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)


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.


Multi-Agent system Distributed system Big Data Processing. 



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.


  1. 1.
    The foundation for intelligent agent.
  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). 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). 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). 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). 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

Personalised recommendations