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Keyword search on structured and semi-structured data
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International Conference on Management of Data archive
Proceedings of the 35th SIGMOD international conference on Management of data table of contents
Providence, Rhode Island, USA
TUTORIAL SESSION: Tutorials table of contents
Pages 1005-1010  
Year of Publication: 2009
ISBN:978-1-60558-551-2
Authors
Yi Chen  Arizona State University, Tempe, AZ, USA
Wei Wang  University of New South Wales and NICTA, Sydney, Australia
Ziyang Liu  Arizona State University, Tempe, AZ, USA
Xuemin Lin  University of New South Wales and NICTA, Sydney, Australia
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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ABSTRACT

Empowering users to access databases using simple keywords can relieve the users from the steep learning curve of mastering a structured query language and understanding complex and possibly fast evolving data schemas. In this tutorial, we give an overview of the state-of-the-art techniques for supporting keyword search on structured and semi-structured data, including query result definition, ranking functions, result generation and top-k query processing, snippet generation, result clustering, query cleaning, performance optimization, and search quality evaluation. Various data models will be discussed, including relational data, XML data, graph-structured data, data streams, and workflows. We also discuss applications that are built upon keyword search, such as keyword based database selection, query generation, and analytical processing. Finally we identify the challenges and opportunities of future research to advance the field.


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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S. Agrawal, S. Chaudhuri, and G. Das. DBXplorer: A system for keyword-based search over relational databases. In ICDE, 2002.
 
2
 
3
 
4
G. Bhalotia, C. Nakhe, A. Hulgeri, S. Chakrabarti, and S. Sudarshan. Keyword Searching and Browsing in Databases using BANKS. In ICDE, 2002.
 
5
 
6
 
7
 
8
B. Ding, J.X. Yu, S. Wang, L. Qin, X. Zhang, and X. Lin. Finding top-k min-cost connected trees in databases. In ICDE, 2007.
 
9
10
11
12
13
 
14
 
15
 
16
V. Hristidis, Y. Papakonstantinou, and A. Balmin. Keyword proximity search on xml graphs. In ICDE, 2003.
 
17
18
 
19
INEX. Initiative for the evaluation of xml retrieval. http://inex.is.informatik.uni-duisburg.de/.
20
 
21
 
22
KEYS 2009. The first international workshop on keyword search on structured data, 2009.
23
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26
 
27
28
 
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31
 
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33
34
 
35
 
36
 
37
38
 
39
 
40
 
41
 
42
M. Sayyadian, H. LeKhac, A. Doan, and L. Gravano. Efficient keyword search across heterogeneous relational databases. In ICDE, 2007.
 
43
 
44
 
45
46
 
47
Databases and IR: Perspectives of a SQL guy. NSF Information and Data Management PI Workshop, 2003.
 
48
49
50
 
51
52
 
53
54
55
56
57
58
 
59
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Collaborative Colleagues:
Yi Chen: colleagues
Wei Wang: colleagues
Ziyang Liu: colleagues
Xuemin Lin: colleagues