Adaptive Algorithms for Weighted Queries on Weighted Binary Relations and Labeled Trees
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Keyword queries are extremely easy for a user to write. They have become a standard way to query for information in web search engines and most other information retrieval systems whose users are usually laypersons and might not have knowledge about the database schema or contained data. As keyword queries do not impose any structural constraints on the retrieved information, the quality of the obtained results is far from perfect. However, one can hardly improve it without changing the ways the queries are asked and the methods the information is stored in the database. The purpose of this thesis is to propose a method to improve the quality of the information retrieving by adding weights to the existing ways of keyword queries asking and information storing in the database. We consider weighted queries on two different data structures: weighted binary relations and weighted multi-labeled trees. We propose adaptive algorithms to solve these queries and prove the measures of the complexity of these algorithms in terms of the high-level operations. We describe how these algorithms can be implemented and derive the upper bounds on their complexity in two specific models of computations: the comparison model and the word-RAM model.