nearest Neighbor Search

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A cost model and index architecture for the similarity join

Abstract: The similarity join is an important database

Cited by 39 publications

References 36 publications

VA-Files vs. R*-Trees in Distance Join Queries

VA-Files vs. R*-Trees in Distance Join Queries

Supporting KDD Applications by the k-Nearest Neighbor Join

Analysis of Nearest Neighbor Queries

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