With both types of cbr specifying precise queries

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Unformatted text preview: n the database for future reference. It may either replace the old version or exist as another version of the data. If users constantly create new versions of data, the database size will grow rapidly. To reduce the need to store new versions of data, a multimedia database management system stores the operations to generate the new data (called derived data) rather than storing the new data itself. Support for Multimedia Data Query Some desired information can be extracted from the data stored in a database by initiating queries. Queries contain predicates that must be satisfied by any data retrieved. It is easy to formulate predicates and look for its matching data in conventional databases. For example, "Find all employees for whom CITY = "PUNE" and AGE > 40". However, this is not an easy task while dealing with multimedia data. The three commonly used methods in existing multimedia database systems for querying multimedia data are: 1. Manual keyword indexing. This method is currently the most popular and straightforward method to query multimedia databases. In this method, descriptive keywords are manually associated with every multimedia data stored in the database. For example, keywords like “long face, sharp pointed nose, broad moustache, no beard, fair complexion, mole on right side of chin” may be used to describe an image data of a face. These keywords are metadata, since they are data about multimedia data. For querying, a user specifies some keywords and the database management system looks for images with matching keywords (the images themselves are not queried). All images with matching keywords are returned as the result of the query. This method has the following limitations: - Keyword classification is subjective since it is performed by a human. - Exceptions will always exist, and some data may be incorrectly classified. - Keywording is usually limited to a well-defined abstraction of the data (for example, for every image of a face, a specific set of features is classified). This means that if the abstraction becomes altered, then all...
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This document was uploaded on 04/07/2014.

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