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A Cost Model for Similarity Queries in Metric Spaces

by Paolo Ciaccia, Marco Patella, Pavel Zezula , 1998
"... Wu consider tho problem of estimating CPU (distance com-putntlons) nnd I/O costs for processing range and k-nearest neighbors qucrics over metric spaces. Unlike the specific case of vector spaces, where information on data distribution has been exploited to derive cost models for predicting the per- ..."
Abstract - Cited by 61 (12 self) - Add to MetaCart
model for the M-tree access method [lo]. Our results rely on the assumption that the indexed dataset comes from a metric space which is “homogeneous ” enough (in a probabilistic sense) to allow reliable cost estimations even if the distance distribution with respect to a specific query object is unknown

M-tree: An Efficient Access Method for Similarity Search in Metric Spaces

by Paolo Ciaccia, Marco Patella, Pavel Zezula , 1997
"... A new access meth d, called M-tree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion o ..."
Abstract - Cited by 663 (38 self) - Add to MetaCart
A new access meth d, called M-tree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion

Features of similarity.

by Amos Tversky - Psychological Review , 1977
"... Similarity plays a fundamental role in theories of knowledge and behavior. It serves as an organizing principle by which individuals classify objects, form concepts, and make generalizations. Indeed, the concept of similarity is ubiquitous in psychological theory. It underlies the accounts of stimu ..."
Abstract - Cited by 1455 (2 self) - Add to MetaCart
tree-like structures rather than dimensionally organized spaces. However, most theoretical and empirical analyses of similarity assume that objects can be adequately represented as points in some coordinate space and that dissimilarity behaves like a metric distance function. Both dimensional

Searching in metric spaces

by Edgar Chávez, Gonzalo Navarro, Ricardo Baeza-yates, José Luis Marroquín , 2001
"... The problem of searching the elements of a set that are close to a given query element under some similarity criterion has a vast number of applications in many branches of computer science, from pattern recognition to textual and multimedia information retrieval. We are interested in the rather gen ..."
Abstract - Cited by 436 (38 self) - Add to MetaCart
general case where the similarity criterion defines a metric space, instead of the more restricted case of a vector space. Many solutions have been proposed in different areas, in many cases without cross-knowledge. Because of this, the same ideas have been reconceived several times, and very different

NiagaraCQ: A Scalable Continuous Query System for Internet Databases

by Jianjun Chen, David J. Dewitt, Feng Tian, Yuan Wang - In SIGMOD , 2000
"... Continuous queries are persistent queries that allow users to receive new results when they become available. While continuous query systems can transform a passive web into an active environment, they need to be able to support millions of queries due to the scale of the Internet. No existing syste ..."
Abstract - Cited by 584 (9 self) - Add to MetaCart
systems have achieved this level of scalability. NiagaraCQ addresses this problem by grouping continuous queries based on the observation that many web queries share similar structures. Grouped queries can share the common computation, tend to fit in memory and can reduce the I/O cost significantly

Blobworld: Image segmentation using Expectation-Maximization and its application to image querying

by Chad Carson, Serge Belongie, Hayit Greenspan, Jitendra Malik - IEEE Transactions on Pattern Analysis and Machine Intelligence , 1999
"... Retrieving images from large and varied collections using image content as a key is a challenging and important problem. We present a new image representation which provides a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture. This "B ..."
Abstract - Cited by 438 (10 self) - Add to MetaCart
;Blobworld" representation is created by clustering pixels in a joint color-texture-position feature space. The segmentation algorithm is fully automatic and has been run on a collection of 10,000 natural images. We describe a system that uses the Blobworld representation to retrieve images from this collection

Efficient estimation of word representations in vector space

by Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean , 2013
"... We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previ-ously best performing techniques based on different types ..."
Abstract - Cited by 371 (6 self) - Add to MetaCart
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previ-ously best performing techniques based on different

Index-driven similarity search in metric spaces

by Gisli R. Hjaltason, Hanan Samet - ACM Transactions on Database Systems , 2003
"... Similarity search is a very important operation in multimedia databases and other database applications involving complex objects, and involves finding objects in a data set S similar to a query object q, based on some similarity measure. In this article, we focus on methods for similarity search th ..."
Abstract - Cited by 192 (8 self) - Add to MetaCart
Similarity search is a very important operation in multimedia databases and other database applications involving complex objects, and involves finding objects in a data set S similar to a query object q, based on some similarity measure. In this article, we focus on methods for similarity search

Discovering similar multidimensional trajectories

by Michail Vlachos - In ICDE , 2002
"... We investigate techniques for analysis and retrieval of object trajectories in a two or three dimensional space. Such kind of data usually contain a great amount of noise, that makes all previously used metrics fail. Therefore, here we formalize non-metric similarity functions based on the Longest C ..."
Abstract - Cited by 260 (6 self) - Add to MetaCart
We investigate techniques for analysis and retrieval of object trajectories in a two or three dimensional space. Such kind of data usually contain a great amount of noise, that makes all previously used metrics fail. Therefore, here we formalize non-metric similarity functions based on the Longest

Integration of Heterogeneous Databases Without Common Domains Using Queries Based on Textual Similarity

by William W. Cohen , 1998
"... Most databases contain "name constants" like course numbers, personal names, and place names that correspond to entities in the real world. Previous work in integration of heterogeneous databases has assumed that local name constants can be mapped into an appropriate global domain by norma ..."
Abstract - Cited by 247 (13 self) - Add to MetaCart
, and assume instead that the names are given in natural language text. We then propose a logic called WHIRL which reasons explicitly about the similarity of local names, as measured using the vector-space model commonly adopted in statistical information retrieval. We describe an efficient implementation
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