Dynamic and Scalable Real-time Analytics in Logistics - Combining Apache Storm with Complex Event Processing for Enabling New Business Models in Logistics

Benjamin Gaunitz, Martin Roth, Bogdan Franczyk

2015

Abstract

In this paper we present an approach for an information system which is capable of processing and analysing vast amounts of data. In addition to Big Data solutions we do not focus on ex post batch processing but on online stream processing. We use Apache Storm in combination with Complex Event Processing to provide a scalable and dynamic event-driven information system, providing logistics businesses with relevant information in real-time to increase their data and process transparency.

References

  1. Apache Storm (2014), “storm”, available at: https://storm.apache.org/ (accessed 16 January 2015).
  2. BIEK (2014), Anzahl der Sendungen von Kurier-, Express- und Paketdiensten (KEP) in Deutschland in den Jahren 2000 bis 2013 (in Millionen), Statista - Das Statistik-Portal.
  3. Bruns, R. and Dunkel, J. (2010), Event-Driven Architecture, Springer, Berlin, Heidelberg.
  4. Dean, J. and Ghemawat, S. (2008), “MapReduce. Simplified Data Processing on Large Clusters”, Communications of the ACM, Vol. 51 No. 1, pp. 107- 113.
  5. Ellis, B. (2014), Real-Time Analytics, 1st ed., Wiley, Indianapolis.
  6. Hackathorn, R. (2003), “Minimizing Action Distance”, available at: http://www.tdan.com/view-articles/5132/ (accessed 19 January 2015).
  7. Isoyama, K., Kobayashi, Y., Sato, T., Kida, K., Yoshida, M. and Tagato, H. (2012), “A scalable complex event processing system and evaluations of its performance”, in Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems, ACM, New York, NY, pp. 123-126.
  8. Jeske, M., Grüner, M. and Weiß, F. (2013), Big Data in Logistics, Troisdorf, Germany.
  9. Luckham, D., Schulte, R., Adkins, J., Bizarro, P., Jacobsen, H.-A., Mavashev, A., Michelson, B.M. and Niblett, P. (2011), Event Processing Glossary: Version 2.0.
  10. Luckham, D.C. (2002), The Power of Events, AddisonWesley, Boston.
  11. Mishne, G., Dalton, J., Li, Z., Sharma, A. and Lin, J. (2013), “Fast data in the era of big data”, in Ross, K. and Data, ACM Special Interest Group on Management of (Eds.), Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, ACM, New York, NY, USA, pp. 1147-1158.
  12. Nissen, V. and Bothe, M. (2002), “Fourth Party Logistics - ein Überblick”, Logistik Management, Vol. 4 No. 1, pp. 16-26.
  13. Schwegmann, B., Matzner, M. and Janiesch, C. (2013), “A Method and Tool for Predictive Event-Driven Process Analytics”, in Alt, R. and Franczyk, B. (Eds.), Proceedings of the 11th International Conference on Wirtschaftsinformatik, Leipzig, Univ., Leipzig.
  14. Toshniwal, A., Donham, J., Bhagat, N., Mittal, S., Ryaboy, D., Taneja, S., Shukla, A., Ramasamy, K., Patel, J.M., Kulkarni, S., Jackson, J., Gade, K. and Fu, M. (2014), “Storm@twitter”, in SIGMOD 7814: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, ACM, New York, NY, USA, pp. 147-156.
  15. van Stijn, E., Hesketh, D., Tan, Y.-H., Klievink, B., Overbeek, S., Heijmann, F., Pikart, M. and Buterly, T. (2013), “The Data Pipeline”, in United Nations Economic Commission for Europe (Ed.), Connecting International Trade: Single Windows and Supply Chains in the Next Decade, United Nations, New York and Geneva, pp. 158-183.
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Paper Citation


in Harvard Style

Gaunitz B., Roth M. and Franczyk B. (2015). Dynamic and Scalable Real-time Analytics in Logistics - Combining Apache Storm with Complex Event Processing for Enabling New Business Models in Logistics . In Proceedings of the 10th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-100-7, pages 289-294. DOI: 10.5220/0005467602890294


in Bibtex Style

@conference{enase15,
author={Benjamin Gaunitz and Martin Roth and Bogdan Franczyk},
title={Dynamic and Scalable Real-time Analytics in Logistics - Combining Apache Storm with Complex Event Processing for Enabling New Business Models in Logistics},
booktitle={Proceedings of the 10th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2015},
pages={289-294},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005467602890294},
isbn={978-989-758-100-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - Dynamic and Scalable Real-time Analytics in Logistics - Combining Apache Storm with Complex Event Processing for Enabling New Business Models in Logistics
SN - 978-989-758-100-7
AU - Gaunitz B.
AU - Roth M.
AU - Franczyk B.
PY - 2015
SP - 289
EP - 294
DO - 10.5220/0005467602890294