Theoretical Challenges in Knowledge Discovery in Big Data - A Logic Reasoning and a Graph Theoretical Point of View

Pavel Surynek, Petra Surynková

Abstract

This paper addresses a problem of knowledge discovery in big data from the point of view of theoretical computer science. Contemporary characterization of big data is often preoccupied by its volume, velocity of change, and variety that causes technical difficulties to handle the data efficiently while theoretical challenges that are offered by big data are neglected at the same time. Contrary to this preoccupation with technical issues, we would like to discuss more theoretical issues focused on the goal briefly expressed as what be understood from big data by imitating human like reasoning through logic and algorithmic means. The ultimate goal marked out in this paper is to develop an automation of the reasoning process that can manipulate and understand data in volumes that is beyond human abilities and to investigate if substantially different patterns appear in big data than in small data.

References

  1. Akers, S.: Binary decision diagrams. IEEE Transactions on Computers, Vol. 27(6), 509-516, IEEE Press, 1978.
  2. Argelich, J., Li, C.-M., Manya F., Planes, J.: The First and Second Max-SAT Evaluations. Journal on Satisfiability, Vol. 4, 251-278, IOS Press, 2008.
  3. Baader, F., Hladik, J., Peñaloza, R.: Automata can show PSpace results for Description Logics. Information and Computation, Special Issue: LATA 2007, 206(9- 10):1045-1056, Elsevier, 2008.
  4. Battiti, R., Protasi, M.: Handbook of Combinatorial Optimization. Kluwer, 1998.
  5. Bienvenu, M., Ortiz, M., Simkus, M.: Conjunctive Regular Path Queries in Lightweight Description Logics. Proceedings of IJCAI 2013, IJCAI/AAAI, 2013.
  6. Di Battista, G., Eades, P., Tamassia, R., Tollis, I. G.: Graph Drawing: Algorithms for the Visualization of Graphs. Prentice-Hall, 1998.
  7. Di Battista, G., Tamassia, R., Tollis, I. G.: International Symposium on Graph Drawing. Web Page, http://www.graphdrawing.org/, 2014, [accessed in February 2014].
  8. Eén, N., Sörensson, N.: An Extensible SAT-solver. Proceedings of SAT 2003, LNCS 2919, 502-518, Springer Verlag, 2004.
  9. Golumbic, M. C.: Algorithmic Graph Theory and Perfect Graphs. Academic Press, 1980.
  10. Hitzler, P., Janowicz, K.: Linked Data, Big Data, and the 4th Paradigm. Semantic Web, 4(3), 233-235, IOS Press, 2013.
  11. Hitzler, P., Krötzsch, M., Rudolph, S.: Foundations of Semantic Web Technologies. Textbooks in Computing, Chapman and Hall/CRC Press, 2009.
  12. Hodges, W.: Model Theory. Cambridge University Press, 1993.
  13. Huang, S., Li, Q., Hitzler, P.: Reasoning with Inconsistencies in Hybrid MKNF Knowledge Bases. Logic Journal of the IGPL 21 (2), 263-290, Oxford University Press, 2013.
  14. Joshi, A., Hitzler, P., Dong, G.: Logical Linked Data Compression. Proceedings of ESWC 2013, LNCS 7882, 170-184, Springer Verlag, 2013.
  15. Knorr, M., Alferes, J. J., Hitzler, P.: Local Closed-World Reasoning with Description Logics under the Wellfounded Semantics. Artificial Intelligence 175 (9-10), 1528-1554, Elsevier, 2011.
  16. Koren, Y., Bell, R. M., Volinsky, C.: Matrix Factorization Techniques for Recommender Systems. IEEE Computer, Vol. 42 (8), 30-37, IEEE Press, 2009.
  17. Krötzsch, M., Rudolph, S., Hitzler, P.: Complexities of Horn Description Logics. ACM Transactions on Computational Logic, Vol. 14 (1), ACM Press, 2013.
  18. Laney, D.: The Importance of 'Big Data': A Definition. Gartner, 2012.
  19. Lehmann, J., Bader, S., Hitzler, P.: Extracting Reduced Logic Programs from Artificial Neural Networks. Applied Intelligence, Vol. 32(3), 249-266, Springer Verlag, 2010.
  20. Liberty, E.: Simple and deterministic matrix sketching. Proceedings KDD 2013, 581-588, ACM Press, 2013.
  21. Lutz, C. The complexity of Description Logics with concrete domains. PhD Thesis, LuFG Theoretical Computer Science, RWTH Aachen, Germany, 2002.
  22. Maaren, H. van, Franco, J.: The international SAT Competitions. Competition web page, http://www. satcompetition.org/, 2013, [accessed in February 2014].
  23. Miller, D. M., Drechsler, R.: Implementing a multiplevalued decision diagram package. Proceedings of ISMVL 1998, 52-57, IEEE Press, 1998.
  24. Mitchell, T.: Machine Learning. McGraw Hill, 1997.
  25. Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, 1988.
  26. Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (editors): Recommender Systems Handbook. Springer Verlag, 2011.
  27. Rice, M.: A Survey of Static Variable Ordering Heuristics for Efficient. BDD/MDD Construction. University of California, 2008.
  28. Rumelhart, D. E., Hinton, G. E., Williams, R. J.: Learning representations by back-propagating errors. Nature, Vol. 323 (6088): 533-536, Nature Publishing, 1986.
  29. Russell, S. and Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, 2009.
  30. Sebastiani, R., Vescovi, M.: Automated Reasoning in Modal and Description Logics via SAT Encoding: the Case Study of K(m)/ALC-Satisfiability. J. AI Res., Vol. 35: 343-389, AAAI Press, 2009.
  31. Surynek, P.: Redundancy Elimination in Highly Parallel Solutions of Motion Coordination Problems. Proceedings of ICTAI 2011, 701-708, IEEE Press, 2011.
  32. Zhang, G. P.: Neural Networks for Classification: A Survey. IEEE Transactions on Systems, Man, and Cybernetics-part C: Applications and Reviews, Vol. 30 (4), IEEE Press, 2000.
Download


Paper Citation


in Harvard Style

Surynek P. and Surynková P. (2014). Theoretical Challenges in Knowledge Discovery in Big Data - A Logic Reasoning and a Graph Theoretical Point of View . In Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2014) ISBN 978-989-758-049-9, pages 327-332. DOI: 10.5220/0005092503270332


in Bibtex Style

@conference{keod14,
author={Pavel Surynek and Petra Surynková},
title={Theoretical Challenges in Knowledge Discovery in Big Data - A Logic Reasoning and a Graph Theoretical Point of View},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2014)},
year={2014},
pages={327-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005092503270332},
isbn={978-989-758-049-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2014)
TI - Theoretical Challenges in Knowledge Discovery in Big Data - A Logic Reasoning and a Graph Theoretical Point of View
SN - 978-989-758-049-9
AU - Surynek P.
AU - Surynková P.
PY - 2014
SP - 327
EP - 332
DO - 10.5220/0005092503270332