Advertisement

Supporting Social Information Discovery from Big Uncertain Social Key-Value Data via Graph-Like Metaphors

  • Calvin S. H. Hoi
  • Carson K. Leung
  • Kimberly Tran
  • Alfredo Cuzzocrea
  • Mario Bochicchio
  • Marco Simonetti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10971)

Abstract

In the current era of big data, huge volumes of a wide variety of valuable data of different veracity (e.g., uncertain data) can be easily collected and generated from a broad range of data sources (e.g., social networking sites) at a high velocity in various real-life applications. Many traditional data management and analytic approaches may not be suitable for handling the big data due to their well-known 5V’s characteristics. In this paper, we present a cognitive-based system for social network analysis. Our system supports information discovery of interesting social patterns from big uncertain social networks—which are represented in the form of key-value pairs—capturing the perceived likelihood of the linkages among the social entities in the network.

Keywords

Cognitive computing Data mining Knowledge discovery Big data Social network Uncertain data Key-value store 

Notes

Acknowledgment

This project is partially supported by Natural Sciences and Engineering Research Council of Canada (NSERC) and the University of Manitoba.

References

  1. 1.
    Abu-Salih, B., Wongthongtham, P., Zhu, D., Alqrainy, S.: An approach for time-aware domain-based analysis of users’ trustworthiness in big social data. IJBD (now STBD) 2(1), 41–56 (2015)CrossRefGoogle Scholar
  2. 2.
    Braun, P., Cameron, J.J., Cuzzocrea, A., Jiang, F., Leung, C.K.: Effectively and efficiently mining frequent patterns from dense graph streams on disk. Procedia Comput. Sci. 35, 338–347 (2014)CrossRefGoogle Scholar
  3. 3.
    Braun, P., Cuzzocrea, A., Jiang, F., Leung, C.K.-S., Pazdor, A.G.M.: MapReduce-based complex big data analytics over uncertain and imprecise social networks. In: Bellatreche, L., Chakravarthy, S. (eds.) DaWaK 2017. LNCS, vol. 10440, pp. 130–145. Springer, Cham (2017)CrossRefGoogle Scholar
  4. 4.
    Braun, P., Cuzzocrea, A., Leung, C.K., Pazdor, A.G.M., Tanbeer, S.K.: Mining frequent patterns from IoT devices with fog computing. In: HPCS 2017, pp. 691–698 (2017)Google Scholar
  5. 5.
    Braun, P., Cuzzocrea, A., Leung, C.K., Pazdor, A., Tran, K.: Knowledge discovery from social graph data. Procedia Comput. Sci. 96, 682–691 (2016)CrossRefGoogle Scholar
  6. 6.
    Chen, I., Guo, J., Tsai, J.J.P.: Trust as a service for SOA-based IoT systems. STIOT 1(1), 43–52 (2017)CrossRefGoogle Scholar
  7. 7.
    Chen, J., Yang, Y.: Grid and workflows. In: Encyclopedia of Database Systems, 2nd edn. (2016).  https://doi.org/10.1007/978-1-4899-7993-3_1472-2Google Scholar
  8. 8.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. MIT Press, Cambridge (2009)Google Scholar
  9. 9.
    Cuzzocrea, A.: Accuracy control in compressed multidimensional data cubes for quality of answer-based OLAP tools. In: SSDBM 2006, pp. 301–310 (2006)Google Scholar
  10. 10.
    Cuzzocrea, A.: Privacy and security of big data: current challenges and future research perspectives. In: PSBD 2014, pp. 45–47 (2014)Google Scholar
  11. 11.
    Cuzzocrea, A., Bertino, E.: A secure multiparty computation privacy preserving OLAP framework over distributed XML data. In: ACM SAC 2010, pp. 1666–1673 (2010)Google Scholar
  12. 12.
    Cuzzocrea, A., Bertino, E.: Privacy preserving OLAP over distributed XML data: a theoretically-sound secure-multiparty-computation approach. JCSS 77(6), 965–987 (2011)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Cuzzocrea, A., Furfaro, F., Saccà, D.: Enabling OLAP in mobile environments via intelligent data cube compression techniques. JISS 33(2), 95–143 (2009)Google Scholar
  14. 14.
    Cuzzocrea, A., Han, Z., Jiang, F., Leung, C.K., Zhang, H.: Edge-based mining of frequent subgraphs from graph streams. Procedia Comput. Sci. 60, 573–582 (2015)CrossRefGoogle Scholar
  15. 15.
    Cuzzocrea, A., Lee, W., Leung, C.K.: High-recall information retrieval from linked big data. In: IEEE COMPSAC 2015, vol. 2, pp. 712–717 (2015)Google Scholar
  16. 16.
    Cuzzocrea, A., Leung, C.K.: Upper bounds to expected support for frequent itemset mining of uncertain big data. In: ACM SAC 2015, pp. 919–921 (2015)Google Scholar
  17. 17.
    Cuzzocrea, A., Matrangolo, U.: Analytical synopses for approximate query answering in OLAP environments. In: Galindo, F., Takizawa, M., Traunmüller, R. (eds.) DEXA 2004. LNCS, vol. 3180, pp. 359–370. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Han, Z., Leung, C.K.: FIMaaS: scalable frequent pattern mining-as-a-service on cloud for non-expert miners. In: BigDAS 2015, pp. 84–91 (2015)Google Scholar
