Skip to main content
SpringerLink
Log in
Book cover

Highlighting the Importance of Big Data Management and Analysis for Various Applications pp 71–78Cite as

Extracting Relevant Information from Big Data to Anticipate Forced Migration

  • Chapter
  • First Online:

  • 1546 Accesses

Part of the book series: Studies in Big Data ((SBD,volume 27))

Abstract

In this paper, we investigate how to extract the relevant information for forced migration from big data. Data-driven approaches have the potential to provide humanitarian agencies with real-time decision-making tools. However, very few humanitarian organisations have been able to gather, analyze and employ the data to its full potential due to capacity limitations.

This is a preview of subscription content, 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   79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   99.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Notes

  1. 1.

    http://www.unhcr.org/.

  2. 2.

    http://care.ca/.

References

  1. Wei Y, Taylor A, Yossinger NS, Swingewood E, Cronbaugh C, Quinn DR, Singh L, Martin SF, Berkowitz S, McGrath S. Using large-scale open source data to identify potential forced migration. In: Knowledge Discovery and Data Mining (KDD) 2014 Workshop on Data Science for Social Good; 2014. 4 pp.

    Google Scholar 

  2. Allan J, Ballesteros L, Callan JP, Croft WB, Lu Z. Recent experiments with inquery. In: Proceedings of the 4th Text Retrieval Conference; 1995. p. 49–64.

    Google Scholar 

  3. Wei X, Croft WB. Modeling term associations for ad-hoc retrieval performance within language modeling framework. Berlin: Springer; 2007.

    Book  Google Scholar 

  4. Hoffman M, Bach FR, Blei DM. Online learning for latent Dirichlet allocation. In: Advances in Neural Information Processing Systems; 2010. p. 856–64.

    Google Scholar 

  5. Zhao J, Huang JX, Ye Z. Modeling term associations for probabilistic information retrieval. ACM Trans Inf Syst. 2014;32(2):7.

    Article  Google Scholar 

  6. Hristidis V, Wu Y, Raschid L. Efficient ranking on entity graphs with personalized relationships. IEEE Trans Knowl Data Eng. 2014;26(4):850–63.

    Article  Google Scholar 

  7. Robertson SE, Walker S, Jones S, Hancock-Beaulieu MM, Gatford M, et al. Okapi at TREC-3. In NIST Special Publication SP, vol. 109; 1995. p. 109.

    Google Scholar 

  8. Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res. 2003;3:993–1022.

    Google Scholar 

  9. Miao J, Huang JX, Zhao J. TopPRF: a probabilistic framework for integrating topic space into pseudo relevance feedback. ACM Trans Inf Syst. 2016;34(4):22.

    Article  Google Scholar 

  10. Wang X, Grimson E. Spatial latent Dirichlet allocation. In: Adv Neural Inf Proces Syst. 2008;1577–84.

    Google Scholar 

Download references

Acknowledgements

This research is supported by the Social Sciences and Humanities Research Council of Canada (SSHRC) Partnership Development Grants (PDG) and the Natural Sciences and Engineering Research Council of Canada (NSERC) Collaborative Research and Training Experience Program (CREATE). We thank the anonymous reviewers for their through comments.

Author information

Authors and Affiliations

  1. Centre for Refugee Studies, York University, Toronto, ON, Canada

    Jiashu Zhao & Susan McGrath

  2. School of Information Technology, York University, Toronto, ON, Canada

    Jiashu Zhao & Jimmy Xiangji Huang

  3. Department of Mathematics and Statistics, York University, Toronto, ON, Canada

    Jianhong Wu & Shicheng Wu

Authors
  1. Jiashu Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Susan McGrath
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jimmy Xiangji Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianhong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shicheng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jiashu Zhao or Jimmy Xiangji Huang .

Editor information

Editors and Affiliations

  1. Department of Electrical & Computer Engineering, University of Calgary, Calgary, Alberta, Canada

    Mohammad Moshirpour

  2. Department of Electrical & Computer Engineering, University of Calgary, Calgary, Alberta, Canada

    Behrouz Far

  3. Department of Electrical & Computer Engineering, University of Calgary, Calgary, Alberta, Canada

    Reda Alhajj

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Zhao, J., McGrath, S., Huang, J.X., Wu, J., Wu, S. (2018). Extracting Relevant Information from Big Data to Anticipate Forced Migration. In: Moshirpour, M., Far, B., Alhajj, R. (eds) Highlighting the Importance of Big Data Management and Analysis for Various Applications. Studies in Big Data, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-60255-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60255-4_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60254-7

  • Online ISBN: 978-3-319-60255-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation