Skip to main content
SpringerLink
Log in

A Novel Data Representation Method for Smart Cities’ Big Data

  • Chapter
  • First Online:
Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 186))

  • 976 Accesses

Abstract

In the past decades, the evolution of big cities was followed by the emergence of smart city technologies. These new technologies enable in-depth analysis, optimization of public city services, and new modes of governance. However, as the cities’ infrastructure develops, the emerging data sources generate massive datasets. Currently, the efficient and accurate processing of smart cities’ enormous time series datasets poses a particular challenge to data scientists.

To overcome this problem, many high-level representations of time series have been proposed, including Fourier transform, wavelet transform, or symbolic representation. Applying fundamental symbolization techniques for time series with multiple variables, such as Symbolic Aggregate Approximation (SAX), results in distinct sequences of symbols for each variable.

A novel multivariate extension of SAX will be presented, which allows to express a multivariate time series with one sequence of symbols. Integrating individual sequences in one symbolic sequence provides better expressive power, while our modified SAX distance measure can be applied for clustering and classification tasks in smart cities, decreasing the enormous dataset storage and speeding up the big data processing. Performance evaluation shows that our multivariate symbolic representation results in better accuracy and dimension reduction than the classical SAX method.

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 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 69.99
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

Institutional subscriptions

References

  1. S.h. An, B.H. Lee, D.R. Shin, in 2011 Third International Conference on Computational Intelligence, Communication Systems and Networks (IEEE, 2011), pp. 332–337

    Google Scholar 

  2. F. Ahmed, Y. Hawas, Transportation Research Part C: Emerging Technologies 60, 52 (2015)

    Article  Google Scholar 

  3. Caltrans performance measurement system (PEMS). URL http://pems.dot.ca.gov/

  4. T. Lin, H. Rivano, F. Le Mouël, IEEE Transactions on Intelligent Transportation Systems 18(12), 3229 (2017)

    Article  Google Scholar 

  5. B. Siregar, A.B.A. Nasution, F. Fahmi, in 2016 International Conference on ICT For Smart Society (ICISS) (IEEE, 2016), pp. 49–52

    Google Scholar 

  6. S. Seto, W. Zhang, Y. Zhou, in 2015 IEEE Symposium Series on Computational Intelligence (IEEE, 2015), pp. 1399–1406

    Google Scholar 

  7. N. Dawar, N. Kehtarnavaz, in 2018 IEEE 14th International Conference on Control and Automation (ICCA) (IEEE, 2018), pp. 482–485

    Google Scholar 

  8. A. Fleury, M. Vacher, N. Noury, IEEE transactions on information technology in biomedicine 14(2), 274 (2009)

    Google Scholar 

  9. X. Su, H. Tong, P. Ji, Tsinghua science and technology 19(3), 235 (2014)

    Google Scholar 

  10. A. Bagnall, J. Lines, A. Bostrom, J. Large, E. Keogh, Data Mining and Knowledge Discovery 31(3), 606 (2017)

    Article  MathSciNet  Google Scholar 

  11. S. Aghabozorgi, A.S. Shirkhorshidi, T.Y. Wah, Information Systems 53, 16 (2015)

    Article  Google Scholar 

  12. H.I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, P.A. Muller, Data Mining and Knowledge Discovery pp. 1–47 (2019)

    Google Scholar 

  13. M. Ahmed, A.N. Mahmood, M.R. Islam, Future Generation Computer Systems 55, 278 (2016)

    Article  Google Scholar 

  14. B. Esmael, A. Arnaout, R.K. Fruhwirth, G. Thonhauser, in International Conference on Computational Science and Its Applications (Springer, 2012), pp. 392–403

    Google Scholar 

  15. R. Agrawal, C. Faloutsos, A. Swami, in International conference on foundations of data organization and algorithms (Springer, 1993), pp. 69–84

    Google Scholar 

  16. Y. Qu, B.l. Adam, M. Thornquist, J.D. Potter, M.L. Thompson, Y. Yasui, J. Davis, P.F. Schellhammer, L. Cazares, M. Clements, et al., Biometrics 59(1), 143 (2003)

    Google Scholar 

  17. B.K. Yi, C. Faloutsos, in VLDB, vol. 385 (Citeseer, 2000), vol. 385, p. 99

    Google Scholar 

  18. C. Guo, H. Li, D. Pan, in International conference on knowledge science, engineering and management (Springer, 2010), pp. 234–244

