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Quark: an efficient XQuery full-text implementation
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Source International Conference on Management of Data archive
Proceedings of the 2006 ACM SIGMOD international conference on Management of data table of contents
Chicago, IL, USA
DEMONSTRATION SESSION: Group C table of contents
Pages: 781 - 783  
Year of Publication: 2006
ISBN:1-59593-434-0
Authors
Anand Bhaskar  Cornell University, Ithaca, New York
Chavdar Botev  Cornell University, Ithaca, New York
Muthiah M. Muthaia Chettiar  Cornell University, Ithaca, New York
Lin Guo  Cornell University, Ithaca, New York
Jayavel Shanmugasundaram  Cornell University, Ithaca, New York
Feng Shao  Cornell University, Ithaca, New York
Fan Yang  Cornell University, Ithaca, New York
Sponsors
ACM: Association for Computing Machinery
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 4,   Downloads (12 Months): 74,   Citation Count: 2
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ABSTRACT

The XQuery 1.0 and XPath 2.0 Full-text (XQFT) language has been developed by the W3C to extend XQuery and XPath with full-text search capabilities. XQFT allows users to specify a mix of structured and complex full-text predicates, and also allows users to score/rank such queries. The power and flexibility of XQFT gives rise to two interesting questions. First, is it possible to efficiently integrate a full-function XML query language with sophisticated full-text search? Second, is it possible to score and rank arbitrary XQuery and XQFT queries? In this demonstration, we present evidence that it is indeed possible to achieve the above goals. We demonstrate the Quark open-source data management system and show how we can seamlessly and efficiently integrate structured and unstructured search over XML data. In particular, we demonstrate (a) techniques for efficiently evaluating keyword search over virtual XML views, and (b) a framework for scoring both structured and full-text predicates.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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C. Botev and J. Shanmugasundaram. Context-sensitive keyword search and ranking for xml. In WebDB'2005 Poster.
 
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Z. Chen, J. Gehrke, F. Korn, N. Koudas, J. Shanmugasundaram, and D. Srivastava. Index structures for matching xml twigs using relational query processors. In XSDM'2005.
 
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Collaborative Colleagues:
Anand Bhaskar: colleagues
Chavdar Botev: colleagues
Muthiah M. Muthaia Chettiar: colleagues
Lin Guo: colleagues
Jayavel Shanmugasundaram: colleagues
Feng Shao: colleagues
Fan Yang: colleagues