Skip to main content

Correlation Feature of Big Data in Smart Cities

  • Conference paper
  • First Online:
Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9645))

Included in the following conference series:

Abstract

Smart cities are constantly faced with the generated data resources. To effectively manage and utilize the big city data, data vitalization technology is proposed. Considering the complex and diverse relationships among the big data, data correlation is very important for data vitalization. This paper presents a framework for data correlation and depicts the discovery, representation and growth of data correlation. In particular, this paper proposes an innovative representation of data correlation, namely the data correlation diagram. Based on the basic and the multi-stage data relations, we optimize the data correlation diagrams according to the transitive rules. We also design dynamic data diagrams to support data and relation changes, reducing the response time to data changes and enabling the autonomous growth of the vitalized data and the relations. Finally an instance of smart behaviors is introduced which verifies the feasibility and efficiency of the data relation diagram.

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

Access this chapter

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. Grady, M., Hare, G.: How smart is your city? Science 335(6076), 1581–1582 (2012)

    Article  Google Scholar 

  2. Hancke, G.P., Silva, B.D.E., Hancke, G.P.: The role of advanced sensing in smart cities. Sensors 13(1), 393–425 (2013)

    Article  Google Scholar 

  3. Xiong, Z., Luo, W., Chen, L., Ni, L.M.: Data Vitalization: a new paradigm for large-scale dataset analysis. In: IEEE 16th International Conference on Parallel and Distributed Systems (ICPADS), pp. 251–258. Shanghai, 8–10 December 2010

    Google Scholar 

  4. Tian, X.: The Technology Research of Multi-sensor Data Association and Track Fusion. Harbin Engineering University, Harbin (2012)

    Google Scholar 

  5. Zhu, H.: An institution theory of formal meta-modelling in graphically extended BNF. Front. Comput. Sci. 6(1), 40C56 (2012)

    MathSciNet  MATH  Google Scholar 

  6. Zemke, F.: Whats new in SQL. SIGMOD 41(1), 67–73 (2012)

    Article  Google Scholar 

  7. Du, Y.: An improved algorithm for mining association rules. Xidian University (2012)

    Google Scholar 

  8. Agrawal, R., Imieliski, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Record 22(2), 207–216 (1993)

    Article  Google Scholar 

  9. Agrawal, R., Ghosh, S., Imielinski, T., Iyer, B., Swami, A.: An interval classifier for database mining applications. In: International Conference on Very Large Data Bases (VLDB), pp. 560–573 (1992)

    Google Scholar 

  10. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: International Conference on Very Large Data Bases (VLDB), pp. 487–499 (1994)

    Google Scholar 

  11. Wang, K., Liu, T., Han, J.: Mining frequent patterns using support constraints. In: International Conference on Very Large Data Bases (VLDB), pp. 43–52 (2000)

    Google Scholar 

Download references

Acknowledgement

We gratefully acknowledge the support from National High Technology Research and Development Program of China (2013AA01A601), National Natural Science Foundation of China (61173009, 61502320), the Science Foundation of Shenzhen City in China (JCYJ20140509150917445), Science & Technology Project of Beijing Municipal Commission of Education in China (KM201410028015), and the State Key Laboratory of Software Development Environment (SKLSDE-2015ZX-25).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Juhua Pu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhang, Y., Tang, X., Du, B., Liu, W., Pu, J., Chen, Y. (2016). Correlation Feature of Big Data in Smart Cities. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-32055-7_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32054-0

  • Online ISBN: 978-3-319-32055-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics