Skip to main content
SpringerLink
Log in
Book cover

Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2017: Trends and Applications in Knowledge Discovery and Data Mining pp 3–14Cite as

Early Classification of Multivariate Time Series on Distributed and In-Memory Platforms

  • Conference paper
  • First Online:

  • 947 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10526))

Abstract

With the popularity of Internet of Things (IOT) applications, various kinds of active sensors are deployed and multivariate time series datasets are generated rapidly. Early classification of multivariate time series is an emerging topic in data mining due to the wide applications in many domains. The unique part of early classification lies in that it uses only earlier part of time series data to reach classification results with the same accuracy as by methods using complete time series information. Although a number of relevant studies have been presented recently, most of them didn’t consider the issues of data scale and execution efficiency simultaneously. The main research issue of this paper falls in how to mine interpretable patterns from multivariate time series data, with which effective classification models can be constructed to ensure the accuracy as well as earliness. To take into account the issues of data scale and execution efficiency simultaneously, we explore distributed in-memory computing techniques and multivariate shapelets-based approaches to construct a Spark-based in-memory mining framework to parallelize the feature extraction process. We implement a framework with Multivariate Shapelets Detection (MSD) method as a based example. Through empirical evaluation on various kinds of sensory datasets, the scalability of the framework is evaluated in terms of efficiency while ensuring the same accuracy and reliability in early classification of multivariate time series. This work is the first one to realize multivariate time series early classification on Spark distributed in-memory computing platform, which can serve as a good base for an extension to other time series classification methods based on shapelet feature extraction.

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   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

Learn about institutional subscriptions

References

  1. Apache Hadoop. http://hadoop.apache.org/

  2. Apache HBase. http://hbase.apache.org/

  3. Bache, K., Lichman, M.: UCI machine learning repository (2013)

    Google Scholar 

  4. Bregón, A., Simón, M.A., Rodríguez, J.J., Alonso, C., Pulido, B., Moro, I.: Early fault classification in dynamic systems using case-based reasoning. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds.) CAEPIA 2005. LNCS, vol. 4177, pp. 211–220. Springer, Heidelberg (2006). doi:10.1007/11881216_23

    Chapter  Google Scholar 

  5. Dachraoui, A., Bondu, A., Cornuéjols, A.: Early classification of time series as a non myopic sequential decision making problem. In: Appice, A., Rodrigues, P., Santos Costa, V., Soares, C., Gama, J., Jorge, A. (eds.) ECML PKDD 2015. LNCS, vol. 4177, pp. 433–447. Springer, Cham (2015). doi:10.1007/978-3-319-23528-8_27

    Chapter  Google Scholar 

  6. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  7. Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proc. VLDB Endow. 1(2), 1542–1552 (2008)

    Article  Google Scholar 

  8. Gates, A.F., Natkovich, O., Chopra, S., Kamath, P., Narayanamurthy, S.M., Olston, C., Reed, B., Srinivasan, S., Srivastava, U.: Building a high-level dataflow system on top of Map-Reduce: the Pig experience. Proc. VLDB Endow. 2(2), 1414–1425 (2009)

    Article  Google Scholar 

  9. Ghalwash, M.F., Obradovic, Z.: Early classification of multivariate temporal observations by extraction of interpretable shapelets. BMC Bioinform. 13(1), 1 (2012)

    Article  Google Scholar 

  10. Ghalwash, M.F., Ramljak, D., Obradović, Z.: Early classification of multivariate time series using a hybrid HMM/SVM model. In: 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1–6. IEEE, October 2012

    Google Scholar 

  11. Ghalwash, M.F., Radosavljevic, V., Obradovic, Z.: Extraction of interpretable multivariate patterns for early diagnostics. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 201–210. IEEE, December 2013

    Google Scholar 

  12. Ghalwash, M.F., Radosavljevic, V., Obradovic, Z.: Utilizing temporal patterns for estimating uncertainty in interpretable early decision making. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 402–411. ACM, August 2014

    Google Scholar 

  13. He, G., Duan, Y., Peng, R., Jing, X., Qian, T., Wang, L.: Early classification on multivariate time series. Neurocomputing 149, 777–787 (2015)

