Skip to main content
SpringerLink
Log in

Efficient Massive Medical Rules Parallel Processing Algorithms

  • Conference paper
  • First Online:
Smart Health (ICSH 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10983))

Included in the following conference series:

  • 1141 Accesses

Abstract

Massive rules processing will play a very important role in the ad hoc computing. In this paper, we first give the massive KOA medical rules processing framework. We propose two kinds of massive rules processing algorithms: Massive Rules processing algorithm without and with external communication. In the Knee Osteoarthritis medical area, lots of rules can be set, our algorithms can be used to process rules set by all kinds of Knee Osteoarthritis rules systems.

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 EPUB and 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

References

  1. Forgy, C.L.: Rete a fast algorithm for the many pattern/many object pattern match problem. Artif. Intell. 19, 17–37 (1982)

    Article  Google Scholar 

  2. Mirankerm, D.P.: TREAT: a better match algorithm for AI production systems. In: Proceedings of AAAI 87 Conference on Artificial Intelligence, pp. 42–47, August 1987

    Google Scholar 

  3. Cheng, A.M.K., Fujii, S.: Self-stabilizing real-time OPS5 production systems. IEEE Trans. Knowl. Data Eng. 16(12), 1543–1554 (2004)

    Article  Google Scholar 

  4. Rete II (2018). http://www.pst.com/rete2.htm

  5. Cadonna, B., Gamper, J., Böhlen, M.: Sequenced event set pattern matching. In: Proceeding of EDBT2011. 22–24 March, Uppsala, Sweden, pp. 33–44 (2011)

    Google Scholar 

  6. Wang, P., Wang, H., Liu, M., Wang, W.: An algorithmic approach to event summarization. In: Proceeding of SIGMOD, pp. 183–194 (2010)

    Google Scholar 

  7. Marcus, A., Bernstein, M.S., Badar, O., Karger, D.R., Madden, S., Miller, R.C.: Tweets as data: demonstration of TweeQL and Twitinfo. In: Proceeding of SIGMOD, pp. 1259–1262 (2011)

    Google Scholar 

  8. Sheykh Esmaili, K., Sanamrad, T., Fischer, P.M., Tatbul, N.: Changing flights in mid-air: a model for safely modifying continuous queries. In: Proceeding of SIGMOD, pp. 613–624 (2011)

    Google Scholar 

  9. Gordin, D.N., Pasik, A.J.: Set-oriented constructs: from rete rule bases to database systems. In: Proceedings of the 1991 ACM SIGMOD International Conference on Management of Data, p. 60 (1991)

    Google Scholar 

  10. Gaudiot, J.-L., Sohn, A.: Data-driven parallel production systems. IEEE Trans. Softw. Eng. 16(3), 281–293 (1990)

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported by NSFC (91646202), Research/Project 2017YB142 supported by Ministry of Education of The People’s Republic of China, the 1000-Talent program.

Author information

Authors and Affiliations

  1. Department of Rehabilitation, Beijing Tsinghua Changgung Hospital, Beijing, 100084, China

    Xin Li

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Guigang Zhang

  3. Research Institute of Information Technology, Beijing, China

    Chunxiao Xing

  4. Beijing National Research Center for Information Science and Technology, Beijing, China

    Chunxiao Xing

  5. Department of Computer Science and Technology, Beijing, China

    Chunxiao Xing

  6. Institute of Internet Industry, Tsinghua University, Beijing, 100084, China

    Chunxiao Xing

  7. Tinghua University High School, Beijing, 100084, China

    Zihan Qu

Authors
  1. Xin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guigang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chunxiao Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zihan Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Li .

Editor information

Editors and Affiliations

  1. University of Arizona, Tucson, AZ, USA

    Hsinchun Chen

  2. Wuhan University, Wuhan, China

    Qing Fang

  3. University of Arizona, Tucson, AZ, USA

    Daniel Zeng

  4. Wuhan University, Wuhan, China

    Jiang Wu

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, X., Zhang, G., Xing, C., Qu, Z. (2018). Efficient Massive Medical Rules Parallel Processing Algorithms. In: Chen, H., Fang, Q., Zeng, D., Wu, J. (eds) Smart Health. ICSH 2018. Lecture Notes in Computer Science(), vol 10983. Springer, Cham. https://doi.org/10.1007/978-3-030-03649-2_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03649-2_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03648-5

  • Online ISBN: 978-3-030-03649-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation