A knowledge-based stream processing using big data analytics

  • Authors

    • D Tamilselvi
    • Dr A. Akila
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.14170
  • Subscribe Services, Interoperability Schema Matching Semantic, Big Data Analytics Ontologies

  • Big Data available in almost all departments of every organization spread throughout the globe in a huge volume and category. The issues such as data heterogeneity and advanced processing capabilities are provided solution with the proposed system. Data heterogeneity is tack-led by using the automatic schema mapping in the proposed work which is knowledge based solution. Inventive processing is achieved using ontology extraction and semantic inference in the proposed work. The solution is evaluated in terms of its performance and effective-ness with the publish/subscribe paradigm. The state of art analysis of huge volume of variety of data and sensory information is the real complexity. The Advanced Message Queuing Protocol is used in the proposed work for the state of art substance explanation of flooding IoT data to have dynamic mingling. The proposed work gives way to produce huge amount of data that can affect the working of the smart city systems that uses IoT data. To point out the reliability and summarization of the data, information model is used in the proposed work. The data size and average exchanged message time are the measures used to examine the working of the framework. A detailed assessment of the various sensors is carried out to inspect the storage data volume and computational cost for the substance explanation of the framework.

     

     

  • References

    1. [1] K. Teymourian, O. Streibel, A. Paschke, R. Alnemr, C. Meinel, Towards semantic event-driven systems, in: Proceedings of the 3rd International Conference on New Technologies, Mobility and Security (NTMS), 2009, pp. 1–6.

      [2] F. Heintz, J. Kvarnström, P. Doherty, and Stream reasoning in DyKnow: a knowledge processing middleware system, in: Proceedings of the Stream Reasoning Workshop. In series: CEUR Workshop Proceedings.

      [3] K. Teymourian, O. Streibel, A. Paschke, R. Alnemr, C. Meinel, Towards semantic event-driven systems, in: Proceedings of the 3rd International Conference on New Technologies, Mobility and Security (NTMS), 2009, pp. 1–6.

      [4] D. Brickley, R.V. Guha, RDF Vocabulary Description Language 1.0: RDF Schema. <http://www.w3.org/TR/rdf-schema/> (accessed July 2013).

      [5] E. Prud’hommeaux, A. Seaborne, SPARQL Query Language for RDF. http:// www.w3.org/TR/rdf-sparql-query/ (accessed July 2013).

      [6] D. Crockford, the Application/json Media Type for JavaScript Object Notation (JSON), RFC4627. <http://www.json.org/index.html> (accessed September 2013).

      [7] O. Ben-Kiki, C. Evans, YAML Ain’t Markup Language. <http://www.yaml.org> (accessed September 2010). Corsaro, OMG RFP mars/2008-05-03. <http://www.omg.org/cgi-bin/ doc?mars/2008-05-03> (accessed September 2013) (restricted access).

      [8] OMG, Extensible and Dynamic Topic Types for DDS. <http://portals.omg.org/ dds/sites/default/files/x-types_ptc_11-03-11.pdf> (accessed September 2013).

      [9] D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic, and EP-SPARQL: A unified language for event processing and stream reasoning, in: Proceedings of the 20th International Conference on World Wide Web, WWW ’11, Hyderabad, India, ACM, New York, NY, USA, 2011, pp. 635–644.

      [10] J. Aasman, Unification of geospatial reasoning, temporal logic, & social network analysis in event-based systems, in: Proceedings of the Second International Conference on Distributed Event-Based Systems (DEBS), 2008, pp. 139–145.

      [11] S. Abdallah, Y. Raimond, the Event Ontology. <http:// www.motools.sourceforge.net/event/event.html> (accessed July 2013).

      [12] A. Llaves, H. Michels, P. Maué, M. Roth, Semantic event processing in ENVISION, in: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics (WIMS), 2012, pp. 25:1–25:9.

      [13] S. Sahoo et al., A Survey of Current Approaches for Mapping of Relational Databases to RDF. <http://www.w3.org/2005/Incubator/rdb2rdf/ RDB2RDF_SurveyReport_01082009.pdf> (accessed July 2013).

      [14] P. T. Thuy, Y.-K. Lee, S. Lee, and XSD2RDFS and XML2RDF transformation: a semantic approach, in: Proceedings of the second International Conference on Emerging Databases (EDB).

      [15] D. McGuinness, F. van Harmelen, OWL Web Ontology Language – Overview. <http://www.w3.org/TR/owl-features/> (accessed July 2013).

      [16] F. Giunchiglia, P. Shvaiko, M. Yatskevich, and S-Match: an algorithm and an implementation of semantic matching, Semantic Web: Res. Appl. Lect. Notes Comput. Sci. 3053 (2004) 61–75.

      [17] P. Bouquet, L. Serafini, S. Zanobini, Semantic coordination: a new approach and an application, in: The Semantic Web – ISWC 2003, Lecture Notes in Computer Science, vol. 2870, 2003, pp. 130–145.

      [18] Gartner, Hype Cycles 2012. <http://www.gartner.com/technology/research/ hype-cycles/> (accessed July 2013).

      [19] D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic, and EP-SPARQL: A unified language for event processing and stream reasoning, in: Proceedings of the 20th International Conference on World Wide Web, WWW ’11, Hyderabad, India, ACM, New York, NY, USA, 2011, pp. 635–644.

      [20] D.J. Abadi, D. Carney, U. Çetintemel, M. Cherniack, C. Convey, S. Lee, M. Stonebraker, N. Tatbul, S. Zdonik, Aurora: A new model and architecture for data stream management, VLDB J. 12 (2) (2003) 120–139.

  • Downloads

  • How to Cite

    Tamilselvi, D., & A. Akila, D. (2018). A knowledge-based stream processing using big data analytics. International Journal of Engineering & Technology, 7(2.33), 287-289. https://doi.org/10.14419/ijet.v7i2.33.14170