Anna Yarygina

Optimizing the Resource Allocation for Approximate Query Processing

Query optimization techniques are a proven tool essential for high performance of the database management systems. However, in a context of data spaces or new querying paradigms, such as similarity based search, exact query evaluation is neither computationally feasible nor meaningful and approximate query evaluation is the only reasonable option. In this paper a problem of resource allocation for approximate evaluation of complex queries is considered and an approximate algorithm for an optimal resource allocation is presented, providing the best feasible quality of the output result subject to a limited total cost of a query.

High-recall extraction of acronym-definition pairs with relevance feedback

This paper addresses the problem of extracting acronyms and their definitions from large documents in a setting, when high recall is required and user feedback is available. We propose a three step approach to deal with the problem. First, acronym candidates are extracted using a weak regular expression. This step results in a list of acronyms with high recall but low precision rates. Second, definitions are constructed for every acronym candidate from its surrounding text. And last, a classifier is used to select genuine acronym-definition pairs. At the last step we use relevance feedback mechanism to tune the classifier model for every particular document. This allows achieving reasonable precision without losing recall. As opposed to existing approaches, either created to be generic and domain independent or tuned to one particular domain, our method is adaptive to an input document. We evaluate the proposed approach using three datasets from different domains. The experiments prove the validity of the presented ideas.

A performance analysis of semantic caching for distributed semi-structured query processing

Caching is important for any system attempting to achieve high performance. The semantic caching is an approach trying to benefit from certain semantical knowledge of the data to be processed. The expectation is that semantical information might help to reduce the number of cache misses and in certain cases even avoid queries to the primary data. However, the major obstacle for wide application of semantic caching is the query containment problem which is computationally hard. In this paper we introduce an approximate conservative algorithm for semantic caching of semistructured queries and analyze its applicability for distributed query processing. Based on this analysis, we outline few scenarios where semantic caching can be benefitial for query processing in a distributed system of heterogeneous semi-structured information resources.

Bi-objective Optimization for Approximate Query Evaluation

A problem of effective and efficient approximate query evaluation is addressed as a special case of multi-objective optimization with 2 criteria: the computational resources and the quality of result. The proposed optimization and execution model provides for interactive trade of quality for speed.

A Performance Analysis of Semantic Caching for XML Query Processing

Caching is important for any system attempting to achieve high performance. The semantic caching is an approach trying to benefit from the certain knowledge of data semantics. The authors expect that this information might enable reuse of semantically close data rather than exactly equal to cached data in the traditional system. However, the major obstacle for extensive application of semantic caching for any data model or query language is the computational complexity of the query containment problem, which is, in general, undecidable. In this article the authors introduce and compare three approximate conservative query matching algorithms for semantic caching of semi-structured queries. The authors then analyze their applicability for distributed query processing. Based on this analysis, the authors outline few scenarios where semantic caching can be beneficial for query processing in a distributed system of heterogeneous semi-structured information resources.