Conference Paper

# Relaxing Join and Selection Queries.

Conference: Proceedings of the 32nd International Conference on Very Large Data Bases, Seoul, Korea, September 12-15, 2006

Source: DBLP

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**ABSTRACT:**Web search engines and specialized online verticals are increasingly incorporating results from structured data sources to answer semantically rich user queries. For example, the query \WebQuery{Samsung 50 inch led tv} can be answered using information from a table of television data. However, the users are not domain experts and quite often enter values that do not match precisely the underlying data. Samsung makes 46- or 55- inch led tvs, but not 50-inch ones. So a literal execution of the above mentioned query will return zero results. For optimal user experience, a search engine would prefer to return at least a minimum number of results as close to the original query as possible. Furthermore, due to typical fast retrieval speeds in web-search, a search engine query execution is time-bound. In this paper, we address these challenges by proposing algorithms that rewrite the user query in a principled manner, surfacing at least the required number of results while satisfying the low-latency constraint. We formalize these requirements and introduce a general formulation of the problem. We show that under a natural formulation, the problem is NP-Hard to solve optimally, and present approximation algorithms that produce good rewrites. We empirically validate our algorithms on large-scale data obtained from a commercial search engine's shopping vertical.08/2011; -
##### Conference Paper: Worst-case optimal join algorithms: [extended abstract]

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**ABSTRACT:**Efficient join processing is one of the most fundamental and well-studied tasks in database research. In this work, we examine algorithms for natural join queries over many relations and describe a novel algorithm to process these queries optimally in terms of worst-case data complexity. Our result builds on recent work by Atserias, Grohe, and Marx, who gave bounds on the size of a full conjunctive query in terms of the sizes of the individual relations in the body of the query. These bounds, however, are not constructive: they rely on Shearer's entropy inequality which is information-theoretic. Thus, the previous results leave open the question of whether there exist algorithms whose running time achieve these optimal bounds. An answer to this question may be interesting to database practice, as we show in this paper that any project-join plan is polynomially slower than the optimal bound for some queries. We construct an algorithm whose running time is worst-case optimal for all natural join queries. Our result may be of independent interest, as our algorithm also yields a constructive proof of the general fractional cover bound by Atserias, Grohe, and Marx without using Shearer's inequality. In addition, we show that this bound is equivalent to a geometric inequality by Bollobás and Thomason, one of whose special cases is the famous Loomis-Whitney inequality. Hence, our results algorithmically prove these inequalities as well. Finally, we discuss how our algorithm can be used to compute a relaxed notion of joins.Proceedings of the 31st symposium on Principles of Database Systems; 05/2012 -
##### Article: An Efficient Trie-based Method for Approximate Entity Extraction with Edit-Distance Constraints

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**ABSTRACT:**Dictionary-based entity extraction has attracted much attention from the database community recently, which locates sub strings in a document into predefined entities (e.g., person names or locations). To improve extraction recall, a recent trend is to provide approximate matching between sub strings of the document and entities by tolerating minor errors. In this paper we study dictionary-based approximate entity extraction with edit-distance constraints. Existing methods have several limitations. First, they need to tune many parameters to achieve high performance. Second, they are inefficient for large edit-distance thresholds. We propose a trie-based method to address these problems. We first partition each entity into a set of segments, and then use a trie structure to index segments. To extract similar entities, we search segments from the document, and extend the matching segments in both entities and the document to find similar pairs. We develop an extension-based method to efficiently find similar string pairs by extending the matching segments. We optimize our partition scheme and select the best partition strategy to improve the extraction performance. Experimental results show that our method achieves much higher performance compared with state-of-the-art studies.01/2012;

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