Skip to main content

A Framework for Extracting Reliable Information from Unstructured Uncertain Big Data

  • Conference paper
  • First Online:
Intelligent Decision Technologies 2016

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 57))

Abstract

Big Data is still in its initial stages and has prompted various basic issues and difficulties to rise, for example, the pace of exchange, information development, and assorted qualities of information and security issues. For example, overseeing and abusing immense measures of information make it more valuable and important has turned into a test driving basic learning for choice making and in picking up an understanding into the general circumstance. Huge information has gotten phenomenal consideration from open and private sectors and in addition from the educated community around the world. In advertising, enormous information is utilized to comprehend the practices and actives of clients. In the experimental fields, huge information can be misused by aiding and taking care of the issues confronting the investigative fields extending from nanotechnology to climatology to geophysics. In the field of law requirement, social administrations and country security, enormous information has exhibited its handiness for government organizations to bolster in their choice making.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Similar content being viewed by others

References

  1. Abawajy, J.: Comprehensive analysis of big data variety landscape. Int. J. Parallel Emergent Distrib. Syst. 30(1), 5–14 (2015)

    Article  MathSciNet  Google Scholar 

  2. Abdulhafiz, W.A., Khamis, A.: Handling data uncertainty and inconsistency using multi sensor data fusion. Adv. Artif. Intell. 11 (2013)

    Google Scholar 

  3. Aggarwal, C.C., Yu, P.S.: A survey of uncertain data algorithms and applications. IEEE Trans. Knowl. Data Eng. 21(5), 609–623 (2009)

    Google Scholar 

  4. AllegroGraph. AllegroGraph (2015). http://franz.com/agraph/allegrograph/

  5. Angelosante, D., Biglieri, E., Lops, M.: Multiuser detection in a dynamic environment: joint user identification and parameter estimation. In: IEEE International Symposium on Information Theory, 2007, ISIT (2007)

    Google Scholar 

  6. Bai, Y., Zhuang, H., Wang, D.: Advanced Fuzzy Logic Technologies in Industrial Applications. Springer (2007)

    Google Scholar 

  7. Begoli, E., Horey, J.: Design principles for effective knowledge discovery from big data. In: Joint Working IEEE/IFIP Conference on Software Architecture (WICSA) and European Conference on Software Architecture (ECSA) (2012)

    Google Scholar 

  8. Brynjolfsson, E., Hitt, L.M., Kim, H.H.: Strength in numbers: how does data-driven decisionmaking affect firm performance? (2011). http://www.SSRN1819486papers.ssrn.com

  9. Camacho, J., Macia-Fernandez, G., Diaz-Verdejo, J., Garcia-Teodoro, P.: Tackling the Big Data 4 vs for anomaly detection. In: IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (2014)

    Google Scholar 

  10. Chan, J.O.: An architecture for big data analytics. Commun. IIMA 13(2), 1 (2014)

    Article  Google Scholar 

  11. Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Gruber, R.E.: Bigtable: a distributed storage system for structured data. ACM Trans. Comput. Syst. (TOCS) 26(2), 4

    Google Scholar 

  12. Chau, M., Cheng, R., Kao, B., Ng, J.: Uncertain data mining: an example in clustering location data. In: Advances in Knowledge Discovery and Data Mining, pp. 199–204. Springer, Berlin (2006)

    Google Scholar 

  13. Chen, C.P., Zhang, C.-Y.: Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf. Sci. 275, 314–347 (2014)

    Article  Google Scholar 

  14. Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mob. Netw. Appl. 19(2), 171–209 (2014)

    Article  Google Scholar 

  15. Cheung, C.F., Lee, W., Wang, Y.: A multi-facet taxonomy system with applications in unstructured knowledge management. J. Knowl. Manag. 9(6), 76–91 (2005)

    Article  Google Scholar 

  16. Chowdhury, M., Stoica, I.: Coflow: a networking abstraction for cluster applications. In: Proceedings of the 11th ACM Workshop on Hot Topics in Networks, pp. 31–36 (2012)

    Google Scholar 

  17. Chu, E., Baid, A., Chen, T., Doan, A. Naughton, J.: A relational approach to incrementally extracting and querying structure in unstructured data. In: Proceedings of the 33rd International Conference on Very Large Data Bases (2007)

    Google Scholar 

  18. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  19. Dijcks, J.P.: Oracle: big data for the enterprise. Oracle White Paper (2012)

    Google Scholar 

  20. Ding, H., Mao, J., Wei, K., Yang, L.: Fuzzy modeling with unstructured data uncertainty. In: International Conference on Control and Automation, ICCA’05 (2005)

    Google Scholar 

  21. Ding, X., Jin, H., Xu, H., Song, W.: Probabilistic skyline queries over uncertain moving objects. Comput. Inform. 32(5), 987–1012 (2014)

    Google Scholar 

  22. Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets*. Int. J. Gen. Syst. 17(2–3), 191–209 (1990)

    Google Scholar 

  23. Durrant-Whyte, H., Henderson, T.C.: Multisensor Data Fusion Springer Handbook of Robotics, pp. 585–610. Springer (2008)

    Google Scholar 

  24. Easton, J.: Carrying out a big data Readiness Assessment (2014)

    Google Scholar 

  25. Eswari, T., Sampath, P., Lavanya, S.: Predictive methodology for diabetic data analysis in Big Data. Proc. Comput. Sci. 50, 203–208 (2015)

    Article  Google Scholar 

  26. Feng, L., Li, T., Ruan, D., Gou, S.: A vague-rough set approach for uncertain knowledge acquisition. Knowl.-Based Syst. 24(6), 837–843 (2011)

    Article  Google Scholar 

  27. Florea, M.C., Jousselme, A.-L. Bossé, É.: Fusion of imperfect information in the unified framework of random sets theory: application to target identification: DTIC Document (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sanjay Kumar Singh .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Singh, S.K., Mani, N., Singh, B. (2016). A Framework for Extracting Reliable Information from Unstructured Uncertain Big Data. In: Czarnowski, I., Caballero, A.M., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies 2016. Smart Innovation, Systems and Technologies, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-39627-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-39627-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39626-2

  • Online ISBN: 978-3-319-39627-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics