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Clu-istos Foutsos, M. llanganatan, Yanais Maaolopoulo (1994)
Fast Subsequence Matching in Time-Series Databases
D. Shasha, J. Wang (1990)
New techniques for best-match retrievalACM Trans. Inf. Syst., 8
N. Roussopoulos, Stephen Kelley, F. Vincent (1995)
Nearest neighbor queries
R. Agrawal, C. Faloutsos, A. Swami (1993)
Efficient Similarity Search In Sequence Databases
P. Ciaccia, M. Patella, P. Zezula (1997)
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
H. Samet (1989)
The Design and Analysis of Spatial Data Structures
P. Ciaccia, M. Patella, P. Zezula (1998)
Processing Complex Similarity Queries with Distance-Based Access Methods
(1999)
Ozsoyoglu ACM Transactions on Database Systems
(1992)
Approximate Matching with High Dimensionality R-trees " . M.Sc Scholarly paper
Stefan Berchtold, D. Keim, H. Kriegel (2001)
The X-tree : An Index Structure for High-Dimensional Data
T. Chiueh (1994)
Content-Based Image Indexing
P. Yianilos (1993)
Data structures and algorithms for nearest neighbor search in general metric spaces
S. Brin (1995)
Near Neighbor Search in Large Metric Spaces
J. Uhlmann (1991)
Satisfying General Proximity/Similarity Queries with Metric TreesInf. Process. Lett., 40
A. Guttman (1984)
R-trees: a dynamic index structure for spatial searching
P. Ciaccia, M. Patella, P. Zezula (1998)
A cost model for similarity queries in metric spaces
N. Beckmann, H. Kriegel, R. Schneider, B. Seeger (1990)
The R*-tree: an efficient and robust access method for points and rectangles
T. Sellis, N. Roussopoulos, C. Faloutsos (1987)
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
BozkayaTolga, OzsoyogluMeral (1997)
Distance-based indexing for high-dimensional metric spacesSigmod Record
Sv) (3) (triangle inequality) d(Q,X) > r (4) (summation of (1),(2), and (3)) Because of (4), X cannot be in the query result, which means that we do not have to check any object in the right branch
Tolga Bozkaya, Meral Ozsoyoğlu (1997)
Distance-based indexing for high-dimensional metric spaces
Stefan Berchtold, C. Böhm, D. Keim, H. Kriegel (1997)
A cost model for nearest neighbor search in high-dimensional data space
Ricardo Baeza-Yates, W. Cunto, U. Manber, Sun Wu (1994)
Proximity Matching Using Fixed-Queries Trees
K. Wakimoto, M. Shima, S. Tanaka, A. Maeda, Hideyuki Tamura, Shunji Mori, J. Shibayama, M. Ireton (1994)
Efficient and Effective Querying by Image Content
W. Burkhard, R. Keller (1973)
Some approaches to best-match file searchingCommunications of the ACM, 16
(1998)
February July
P. Ciaccia, M. Patella (2001)
Bulk Loading the M-tree
One of the common queries in many database applications is finding approximate matches to a given query item from a collection of data items. For example, given an image database, one may want to retrieve all images that are similar to a given query image. Distance-based index structures are proposed for applications where the distance computations between objects of the data domain are expensive (such as high-dimensional data) and the distance function is metric. In this paper we consider using distance-based index structures for similarity queries on large metric spaces. We elaborate on the approach that uses reference points (vantage points) to partition the data space into spherical shell-like regions in a hierarchical manner. We introduce the multivantage point tree structure (mvp-tree) that uses more than one vantage point to partiton the space into spherical cuts at each level. In answering similarity-based queries, the mvp-tree also utilizes the precomputed (at construction time) distances between the data points and the vantage points. We summarize the experiments comparing mvp-trees to vp-trees that have a similar partitioning strategy, but use only one vantage point at each level and do not make use of the precomputed distances. Empirical studies show that the mvp-tree outperforms the vp-tree by 20% to 80% for varying query ranges and different distance distributions. Next, we generalize the idea of using multiple vantage points and discuss the results of experiments we have made to see how varying the number of vantage points in a node affects affects performance and how much is gained in performance by making use of precomputed distances. The results show that, after all, it may be best to use a large number of vantage points in an internal node in order to end up with a single directory node and keep as many of the precomputed distances as possible to provide more efficient filtering during search operations. Finally, we provide some experimental results that compare mvp-trees with M-trees, which is a dynamic distance-based index structure for metric domains.
ACM Transactions on Database Systems (TODS) – Association for Computing Machinery
Published: Sep 1, 1999
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