  19. 19.
    Jiang, F., Leung, C.K., Liu, D.: Efficiency improvements in social network communication via MapReduce. In: IEEE DSDIS 2015, pp. 161–168 (2015)Google Scholar
  20. 20.
    Kawagoe, K., Leung, C.K.: Similarities of frequent following patterns and social entities. Procedia Comput. Sci. 60, 642–651 (2015)CrossRefGoogle Scholar
  21. 21.
    Lahoti, P., Garimella, K., Gionis, A.: Joint non-negative matrix factorization for learning ideological leaning on Twitter. In: ACM WSDM 2018, pp. 351–359 (2018)Google Scholar
  22. 22.
    Leung, C.K.: Big data mining applications and services. In: BigDAS 2015, pp. 1–8 (2015)Google Scholar
  23. 23.
    Leung, C.K., Braun, P., Enkhee, M., Pazdor, A.G.M., Sarumi, O.A., Tran, K.: Knowledge discovery from big social key-value data. In: IEEE CIT 2016, pp. 484–491 (2016)Google Scholar
  24. 24.
    Leung, C.K., Cuzzocrea, A.: Frequent subgraph mining from streams of uncertain data. In: C3S2E 2015, pp. 18–27 (2015)Google Scholar
  25. 25.
    Leung, C.K.-S., Hayduk, Y.: Mining frequent patterns from uncertain data with mapreduce for big data analytics. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds.) DASFAA 2013, Part I. LNCS, vol. 7825, pp. 440–455. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  26. 26.
    Leung, C.K.-S., Jiang, F.: Big data analytics of social networks for the discovery of “Following” patterns. In: Madria, S., Hara, T. (eds.) DaWaK 2015. LNCS, vol. 9263, pp. 123–135. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-22729-0_10CrossRefGoogle Scholar
  27. 27.
    Leung, C.K., Jiang, F., Pazdor, A.G.M., Peddle, A.M.: Parallel social network mining for interesting ‘following’ patterns. Concurrency Comput. Pract. Exp. 28(15), 3994–4012 (2016)CrossRefGoogle Scholar
  28. 28.
    Leung, C.K., Tanbeer, S.K., Cuzzocrea, A., Braun, P., MacKinnon, R.K.: Interactive mining of diverse social entities. Int. J. Knowl. Based Intell. Eng. Syst. 20(2), 97–111 (2016)CrossRefGoogle Scholar
  29. 29.
    Li, Y.: Socially enhanced account benchmarking in application management service (AMS). IJSC (now STSC) 3(1), 1–13 (2015)Google Scholar
  30. 30.
    MacKinnon, R.K., Leung, C.K.: Stock price prediction in undirected graphs using a structural support vector machine. In: IEEE/WIC/ACM WI-IAT 2015, vol. 1, pp. 548–555 (2015)Google Scholar
  31. 31.
    Madden, S.: From databases to big data. IEEE Internet Comput. 16(3), 4–6 (2012)CrossRefGoogle Scholar
  32. 32.
    McAuley, J., Leskovec, J.: Discovering social circles in ego networks. ACM TKDD 8(1), article 4 (2014)CrossRefGoogle Scholar
  33. 33.
    Peterson, B., Baumgartner, G., Wang, Q.: A decentralized scheduling framework for many-task scientific computing in a hybrid cloud. STCC 5(1), 1–13 (2017)Google Scholar
  34. 34.
    Petri, I., Punceva, M., Rana, O.F., Theodorakopoulos, G., Rezgui, Y.: A broker based consumption mechanism for social clouds. IJCC (now STCC) 2(1), 45–57 (2014)Google Scholar
  35. 35.
    Rahman, Q.M., Fariha, A., Mandal, A., Ahmed, C.F., Leung, C.K.: A sliding window-based algorithm for detecting leaders from social network action streams. In: IEEE/WIC/ACM WI-IAT 2015, vol. 1, pp. 133–136 (2015)Google Scholar
  36. 36.
    Salah, K.: A queuing model to achieve proper elasticity for cloud cluster jobs. IJCC (now STCC) 1(1), 53–64 (2013)MathSciNetGoogle Scholar
  37. 37.
    Singh, S., Liu, Y., Ding, W., Li, Z.: Empirical evaluation of big data analytics using design of experiment: case studies on telecommunication data. STBD 3(2), 1–20 (2016)CrossRefGoogle Scholar
  38. 38.
    Taber, L., Whittaker, S.: Personality depends on the medium: differences in self-perception on Snapchat, Facebook and offline. In: ACM CHI 2018, paper no. 607 (2018)Google Scholar
  39. 39.
    Wallace, B., Knoefel, F., Goubran, R., Porter, M.M., Smith, A., Marshall, S.: Features that distinguish drivers: big data analytics of naturalistic driving data. STBD 4(1), 20–32 (2017)CrossRefGoogle Scholar
  40. 40.
    Zeng, J., Min, J.: A systematic framework for designing IoT-enabled systems. STIOT 1(1), 23–31 (2017)CrossRefGoogle Scholar
  41. 41.
    Zhang, J., Jin, S., Yu, P.S.: Mutual community detection across multiple partially aligned social networks. STBD 3(2), 47–69 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of ManitobaManitobaCanada
  2. 2.University of TriesteTriesteItaly
  3. 3.ICAR-CNRRendeItaly
  4. 4.University of SalentoLecceItaly

Personalised recommendations