Significant Big Data Interpretation using Map Reduce Paradigm

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Lavanya Kakkirala, K .Venkateswara Rao

Lavanya Kakkirala and .Venkateswara K Rao. Significant Big Data Interpretation using Map Reduce Paradigm. International Journal of Computer Applications 156(1):7-11, December 2016. BibTeX

	author = {Lavanya Kakkirala and K .Venkateswara Rao},
	title = {Significant Big Data Interpretation using Map Reduce Paradigm},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {156},
	number = {1},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {7-11},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2016912339},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The development of ontologies involves continuous but relatively small modifications. Even after a number of changes, ontology and its previous versions usually share most of their axioms. For large and complex ontologies this may require a few minutes, or even a few hours. Cognitive on a Web scale becomes increasingly stimulating because of the large volume of data involved and the complexity of the task. Full re-reasoning over the entire dataset at every update is too time-consuming to be practical. Semantic information has been reduced by using Hadoop framework with simple machine learning algorithm. Each level of mapping and reducing is based on k-means clustering technique. Large set of information can be constructing or modified with the help of simple pattern based grouping. Dynamically grouping dependencies can be made based on attributes. Clustered values have got modifications like addition. At the end user query has been retrieved with the help of grouped items. The system has been assessed on the BTC benchmark and the results show that this method outperforms related ones in nearly all aspects.


  1. J. Urbani, S. Kotoulas, E. Oren, and F. Harmelen, “Scalable distributed reasoning using MapReduce,” in Proc. 8th Int. Semantic Web Conf., Chantilly, VA, USA, Oct. 2009, pp. 634–649.
  2. J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” Commun. ACM, vol. 51, no. 1, pp. 107–113, 2008.
  3. C.Anagnostopoulos and S.Hadjiefthymiades, “Advanced inference in situation-aware computing,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 39, no. 5, pp. 1108–1115, Sep. 2009.
  4. H. Paulheim and C. Bizer, “Type inference on noisy RDF data,” in Proc. ISWC, Sydney, NSW, Australia, 2013, pp. 510–525.
  5. G. Antoniou and A. Bikakis, “DR-Prolog: A system for defeasible reasoning with rules and ontologies on the Semantic Web,” IEEE Trans. Knowl. Data Eng., vol. 19, no. 2, pp. 233–245, Feb. 2007.
  6. V. Milea, F. Frasincar, and U. Kaymak, “tOWL: A temporal web ontol-ogy language,”IEEE Trans. Syst., Man, Cybern. B, Cybern. vol. 42, no. 1, pp. 268–281, Feb. 2012.
  7. D. Lopez, J. M. Sempere, and P. García, “Inference of reversible tree languages,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 4, pp. 1658–1665, Aug. 2004.
  8. A. Schlicht and H. Stuckenschmidt, “MapResolve,” in Proc. 5th Int. Conf. RR, Galway, Ireland, Aug. 2011, pp. 294–299.
  9. B. C. Grau, C. Halaschek-Wiener, and Y. Kazakov, “History matters: Incremental ontology reasoning using modules,” in Proc. ISWC/ASWC, Busan, Korea, 2007, pp. 183–196.
  10. RDF Semantics [Online]. Available: [21] RDF Schema[Online]. Available:
  11. SPARQL 1.1 Overview[Online]. Available:
  12. Hadoop [Online]. Available:
  13. HBase [Online]. Available:
  14. Billion Triples Challenge 2012 Dataset [Online]. Available:
  15. Y. Guo, Z. Pan, and J. Heflin, “LUBM: A benchmark for OWL knowl-edge base systems,”J. Web Semantics, vol. 3, nos. 2–3, pp. 158–182, Oct. 2005.
  16. Bio2RDF [Online]. Available:


Ontology, Hadoop, Semantic, Cognitive, Pattern, machine learning.