| Peer-Reviewed

Ontology Based Fuzzy Query Execution

Received: 22 December 2014     Accepted: 25 December 2014     Published: 28 January 2015
Views:       Downloads:
Abstract

Database engineering has been progressed up to the Relational database stage. Fuzzy information administration in databases is a complex process in view of adaptable information nature and heterogeneous database frameworks. Relational Database Management System (RDBMS) can just handle fresh information but cannot handle precise data information. Structured Query Language (SQL) is a very powerful tool but can handle data which is crisp and precise in nature. It is not able to fulfill the requirements for information which is indeterminate, uncertain, inapplicable and imprecise and vague in nature. The goal of this work is to use Fuzzy technique in RDBMS. But, Fuzzy Relational Database Management System (FRDB) requires complex data structures, in most cases, are dependent on the platform in which they are implemented. A solution that involves representing an FRDB using an Ontology as an interface has been defined to overcome this problem. A new Fuzzy Query Ontology is proposed in this dissertation with implementation. The implementation layer, which is responsible for parsing and translating user requests into the corresponding DB implementations in transparent, is required to establish communication between the Ontology and the relational databases management system (RDBMS). This ontology defines a framework for storing fuzzy data by defining those using classes, slots, and instances. An Ontology is an explicit and formal specification of a conceptualization. Ontologies provides a shared understanding of a domain which allows interoperability between semantics.

Published in American Journal of Networks and Communications (Volume 4, Issue 3-1)

This article belongs to the Special Issue Ad Hoc Networks

DOI 10.11648/j.ajnc.s.2015040301.14
Page(s) 16-21
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Fuzzy, Ontology, Fuzzy Data, Metadata, DBMS Catalog, Fuzzy Knowledge Representation, Ontology, Databases, Database Modeling

References
[1] Rumbaugh, J., Blaha, M., Premerlani, W., Eddy, F. and Lorensen, W. (1991). Object-oriented modeling and design. Englewood Cliffs, New Jersey: Prentice Hall.
[2] Carmen Martinez-Cruz.. “An Ontology to represent Queries in Fuzzy Relational Databases”. IEEE 2011
[3] J. Galindo, A. Urrutia, and M. Piattini, “Fuzzy Database Modeling, Design and Implementation”. Idea Group Publishing, 2006
[4] “Describing Fuzzy Database DB Schemas as Ontologies: A System Architecture View”. Springer 2010. 13 th International Conference, IPMU july 2010.
[5] Sunita M. Mahajan, Vaishali P. Jadhav,”Analysis of Execution Plans in query optimization”, International Journal of Scientific & Engineering Research, Volume 3, Issue 2, February-2012 , ISSN 2229-5518.
[6] Protégé, tool for creating and editing ontologies. http://protege.stanford.edu/, 2011.
[7] M. Neunerdt, B. Trevisan, T. C. Teixeira, R. Mathar, and E.- M. Jakobs, “Ontology-based corpus generation for web comment analysis,” in ACM conference on Hypertext and hypermedia (HT 2011), (Eindhoven), 05 2011.
[8] Michaela Kreutzová, Jaroslav Porubän, “Automating User Actions on GUI: Defining a GUI Domain-Specific Language”. In: CSE 2010: proceedings of International Scientific conference on Computer Science and Engineering: Stará Ľubovňa, Slovakia, 2010 pp. 60-67.
[9] Java Look & feel design guidelines, http://java.sun.com/products/jlf/ed2/book/, 2001
[10] J. Cheng, Z. M. Ma, and L. Yan, “f-SPARQL: a flexible extension of SPARQL,” in Proceedings of the 21st international Conference on Database and expert systems applications: Part I, ser. DEXA’10. Berlin, Heidelberg: Springer-Verlag, 2010, pp. 487–494.
Cite This Article
  • APA Style

    Geetanjali tyagi, kumar kaushik, Arnika Jain, Manish Bhardwaj. (2015). Ontology Based Fuzzy Query Execution. American Journal of Networks and Communications, 4(3-1), 16-21. https://doi.org/10.11648/j.ajnc.s.2015040301.14