    Google Scholar 

  19. E. Keogh, K. Chakrabarti, M. Pazzani, S. Mehrotra, ACM Sigmod Record 30(2), 151 (2001)

    Article  Google Scholar 

  20. I.V. Oseledets, E.E. Tyrtyshnikov, SIAM Journal on Scientific Computing 31(5), 3744 (2009)

    Article  MathSciNet  Google Scholar 

  21. J. Lin, E. Keogh, L. Wei, S. Lonardi, Data Mining and knowledge discovery 15(2), 107 (2007)

    Google Scholar 

  22. B. Lkhagva, Y. Suzuki, K. Kawagoe, DEWS2006 4A-i8 7 (2006)

    Google Scholar 

  23. S. Malinowski, T. Guyet, R. Quiniou, R. Tavenard, in International Symposium on Intelligent Data Analysis (Springer, 2013), pp. 273–284

    Google Scholar 

  24. C.T. Zan, H. Yamana, in Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services (ACM, 2016), pp. 72–80

    Google Scholar 

  25. Y. Sun, J. Li, J. Liu, B. Sun, C. Chow, Neurocomputing 138, 189 (2014)

    Article  Google Scholar 

  26. A.M. Nagy, V. Simon, Pervasive and Mobile Computing 50, 148 (2018). DOI https://doi.org/10.1016/j.pmcj.2018.07.004

    Article  Google Scholar 

  27. C.T. Zan, H. Yamana, International Journal of Web Information Systems 13(4), 387 (2017)

    Article  Google Scholar 

  28. E. Keogh, S. Kasetty, Data Mining and knowledge discovery 7(4), 349 (2003)

    Google Scholar 

  29. E. Keogh, S. Lonardi, B.c. Chiu, in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (ACM, 2002), pp. 550–556

    Google Scholar 

  30. R.J. Larsen, M.L. Marx, et al., An introduction to mathematical statistics and its applications, vol. 2 (Prentice-Hall Englewood Cliffs, NJ, 1986)

    Google Scholar 

  31. A. Apostolico, M.E. Bock, S. Lonardi, Journal of Computational Biology 10(3–4), 283 (2003)

    Article  Google Scholar 

  32. K. Zhang, Y. Li, Y. Chai, L. Huang, in 2018 Chinese Control And Decision Conference (CCDC) (IEEE, 2018), pp. 2234–2240

    Google Scholar 

  33. N.C. Castro, P.J. Azevedo, Intelligent Data Analysis 19(3), 581–595 (2015). URL https://doi.org/10.3233/IDA-150733

  34. R. Xu, D.C. Wunsch, (2005)

    Google Scholar 

  35. D. Dua, C. Graff. UCI machine learning repository (2017). URL http://archive.ics.uci.edu/ml

  36. N. Hansen, in Towards a new evolutionary computation (Springer, 2006), pp. 75–102

    Google Scholar 

  37. N. Hansen, in Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers (ACM, 2009), pp. 2389–2396

    Google Scholar 

  38. R. Eberhart, J. Kennedy, in MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (IEEE, 1995), pp. 39–43

    Google Scholar 

  39. L.J.V. Miranda, Journal of Open Source Software 3 (2018). URL https://doi.org/10.21105/joss.00433

  40. N. Hansen, Y. Akimoto, P. Baudis. Cma-es/pycma on github (2019). URL https://doi.org/10.5281/zenodo.2559635

  41. A.M. Nagy. Msax (2019). URL https://doi.org/10.5281/zenodo.3402973

  42. P. Senin, S. Malinchik, in 2013 IEEE 13th international conference on data mining (IEEE, 2013), pp. 1175–1180

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics, Department of Networked Systems and Services, Budapest, Hungary

    Attila M. Nagy & Vilmos Simon

Authors
  1. Attila M. Nagy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vilmos Simon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Attila M. Nagy .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  2. University of Macedonia & Le.D.R.A. Group, Thessaloniki, Greece

    Stamatina Th. Rassia

  3. Center for Applied Optimization, University of Florida, Gainesville, FL, USA

    Arsenios Tsokas

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Nagy, A.M., Simon, V. (2022). A Novel Data Representation Method for Smart Cities’ Big Data. In: Pardalos, P.M., Rassia, S.T., Tsokas, A. (eds) Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities. Springer Optimization and Its Applications, vol 186. Springer, Cham. https://doi.org/10.1007/978-3-030-84459-2_6

Download citation

Publish with us

Policies and ethics

Search

Navigation