Building on the Synergy of Machine and Human Reasoning to Tackle Data-Intensive Collaboration and Decision Making

  • Nikos Karacapilidis
  • Stefan Rüping
  • Manolis Tzagarakis
  • Axel Poigné
  • Spyros Christodoulou
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 10)


This paper reports on a hybrid approach aiming to facilitate and augment collaboration and decision making in data-intensive and cognitively-complex settings. The proposed approach exploits and builds on the most prominent high-performance computing paradigms and large data processing technologies to meaningfully search, analyze and aggregate data existing in diverse, extremely large and rapidly evolving sources. It can be viewed as an innovative workbench incorporating and orchestrating a set of interoperable services that reduce the data-intensiveness and complexity overload at critical decision points to a manageable level, thus permitting stakeholders to be more productive and concentrate on creative activities.


Data Mining Text Mining Latent Dirichlet Allocation Human Reasoning Decision Making Setting 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Bolloju, N., Khalifa, M., Turban, E.: Integrating Knowledge Management into Enterprise Environments for the Next Generation Decision Support. Decision Support Systems 33, 163–176 (2002)CrossRefGoogle Scholar
  3. 3.
    Chu, C.T., Kim, S.K., Lin, Y.A., Yu, Y., Bradski, G.R., Ng, A.Y., Olukotun, K.: Map-reduce for machine learning on multicore. In: Schölkopf, B., Platt, J.C., Hoffman, T. (eds.) Proceedings of the Twentieth Annual Conference on Advances in Neural Information Processing Systems, Vancouver, Canada, December 4-7, vol. 19, MIT Press, Cambridge (2006)Google Scholar
  4. 4.
    Eppler, M.J., Mengis, J.: The Concept of Information Overload: A Review of Literature from Organization Science, Accounting, Marketing, MIS, and Related Disciplines. The Information Society 20, 325–344 (2004)CrossRefGoogle Scholar
  5. 5.
    Ferrucci, D., Lally, A.: UIMA: an architectural approach to unstructured information processing in the corporate research environment. Natural Language Engineering archive 10(3-4), 327–348 (2004)CrossRefGoogle Scholar
  6. 6.
    Friesen, N., Rüping, S.: Workflow Analysis Using Graph Kernels. In: Proceedings of the ECML/PKDD Workshop on Third-Generation Data Mining: Towards Service-Oriented Knowledge Discovery (SoKD 2010), Barcelona, Spain (2010)Google Scholar
  7. 7.
    Horváth, T., Paass, G., Reichartz, F., Wrobel, S.: A logic-based approach to relation extraction from texts. In: De Raedt, L. (ed.) ILP 2009. LNCS, vol. 5989, pp. 34–48. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    IDC , The Diverse and Exploding Digital Universe, White Paper (March 2008),
  9. 9.
    Ingersoll, G.: Introducing Apache Mahout, IBM developer works, Java Technical library (2009),
  10. 10.
    Rao, S.N.T., Prasad, E.V., Venkateswarlu, N.B.: A scalable k-means clustering algorithm on Multi-Core architecture. In: Proc. of International Conference on Methods and Models in Computer Science (ICM2CS 2009), pp. 1–9 (2009)Google Scholar
  11. 11.
    Rüping, S., Punko, N., Günter, B., Grosskreutz, H.: Procurement Fraud Discovery using Similarity Measure Learning. Transactions on Case-based Reasoning 1(1), 37–46 (2008)Google Scholar
  12. 12.
    Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R., Carlsson, C.: Past, Present and Future of Decision Support Technology. Decision Support Systems 33, 111–126 (2002)CrossRefGoogle Scholar
  13. 13.
    Wegener, D., Mock, M., Adranale, D., Wrobel, S.: Toolkit-based high-performance Data Mining of large Data on MapReduce Clusters. In: Proc. of the 1st IEEE ICDM Workshop on Large-scale Data Mining: Theory & Applications (2009)Google Scholar
  14. 14.
    Whitelaw, C., Kehlenbeck, A., Petrovic, N., Ungar, L.: Web-Scale Named Entity Recognition. In: Proceedings of CIKM, pp. 123-132 (2008)Google Scholar
  15. 15.
    Yan, F., Xu, N., Qi, Y.: Parallel inference for latent Dirichlet allocation on graphics processing units. In: Advances in Neural Information Processing Systems, vol. 22, pp. 2134–2142 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nikos Karacapilidis
    • 1
  • Stefan Rüping
    • 2
  • Manolis Tzagarakis
    • 1
  • Axel Poigné
    • 2
  • Spyros Christodoulou
    • 1
  1. 1.University of Patras and RA CTIRio PatrasGreece
  2. 2.Schloss BirlinghovenFraunhofer IAISSankt AugustinGermany

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