XRANK: ranked keyword search over XML documents
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Efficient keyword search for smallest LCAs in XML databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Pathfinder: XQuery---the relational way
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Multiway SLCA-based keyword search in XML data
Proceedings of the 16th international conference on World Wide Web
Identifying meaningful return information for XML keyword search
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Effective keyword search for valuable lcas over xml documents
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Enabling Schema-Free XQuery with meaningful query focus
The VLDB Journal — The International Journal on Very Large Data Bases
Hash-Search: An Efficient SLCA-Based Keyword Search Algorithm on XML Documents
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Effective XML Keyword Search with Relevance Oriented Ranking
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
XKMis: effective and efficient keyword search in XML databases
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Effective keyword search in XML documents based on MIU
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
| Hi-index | 0.00 |
In this paper, we focus on the problem of effectively and efficiently answering XML keyword search. We first show the weakness of existing SLCA (Smallest Lowest Common Ancestor) based solutions, and then we propose the concept of Candidate Fragment. A Candidate Fragment is a meaningful sub tree in the XML document tree, which has the appropriate granularity. To efficiently compute Candidate Fragments as the answers of XML keyword search, we design Node Match Algorithm and Path Match algorithm. Finally, we conduct extensive experiments to show that our approach is both effective and efficient.