Browsing by Subject "Metric Spaces"
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Item Open Access Data sensitive approximate query approaches in metric spaces(2011) Dilek, MerveSimilarity searching is the task of retrieval of relevant information from datasets. We are particularly interested in datasets that contain complex and unstructured data such as images, videos, audio recordings, protein and DNA sequences. The relevant information is typically defined using one of two common query types: a range query involves retrieval of all the objects within a specified distance to the query object; whereas a k-nearest neighbor query deals with obtaining k closest database objects to the query object. A variety of index structures based on the notion of metric spaces have been offered to process these two query types. The query performances of the proposed index structures have not been satisfactory particularly for high dimensional datasets. As a solution, various approximate similarity search methods offering the users a quality/time trade-off have been proposed. The rationale is that the users might be willing to tolerate query precision to retrieve query results relatively faster. The proposed approximate searching schemes usually have strong connections to the underlying data structures, making the comparison of the quality of the essence of their ideas difficult. In this thesis we investigate various approximation approaches to decrease the response time of similarity queries. These approaches use a variety of statistics about the dataset in order to obtain dynamic (at the time of querying) and specific guidance on the approximation for each query object individually. The experiments are performed on top of a simple underlying pivot-based index structure to minimize the effects of the index to our approximation schemes. The results show that it is possible to improve the performance/precision of the approximation based on data and query object sensitive guidance.Item Open Access Efficient k-nearest neighbor query processing in metric spaces based on precise radius estimation(2009) Şardan, CanSimilarity searching is an important problem for complex and unstructured data such as images, video, and text documents. One common solution is approximating complex objects into feature vectors. Metric spaces approach, on the other hand, relies solely on a distance function between objects. No information is assumed about the internal structure of the objects, therefore a more general framework is provided. Methods that use the metric spaces have also been shown to perform better especially on high dimensional data. A common query type used in similarity searching is the range query, where all the neighbors in a certain area defined by a query object and a radius are retrieved. Another important type, k-nearest neighbor queries return k closest objects to a given query center. They are more difficult to process since the distance of the kth nearest neighbor varies highly. For that reason, some techniques are proposed to estimate a radius that will return exactly k objects, reducing the computation into a range query. A major problem with these methods is that multiple passes over the index data is required if the estimation is low. In this thesis we propose a new framework for k-nearest neighbor search based on radius estimation where only one sequential pass over the index data is required. We accomplish this by caching a short-list of promising candidates. We also propose several algorithms to estimate the query radius which outperform previously proposed methods. We show that our estimations are accurate enough to keep the size of the promising objects at acceptable levels.