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A Multi-Similarity Algebra.

Sibel Adali, Piero A. Bonatti, Maria Luisa Sapino, V. S. Subrahmanian: A Multi-Similarity Algebra. SIGMOD Conference 1998: 402-413
@inproceedings{DBLP:conf/sigmod/AdaliBSS98,
  author    = {Sibel Adali and
               Piero A. Bonatti and
               Maria Luisa Sapino and
               V. S. Subrahmanian},
  editor    = {Laura M. Haas and
               Ashutosh Tiwary},
  title     = {A Multi-Similarity Algebra},
  booktitle = {SIGMOD 1998, Proceedings ACM SIGMOD International Conference
               on Management of Data, June 2-4, 1998, Seattle, Washington, USA},
  publisher = {ACM Press},
  year      = {1998},
  isbn      = {0-89791-995-5},
  pages     = {402-413},
  ee        = {http://doi.acm.org/10.1145/276304.276340, db/conf/sigmod/AdaliBSS98.html},
  crossref  = {DBLP:conf/sigmod/98},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

The need to automatically extract and classify the contents of multimedia data archives such as images, video, and text documents has led to significant work on similarity based retrieval of data. To date, most work in this area has focused on the creation of index structures for similarity based retrieval. There is very little work on developing formalisms for querying multimedia databases that support similarity based computations and optimizing such queries, even though it is well known that feature extraction and identification algorithms in media data are very expensive. We introduce a similarity algebra that brings together relational operators and results of multiple similarity implementations in a uniform language. The algebra can be used to specify complex queries that combine different interpretations of similarity values and multiple algorithms for computing these values. We prove equivalence and containment relationships between similarity algebra expressions and develop query rewriting methods based on these results. We then provide a generic cost model for evaluating cost of query plans in the similarity algebra and query optimization methods based on this
model. We supplement the paper with experimental results that illustrate the use of the algebra and the effectiveness of query optimization methods using the Integrated Search Engine (I.SEE) as the testbed.

Copyright © 1998 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.


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Laura M. Haas, Ashutosh Tiwary (Eds.): SIGMOD 1998, Proceedings ACM SIGMOD International Conference on Management of Data, June 2-4, 1998, Seattle, Washington, USA. ACM Press 1998, ISBN 0-89791-995-5 BibTeX , SIGMOD Record 27(2), June 1998
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BibTeX
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