NDLI: To search or to crawl?: towards a query optimizer for text-centric tasks

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To search or to crawl?: towards a query optimizer for text-centric tasks

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To search or to crawl?: towards a query optimizer for text-centric tasks

Content Provider	ACM Digital Library
Author	Gravano, Luis Jain, Pranay Agichtein, Eugene Ipeirotis, Panagiotis G.
Abstract	Text is ubiquitous and, not surprisingly, many important applications rely on textual data for a variety of tasks. As a notable example, information extraction applications derive structured relations from unstructured text; as another example, focused crawlers explore the web to locate pages about specific topics. Execution plans for text-centric tasks follow two general paradigms for processing a text database: either we can scan, or 'crawl," the text database or, alternatively, we can exploit search engine indexes and retrieve the documents of interest via carefully crafted queries constructed in task-specific ways. The choice between crawl- and query-based execution plans can have a substantial impact on both execution time and output "completeness" (e.g., in terms of recall). Nevertheless, this choice is typically ad-hoc and based on heuristics or plain intuition. In this paper, we present fundamental building blocks to make the choice of execution plans for text-centric tasks in an informed, cost-based way. Towards this goal, we show how to analyze query- and crawl-based plans in terms of both execution time and output completeness. We adapt results from random-graph theory and statistics to develop a rigorous cost model for the execution plans. Our cost model reflects the fact that the performance of the plans depends on fundamental task-specific properties of the underlying text databases. We identify these properties and present efficient techniques for estimating the associated cost-model parameters. Overall, our approach helps predict the most appropriate execution plans for a task, resulting in significant efficiency and output completeness benefits. We complement our results with a large-scale experimental evaluation for three important text-centric tasks and over multiple real-life data sets.
Starting Page	265
Ending Page	276
Page Count	12
File Format	PDF
ISBN	1595934340
DOI	10.1145/1142473.1142504
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-06-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Text databases Metasearching Focused crawling Information extraction Research Query optimization
Content Type	Text
Resource Type	Article

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