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• W2020271151 startingPage "1" @default.
• W2020271151 abstract "The task of similarity search is widely used in various areas of computing, including multimedia databases, data mining, bioinformatics, social networks, etc. In fact, retrieval of semantically unstructured data entities requires a form of aggregated qualification that selects entities relevant to a query. A popular type of such a mechanism is similarity querying. For a long time, the database-oriented applications of similarity search employed the definition of similarity restricted to metric distances. Due to its topological properties, metric similarity can be effectively used to index a database which can then be queried efficiently by so-called metric access methods. However, together with the increasing complexity of data entities across various domains, in recent years there appeared many similarities that were not metrics—we call them nonmetric similarity functions. In this article we survey domains employing nonmetric functions for effective similarity search, and methods for efficient nonmetric similarity search. First, we show that the ongoing research in many of these domains requires complex representations of data entities. Simultaneously, such complex representations allow us to model also complex and computationally expensive similarity functions (often represented by various matching algorithms). However, the more complex similarity function one develops, the more likely it will be a nonmetric. Second, we review state-of-the-art techniques for efficient (fast) nonmetric similarity search, concerning both exact and approximate search. Finally, we discuss some open problems and possible future research trends." @default.
• W2020271151 created "2016-06-24" @default.
• W2020271151 creator A5009309241 @default.
• W2020271151 creator A5013042933 @default.
• W2020271151 date "2011-10-01" @default.
• W2020271151 modified "2023-09-30" @default.
• W2020271151 title "On nonmetric similarity search problems in complex domains" @default.
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