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A Distributed Multi-Agent System (MAS) Application For continuous and Integrated Big Data Processing

  • Ariona ShashajEmail author
  • Federico Mastrorilli
  • Massimiliano Morrelli
  • Giacomo Pansini
  • Enrico Iannucci
  • Massimiliano Polito
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11912)

Abstract

With the advent of Ambient Intelligence (AmI) as an inter-disciplinary methodology which ranges from Ubiquitous and Pervasive computing to Artificial Intelligence with the final aim to create a sensitive and responsive environment, the focus is moving towards integrated solutions of the encompassed technologies. Multi-Agent System (MAS) approach, characterized by a set of autonomous intelligent agents which can cooperate in order to achieve a common goal, can help the development of AmI integrated solutions. In this paper, we present a distributed MAS environment, Multi-Agent Specialized system (MASs), which supports the development of integrated AmI solutions. An application scenario considering the case of continuous Big Data processing is shown.

Keywords

Multi-Agent system Distributed system Big Data Processing. 

Notes

Acknowledgements

Funding/Support: This work was supported by the Horizon 2020-PON 2014/2020 project B.4.M.A.S.S “Big Data for Multi-Agent Specialized System”.

Contribution: The MASs environment has been developed by Ingegneria dei Sistemi Department, Network Contacts, Molfetta, Italy.

References

  1. 1.
    The foundation for intelligent agent. http://www.fipa.org/repository/standardspecs.html
  2. 2.
    Augusto, J.C., McCullagh, P.J.: Ambient intelligence: concepts and applications. Comput. Sci. Inf. Syst. 4(1), 1–27 (2007)CrossRefGoogle Scholar
  3. 3.
    Belghache, E., Georgé, J.P., Gleizes, M.P.: Towards an adaptive multi-agent system for dynamic big data analytics. In: 2016 International IEEE Conferences on Ubiquitous Intelligence & Computing (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp. 753–758. IEEE (2016)Google Scholar
  4. 4.
    Bellifemine, F., Caire, G., Poggi, A., Rimassa, G.: Jade: a software framework for developing multi-agent applications lessons learned. Inf. Softw. Technol. 50(1–2), 10–21 (2008)CrossRefGoogle Scholar
  5. 5.
    Bordini, R.H., Hübner, J.F., Wooldridge, M.: Programming Multi-agent Systems in AgentSpeak using Jason, vol. 8. Wiley, Hoboken (2007)CrossRefGoogle Scholar
  6. 6.
    Cao, L., Gorodetsky, V., Mitkas, P.A.: Agent mining: the synergy of agents and data mining. IEEE Intell. Syst. 24(3), 64–72 (2009)CrossRefGoogle Scholar
  7. 7.
    Di Marzo Serugendo, G., Gleizes, M.P., Karageorgos, A.: Self-organising software: from natural to artificial adaptation (2011)Google Scholar
  8. 8.
    Garcia-Molina, H.: Elections in a distributed computing system. IEEE Trans. Comput. 1, 48–59 (1982)CrossRefGoogle Scholar
  9. 9.
    Jagadish, H., et al.: Big data and its technical challenges. Commun. ACM 57(7), 86–94 (2014)CrossRefGoogle Scholar
  10. 10.
    Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Real-time Data Systems. Manning Publications Co., New York (2015)Google Scholar
  11. 11.
    Morrelli Massimiliano, C.M.: Nuova frontiera della classificazione testuale: big data e calcolo distribuito, pp. 1–20 (2019)Google Scholar
  12. 12.
    Pal, G., Li, G., Atkinson, K.: Multi-agent big-data lambda architecture model for e-commerce analytics. Data 3(4), 58 (2018)CrossRefGoogle Scholar
  13. 13.
    Palpanas, T.: Big sequence management: a glimpse of the past, the present, and the future. In: Freivalds, R.M., Engels, G., Catania, B. (eds.) SOFSEM 2016. LNCS, vol. 9587, pp. 63–80. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-49192-8_6CrossRefzbMATHGoogle Scholar
  14. 14.
    Piette, F., Caval, C., Dinont, C., Seghrouchni, A.E.F., Taillibert, P.: A multi-agent approach for the deployment of distributed applications in smart environments. Intelligent Distributed Computing X. SCI, vol. 678, pp. 37–46. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-48829-5_4CrossRefGoogle Scholar
  15. 15.
    Pokahr, A., Braubach, L., Lamersdorf, W.: Jadex: a BDI reasoning engine. In: Bordini, R.H., Dastani, M., Dix, J., El Fallah Seghrouchni, A. (eds.) Multi-Agent Programming. MSASSO, vol. 15, pp. 149–174. Springer, Boston, MA (2005).  https://doi.org/10.1007/0-387-26350-0_6CrossRefGoogle Scholar
  16. 16.
    Seghrouchni, A.E.F., Florea, A.M., Olaru, A.: Multi-agent systems: a paradigm to design ambient intelligent applications. In: Essaaidi, M., Malgeri, M., Badica, C. (eds.) Intelligent Distributed Computing IV, pp. 3–9. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Twardowski, B., Ryzko, D.: Multi-agent architecture for real-time big data processing. In: 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 3, pp. 333–337. IEEE (2014)Google Scholar
  18. 18.
    Winikoff, M.: Jack™ intelligent agents: an industrial strength platform. In: Bordini, R.H., Dastani, M., Dix, J., El Fallah Seghrouchni, A. (eds.) Multi-Agent Programming. MSASSO, vol. 15, pp. 175–193. Springer, Boston, MA (2005).  https://doi.org/10.1007/0-387-26350-0_7CrossRefGoogle Scholar
  19. 19.
    Zoumpatianos, K., Palpanas, T.: Data series management: Fulfilling the need for big sequence analytics. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp. 1677–1678. IEEE (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ariona Shashaj
    • 1
    Email author
  • Federico Mastrorilli
    • 1
  • Massimiliano Morrelli
    • 1
  • Giacomo Pansini
    • 1
  • Enrico Iannucci
    • 1
  • Massimiliano Polito
    • 1
  1. 1.Network ContactsMolfettaItaly

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