Abstract
Similarity has been a central notion throughout our lives and due to the current unprecedented growth of digital data collections of various types in huge quantities, similarity management of digital data is becoming necessary. The Multi-Feature Indexing Network (MUFIN) is a generic engine for similarity search in various data collections, such as pictures, video, music, biometric data, sensor and scientific data, and many others. MUFIN can provide answers to queries based on the example paradigm. The system assumes a very universal concept of similarity that is based on the mathematical notion of metric space. In this concept, the data collection is seen as objects together with a method to measure similarity between pairs of objects. The key implementation strategies of MUFIN concern: extensibility - to be applied on variety of data types, scalability - to be efficient even for very large databases, and infrastructure independence - to run on various hardware infrastructures so that the required query response time and query execution throughput can be adjusted. The capability of MUFIN is demonstrated by several applications and advance prototypes. Other applications and future research and application trends are also to be discussed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Batko, M., Novak, D., Falchi, F., Zezula, P.: On scalability of the similarity search in the world of peers. In: INFOSCALE, pp. 1–12. ACM (2006)
Batko, M., Novak, D., Falchi, F., Zezula, P.: MESSIF: Metric Similarity Search Implementation Framework. In: Thanos, C., Borri, F., Candela, L. (eds.) Digital Libraries: Research and Development. LNCS, vol. 4877, pp. 1–10. Springer, Heidelberg (2007)
Ciaccia, P., Patella, M., Zezula, P.: M-tree: An efficient access method for similarity search in metric spaces. In: VLDB, pp. 426–435. Morgan Kaufmann (1997)
Dohnal, V., Gennaro, C., Savino, P., Zezula, P.: D-Index: Distance searching index for metric data sets. Multimedia Tools and Applications 21(1), 9–33 (2003)
Larkey, L., Markman, A.B.: Processes of similarity judgment. Cognitive Science 29, 1061–1076 (2005)
Novak, D., Batko, M., Zezula, P.: Metric index: An efficient and scalable solution for precise and approximate similarity search. Inf. Syst. 36(4), 721–733 (2011)
Novak, D., Zezula, P.: M-Chord: A scalable distributed similarity search structure. In: INFOSCALE, pp. 1–10. IEEE (2006)
O’Searcoid, M.: Metric Spaces. Springer, Heidelberg (2006)
Samet, H.: Foundations of Multidimensional And Metric Data Structures. Series in Data Management Systems. Morgan Kaufmann (2006)
Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Advances in Database Systems, vol. 32. Springer, Heidelberg (2006)
Zezula, P., Savino, P., Rabitti, F., Amato, G., Ciaccia, P.: Processing M-trees with parallel resources. In: RIDE, pp. 147–154. IEEE (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zezula, P. (2012). Multi Feature Indexing Network MUFIN for Similarity Search Applications. In: Bieliková, M., Friedrich, G., Gottlob, G., Katzenbeisser, S., Turán, G. (eds) SOFSEM 2012: Theory and Practice of Computer Science. SOFSEM 2012. Lecture Notes in Computer Science, vol 7147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27660-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-27660-6_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27659-0
Online ISBN: 978-3-642-27660-6
eBook Packages: Computer ScienceComputer Science (R0)