    Article  Google Scholar 

  14. Junqueira, F.P., Reed, B.C.: The life and times of a zookeeper. In: Proceedings of the 28th ACM Symposium on Principles of Distributed Computing, p. 4. ACM, August 2009

    Google Scholar 

  15. Lin, Y.F., Chen, H.H., Tseng, V.S., Pei, J.: Reliable early classification on multivariate time series with numerical and categorical attributes. In: Cao, T., Lim, E.P., Zhou, Z.H., Ho, T.B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS, vol. 9077, pp. 199–211. Springer, Cham (2015). doi:10.1007/978-3-319-18038-0_16

    Google Scholar 

  16. Lines, J., Davis, L.M., Hills, J., Bagnall, A.: A shapelet transform for time series classification. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 289–297. ACM, August 2012

    Google Scholar 

  17. Mueen, A., Keogh, E., Young, N.: Logical-shapelets: an expressive primitive for time series classification. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1154–1162. ACM, August 2011

    Google Scholar 

  18. Olszewski, R.T.: Generalized feature extraction for structural pattern recognition in time-series data (No. CMU-CS-01-108). Carnegie-Mellon University Pittsburgh, PA School of Computer Science (2001)

    Google Scholar 

  19. Rodríguez, J.J., Alonso, C.J., Boström, H.: Boosting interval based literals. Intell. Data Anal. 5(3), 245–262 (2001)

    MATH  Google Scholar 

  20. Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The Hadoop distributed file system. In: 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), pp. 1–10. IEEE, May 2010

    Google Scholar 

  21. Thusoo, A., Sarma, J.S., Jain, N., Shao, Z., Chakka, P., Anthony, S., Liu, H., Wyckoff, P., Murthy, R.: Hive: a warehousing solution over a map-reduce framework. Proc. VLDB Endow. 2(2), 1626–1629 (2009)

    Article  Google Scholar 

  22. Xi, X., Keogh, E., Shelton, C., Wei, L., Ratanamahatana, C.A.: Fast time series classification using numerosity reduction. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 1033–1040. ACM, June 2006

    Google Scholar 

  23. Xing, Z., Pei, J., Dong, G., Philip, S.Y.: Mining sequence classifiers for early prediction. In: SDM, pp. 644–655, April 2008

    Google Scholar 

  24. Xing, Z., Pei, J., Philip, S.Y.: Early prediction on time series: a nearest neighbor approach. In: IJCAI, pp. 1297–1302, July 2009

    Google Scholar 

  25. Xing, Z., Pei, J., Philip, S.Y., Wang, K.: Extracting interpretable features for early classification on time series. In: SDM, vol. 11, pp. 247–258, April 2011

    Google Scholar 

  26. Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947–956. ACM, June 2009

    Google Scholar 

  27. Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation, p. 2. USENIX Association, April 2012

    Google Scholar 

Download references

Acknowledgement

This study was conducted under the “Complex Event Processing System” project of the Institute for Information Industry which is subsidized by the Ministry of Economic Affairs of the Republic of China.

Author information

Authors and Affiliations

  1. National Chiao Tung University, Hsinchu, Taiwan, Republic of China

    Vincent S. Tseng, Huai-Shuo Huang & Chia-Wei Huang

  2. Institute for Information Industry, Taipei, Taiwan, Republic of China

    Ping-Feng Wang & Chu-Feng Li

Authors
  1. Vincent S. Tseng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huai-Shuo Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chia-Wei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ping-Feng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chu-Feng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vincent S. Tseng .

Editor information

Editors and Affiliations

  1. Seoul National University, Seoul, Korea (Republic of)

    U Kang

  2. School of Information Systems, Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Chinese University of Hong Kong, Hong Kong, China

    Jeffrey Xu Yu

  4. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Tseng, V.S., Huang, HS., Huang, CW., Wang, PF., Li, CF. (2017). Early Classification of Multivariate Time Series on Distributed and In-Memory Platforms. In: Kang, U., Lim, EP., Yu, J., Moon, YS. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10526. Springer, Cham. https://doi.org/10.1007/978-3-319-67274-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67274-8_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67273-1

  • Online ISBN: 978-3-319-67274-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation