Publication details
Similarity Search: The Metric Space Approach
| Basic information | |
|---|---|
| Original title: | Similarity Search: The Metric Space Approach |
| Authors: | Pavel Zezula, Giuseppe Amato, Vlastislav Dohnal |
| Further information | |
|---|---|
| Citation: | ZEZULA, Pavel  - AMATO, Giuseppe - DOHNAL, Vlastislav. Similarity Search: The Metric Space Approach. Vyd. 2007. Seoul, Korea : ACM, 2007. ACM SAC 2007 Conference. ACM SAC 2007 Conference Tutorial. ISBN 1-59593-480-4. |
| Original language: | English |
| Field: | Informatika |
| WWW: |  ACM SAC 2007 - Home Page |
| Type: | R&D Presentation |
| Keywords: | similarity search; approximate search; metric space; index structures; distributed index structure; scalability |
Similarity searching has become afundamental computational task in a variety of application areas, including multimedia information retrieval, data mining, pattern recognition, machine learning, computer vision, biomedical databases, data compression and statistical data analysis. In such environments, an exact match has little meaning, and proximity/distance (similarity/dissimilarity) concepts are typically much more fruitful for searching. In this tutorial, we review the state of the art in developing similarity search mechanisms that accept the metric space paradigm. We explain the high extensibility of the metric space approach and demonstrate its capability with examples of distance functions. The efforts to further speed up retrieval are demonstrated by a class of approximated techniques and the very recent proposals of scalable and distributed structures based on the P2P communication paradigm.
Related projects:
- Intelligent Models, Algorithms, Methods and Tools for the Semantic Web (realization)
- Distributed Index Structures for Similarity Searching
