Skip to main content
SpringerLink
Log in

Associated Index for Big Structured and Unstructured Data

  • Conference paper
  • First Online:
Web-Age Information Management (WAIM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9098))

Included in the following conference series:

  • 2703 Accesses

Abstract

In big data epoch, one of the major challenges is the large volume of mixed structured and unstructured data. Because of different form, structured and unstructured data are often considered apart from each other. However, they may speak about the same entities of the world. If a query involves both structured data and its unstructured counterpart, it is inefficient to execute it separately. The paper presents a novel index structure tailored towards associations between structured and unstructured data, based on entity co-occurrences. It is also a semantic index represented as RDF graphs which describes the semantic relationships among entities. Experiments show that the associated index can not only provide apposite information but also execute queries efficiently.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Mudunuri, U.S., Khouja, M., Repetski, S.: Knowledge and Theme Discovery across Very Large Biological Data Sets Using Distributed Queries: A Prototype Combining Unstructured and Structured Data. PLoS One 8(12), e80503 (2013)

    Article  Google Scholar 

  2. Wu, S., Jiang, D.W., Ooi, B.C., Wu, K.: Efficient B-tree based indexing for cloud data processing. Proc. of the VLDB Endowment 3(1), 1207–1218 (2010)

    Article  Google Scholar 

  3. George, T., Iraklis, V., Kjetil, N.: SemaFor: semantic document indexing using semantic forests. In: CIKM, pp. 1692–1696 (2012)

    Google Scholar 

  4. Markus, G., Andreas, R., Helmut, B.: Bridging structured and unstructured data via hybrid semantic search and interactive ontology-enhanced query formulation. Knowl. Inf. Syst. 41(3), 761–792 (2014)

    Article  Google Scholar 

  5. PubMed. http://www.ncbi.nlm.nih.gov/pubmed

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, China

    Chunying Zhu, Qingzhong Li, Lanju Kong & Xiaoguang Hong

  2. State Grid SHANDONG Electric Power Company, Jinan, China

    Xiangwei Wang

Authors
  1. Chunying Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qingzhong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lanju Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiangwei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoguang Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingzhong Li .

Editor information

Editors and Affiliations

  1. Google, CA, USA

    Xin Luna Dong

  2. Postdoc Apartments (Hong Lou) 4-1-4, Shandong University, Li Cheng, Jinan, China

    Xiaohui Yu

  3. Tsinghua University, Beijing, China

    Jian Li

  4. Northeastern University, BOSTON, USA

    Yizhou Sun

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhu, C., Li, Q., Kong, L., Wang, X., Hong, X. (2015). Associated Index for Big Structured and Unstructured Data. In: Dong, X., Yu, X., Li, J., Sun, Y. (eds) Web-Age Information Management. WAIM 2015. Lecture Notes in Computer Science(), vol 9098. Springer, Cham. https://doi.org/10.1007/978-3-319-21042-1_64

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-21042-1_64

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21041-4

  • Online ISBN: 978-3-319-21042-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation