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International Conference on Computational Science and Its Applications

ICCSA 2017: Computational Science and Its Applications – ICCSA 2017 pp 325–341Cite as

PrescStream: A Framework for Streaming Soft Real-Time Predictive and Prescriptive Analytics

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10404))

Abstract

All the data volume generated by modern applications brings opportunities for knowledge extraction and value creation. In this sense, the integration of predictive and prescriptive analytics may help the industry and users to be more productive and successful. It means not only to estimate an outcome but also to act on it in the real world. Nonetheless, mastering these concepts and providing their integration is not an easy task. This work proposes PrescStream, a proof of concept framework that uses machine learning based prediction, and process this outcome result to do prescriptive analytics, allowing researchers to integrate predictive and prescriptive analytics into their experiments. It has a scalable, fault-tolerant microservices based architecture, making it ideal for cloud deployment and IoT (internet of things) applications. The paper describes the general architecture of the system, as well as a validation usage with result analysis.

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References

  1. Atzori, L., Iera, A., Morabito, G., Diee, A.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)

    Article  MATH  Google Scholar 

  2. Barber, R., Sharkey, M.: Course correction: using analytics to predict course success. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 259–262. ACM (2012)

    Google Scholar 

  3. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  4. Cassandra: Apache Cassandra. http://cassandra.apache.org/

  5. Dragoni, N., Giallorenzo, S., Lafuente, A.L., Mazzara, M., Montesi, F., Mustafin, R., Safina, L.: Microservices: yesterday, today, and tomorrow (2016). arXiv preprint arXiv:1606.04036

  6. Fielding, R.T., Taylor, R.N.: Principled design of the modern web architecture. ACM Trans. Internet Technol. (TOIT) 2(2), 115–150 (2002)

    Article  Google Scholar 

  7. Hearst, M.A., Dumais, S.T., Osman, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. 13, 18–28 (1998)

    Article  Google Scholar 

  8. Huang, Z., Wong, P.C., Mackey, P., Chen, Y., Ma, J., Schneider, K., Greitzer, F.L.: Managing complex network operation with predictive analytics. In: AAAI Spring Symposium: Technosocial Predictive Analytics, pp. 59–65 (2009)

    Google Scholar 

  9. Jhawar, R., Piuri, V., Santambrogio, M.: Fault tolerance management in cloud computing: a system-level perspective. IEEE Syst. J. 7(2), 288–297 (2013)

    Article  Google Scholar 

  10. Node: NodeJS. https://nodejs.org/

  11. Rabbit: Rabbitmq. https://www.rabbitmq.com/

  12. Redis: Redis. http://redis.io/

  13. Scikit: Scikit-learn. http://scikit-learn.org/

  14. Siegel, E.: Predictive Analytics: The Power to Predict Who will Click, Buy, Lie, or Die. Wiley, Hoboken (2013)

    Google Scholar 

  15. Tönjes, R., Barnaghi, P., Ali, M., Mileo, A., Hauswirth, M., Ganz, F., Ganea, S., Kjærgaard, B., Kuemper, D., Nechifor, S., et al.: Real time iot stream processing and large-scale data analytics for smart city applications. In: Poster Session, European Conference on Networks and Communications (2014)

    Google Scholar 

  16. Velloso, E., Bulling, A., Gellersen, H., Ugulino, W., Fuks, H.: Qualitative activity recognition of weight lifting exercises. In: Proceedings of the 4th Augmented Human International Conference, pp. 116–123. ACM (2013)

    Google Scholar 

  17. Zumel, N., Mount, J., Porzak, J.: Practical Data Science with R. Manning, Greenwich (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science Department, Federal University of Bahia, Salvador, Brazil

    Marcos de Aguiar & Fabíola Greve

  2. Humanities, Arts and Sciences Department, Federal University of Bahia, Salvador, Brazil

    Genaro Costa

Authors
  1. Marcos de Aguiar
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  2. Fabíola Greve
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  3. Genaro Costa
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Corresponding author

Correspondence to Marcos de Aguiar .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

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© 2017 Springer International Publishing AG

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de Aguiar, M., Greve, F., Costa, G. (2017). PrescStream: A Framework for Streaming Soft Real-Time Predictive and Prescriptive Analytics. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10404. Springer, Cham. https://doi.org/10.1007/978-3-319-62392-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-62392-4_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62391-7

  • Online ISBN: 978-3-319-62392-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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