XBridge-Mobile: efficient XML keyword search on mobile web data | Computing Skip to main content
Springer Nature Link
Log in

XBridge-Mobile: efficient XML keyword search on mobile web data

  • Published:
Computing Aims and scope Submit manuscript

Abstract

Mobile computing over intelligent mobile is affecting human’s habits of obtaining information over Internet, especially keyword search. Most of previous keyword search works are mainly focused on traditional web data sources, in which the performance can be improved by adding more computing power and/or building more offline-computed index. However, it is very challenging to apply the traditional keyword search methods to mobile web-based keyword search because mobile computing has many different features, e.g., frequent disconnections, variety of bandwidths, limited power of mobile devices, limited data size to be downloaded, etc.. To address this challenge, in this paper we design an adaptive mobile-based XML keyword search approach, called XBridge-Mobile, that can derive the semantics of a keyword query and generate a set of effective structured patterns by analyzing the given keyword query and the schemas of XML data sources. Each structured pattern represents one of user’s possible search intentions. The patterns will be firstly sent to the mobile client from web server. And then, the mobile client can select some interested patterns to load the results. By doing this, we can reduce the communication cost a lot between web server and mobile client because only the derived patterns and a few results need to be transferred, not all the keyword search results, by which we can save lots of expenses when the downloaded data is priced. In addition, we can economically maintain the frequent structured pattern queries in the mobile device, which can further reduce the expense of downloading data. At last, we analyze and propose a ranking function to measure the quality of keyword search results, design a set of algorithms to optimize mobile keyword search based on the maintained structured patterns, and present the experimental study of XBridge-Mobile with real XML datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Clark J, DeRose S (1999) XML Path Language (XPath) 1.0, November 1999, Steve DeRose. XML Path Language (XPath) 1.0 http://www.w3.org/TR/xpath

  2. (2001) XQuery 1.0: An XML Query Language. http://www.w3.org/XML/Query

  3. Amer-Yahia S, Cho S, Srivastava D (2002) Tree pattern relaxation. In EDBT, Mankato, pp 496–513

  4. Li J, Liu C, Xu Yu J, Zhou R (2008) Efficient top-k search across heterogeneous xml data sources. In DASFAA, Hong Kong, pp 314–329

  5. Chengfei L, Jianxin L, XuYu J, Rui Z (2010) Adaptive relaxation for querying heterogeneous xml data sources. Inf. Syst. 35(6):688–707

    Article  Google Scholar 

  6. Vagelis H, Nick K, Yannis P, Divesh S (2006) Keyword proximity search in XML trees. IEEE Trans. Knowl. Data Eng. 18(4):525–539

    Article  Google Scholar 

  7. Schmidt A, Kersten ML, Windhouwer M (2001) Querying XML Documents Made Easy: Nearest Concept Queries. In ICDE,California, pp 321–329

  8. Theobald A, Weikum G (2002) The Index-Based XXL Search Engine for Querying XML Data with Relevance Ranking. In EDBT, Prague, pp 477–495

  9. Cohen S, Mamou J, Kanza Y, Sagiv Y (2003) XSEarch: A Semantic Search Engine for XML. In VLDB, Vienna, pp 45–56

  10. Li Y, Yu C, Jagadish HV (2004) Schema-Free XQuery. In VLDB, Istanbul, pp 72–83

  11. Hristidis V, Papakonstantinou Y, Balmin A (2003) Keyword Proximity Search on XML Graphs. In ICDE, France,pp 367–378

  12. Guo L, Shao F, Botev C, Shanmugasundaram J (2003) XRANK: Ranked Keyword Search over XML Documents. In SIGMOD Conference, Shenzhen, pp 16–27

  13. Xu Y, Papakonstantinou Y (2005) Efficient Keyword Search for Smallest LCAs in XML Databases. In SIGMOD Conference, Shenzhen, pp 537–538

  14. Liu Z, Chen Y (2007) Identifying meaningful return information for XML keyword search. In SIGMOD Conference, Shenzhen, pp 329–340

  15. Sun C, Yong Chan C, Goenka AK (2007) Multiway slca-based keyword search in xml data. In WWW, Singapore, pp 1043–1052

  16. Li G, Feng J, Wang J, Zhou L (2007) Effective keyword search for valuable lcas over xml documents. In CIKM, Lisboa, pp 31–40

  17. Yang W, Shi B (2007) Schema-aware keyword search over xml streams. In CIT, Shanghai, pp 29–34

  18. Li J, Liu C, Zhou R, Ning B (2009) Processing xml keyword search by constructing effective structured queries. In APWeb/WAIM, Suzhou, pp 88–99

  19. Böttcher S, Türling A (2004) Caching xml data on mobile web clients. In: International Conference on Internet Computing, Las Vegas, pp 150–156

  20. Li J, Liu C, Zhou R, Wang W (2010) Suggestion of promising result types for xml keyword search. In EDBT, Swinburne, pp 561–572

  21. Zhou R, Liu C, Li J (2010) Fast elca computation for keyword queries on xml data. In EDBT, Swinburne, pp 549–560

  22. Li J, Liu C, Zhou R, Wang W (2011) Top-k keyword search over probabilistic xml data. In ICDE, Swinburne, pp 673–684

  23. Lu Y, Wang W, Li J, Liu C (2011) Xclean: Providing valid spelling suggestions for xml keyword queries. In ICDE, Swinburne, pp 661–672

  24. Ziyang L, Yi C (2008) Reasoning and identifying relevant matches for xml keyword search. PVLDB 1(1):921–932

    Google Scholar 

  25. Daniela F, Donald K, Ioana M (2000) Integrating keyword search into XML query processing. Comput. Netw. 33(1–6):119–135

    Google Scholar 

Download references

Acknowledgments

This work is supported by the Australian Research Council Discovery Projects under the Grant No. DP110102407 and the Grant No. DP120102627, and Natural Science Foundation of China No.91124001. We would like to thank anonymous reviewers for their helpful comments.

Author information

Authors and Affiliations

  1. Swinburne University of Technology, Melbourne, Australia

    Jianxin Li & Chengfei Liu

  2. Institute of Software, Chinese Adedamy of Sciences, Beijing, China

    Jiajie Xu

Authors
  1. Jianxin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chengfei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiajie Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianxin Li.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, J., Liu, C. & Xu, J. XBridge-Mobile: efficient XML keyword search on mobile web data. Computing 96, 631–650 (2014). https://doi.org/10.1007/s00607-013-0315-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-013-0315-3

Keywords

Mathematics Subject Classification

Search

Navigation