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XRANK: ranked keyword search over XML documents
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Keyword Searching and Browsing in Databases using BANKS
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Texquery: a full-text search extension to xquery
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Efficient keyword search for smallest LCAs in XML databases
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Bidirectional expansion for keyword search on graph databases
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Multiway SLCA-based keyword search in XML data
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Identifying meaningful return information for XML keyword search
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Discover: keyword search in relational databases
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XSEarch: a semantic search engine for XML
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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Efficient Data Structure for XML Keyword Search
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Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
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Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
SAIL: Structure-aware indexing for effective and progressive top-k keyword search over XML documents
Information Sciences: an International Journal
Efficient keyword proximity search using a frontier-reduce strategy based on d-distance graph index
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Return specification inference and result clustering for keyword search on XML
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Fast ELCA computation for keyword queries on XML data
Proceedings of the 13th International Conference on Extending Database Technology
Suggestion of promising result types for XML keyword search
Proceedings of the 13th International Conference on Extending Database Technology
LCA-based selection for XML document collections
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The VLDB Journal — The International Journal on Very Large Data Bases
Exploit keyword query semantics and structure of data for effective XML keyword search
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
An effective 3-in-1 keyword search method over heterogeneous data sources
Information Systems
Using structural information in XML keyword search effectively
ACM Transactions on Database Systems (TODS)
Providing built-in keyword search capabilities in RDBMS
The VLDB Journal — The International Journal on Very Large Data Bases
Relevant answers for XML keyword search: a skyline approach
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
A survey on XML keyword search
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Adaptive and effective keyword search for XML
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K-graphs: selecting top-k data sources for XML keyword queries
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Processing keyword search on XML: a survey
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Top-K data source selection for keyword queries over multiple XML data sources
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Efficient keyword search on large tree structured datasets
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Fast result enumeration for keyword queries on XML data
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Efficiently identifying contributors for XML keyword search
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
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WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
ELCA evaluation for keyword search on probabilistic XML data
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Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Effectively return query results for keyword search on XML data
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The VLDB Journal — The International Journal on Very Large Data Bases
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Keyword search in XML documents based on the notion of lowest common ancestors (LCAs) and modifications of it has recently gained research interest [10, 14, 22]. In this paper we propose an efficient algorithm called Indexed Stack to find answers to keyword queries based on XRank's semantics to LCA [10]. The complexity of the Indexed Stack algorithm is O(kd|S1| log |S|) where k is the number of keywords in the query, d is the depth of the tree and |S1| (|S|) is the occurrence of the least (most) frequent keyword in the query. In comparison, the best worst case complexity of the core algorithms in [10] is O(kd|S|). We analytically and experimentally evaluate the Indexed Stack algorithm and the two core algorithms in [10]. The results show that the Indexed Stack algorithm outperforms in terms of both CPU and I/O costs other algorithms by orders of magnitude when the query contains at least one low frequency keyword along with high frequency keywords. This is important in practice since the frequencies of keywords typically vary significantly.