    Copy | Download

    ACS Style

    Geetanjali tyagi; kumar kaushik; Arnika Jain; Manish Bhardwaj. Ontology Based Fuzzy Query Execution. Am. J. Netw. Commun. 2015, 4(3-1), 16-21. doi: 10.11648/j.ajnc.s.2015040301.14

    Copy | Download

    AMA Style

    Geetanjali tyagi, kumar kaushik, Arnika Jain, Manish Bhardwaj. Ontology Based Fuzzy Query Execution. Am J Netw Commun. 2015;4(3-1):16-21. doi: 10.11648/j.ajnc.s.2015040301.14

    Copy | Download

  • @article{10.11648/j.ajnc.s.2015040301.14,
      author = {Geetanjali tyagi and kumar kaushik and Arnika Jain and Manish Bhardwaj},
      title = {Ontology Based Fuzzy Query Execution},
      journal = {American Journal of Networks and Communications},
      volume = {4},
      number = {3-1},
      pages = {16-21},
      doi = {10.11648/j.ajnc.s.2015040301.14},
      url = {https://doi.org/10.11648/j.ajnc.s.2015040301.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.s.2015040301.14},
      abstract = {Database engineering has been progressed up to the Relational database stage. Fuzzy information administration in databases is a complex process in view of adaptable information nature and heterogeneous database frameworks. Relational Database Management System (RDBMS) can just handle fresh information but cannot handle precise data information. Structured Query Language (SQL) is a very powerful tool but can handle data which is crisp and precise in nature. It is not able to fulfill the requirements for information which is indeterminate, uncertain, inapplicable and imprecise and vague in nature. The goal of this work is to use Fuzzy technique in RDBMS. But, Fuzzy Relational Database Management System (FRDB) requires complex data structures, in most cases, are dependent on the platform in which they are implemented. A solution that involves representing an FRDB using an Ontology as an interface has been defined to overcome this problem. A new Fuzzy Query Ontology is proposed in this dissertation with implementation. The implementation layer, which is responsible for parsing and translating user requests into the corresponding DB implementations in transparent, is required to establish communication between the Ontology and the relational databases management system (RDBMS). This ontology defines a framework for storing fuzzy data by defining those using classes, slots, and instances. An Ontology is an explicit and formal specification of a conceptualization. Ontologies provides a shared understanding of a domain which allows interoperability between semantics.},
     year = {2015}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Ontology Based Fuzzy Query Execution
    AU  - Geetanjali tyagi
    AU  - kumar kaushik
    AU  - Arnika Jain
    AU  - Manish Bhardwaj
    Y1  - 2015/01/28
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ajnc.s.2015040301.14
    DO  - 10.11648/j.ajnc.s.2015040301.14
    T2  - American Journal of Networks and Communications
    JF  - American Journal of Networks and Communications
    JO  - American Journal of Networks and Communications
    SP  - 16
    EP  - 21
    PB  - Science Publishing Group
    SN  - 2326-8964
    UR  - https://doi.org/10.11648/j.ajnc.s.2015040301.14
    AB  - Database engineering has been progressed up to the Relational database stage. Fuzzy information administration in databases is a complex process in view of adaptable information nature and heterogeneous database frameworks. Relational Database Management System (RDBMS) can just handle fresh information but cannot handle precise data information. Structured Query Language (SQL) is a very powerful tool but can handle data which is crisp and precise in nature. It is not able to fulfill the requirements for information which is indeterminate, uncertain, inapplicable and imprecise and vague in nature. The goal of this work is to use Fuzzy technique in RDBMS. But, Fuzzy Relational Database Management System (FRDB) requires complex data structures, in most cases, are dependent on the platform in which they are implemented. A solution that involves representing an FRDB using an Ontology as an interface has been defined to overcome this problem. A new Fuzzy Query Ontology is proposed in this dissertation with implementation. The implementation layer, which is responsible for parsing and translating user requests into the corresponding DB implementations in transparent, is required to establish communication between the Ontology and the relational databases management system (RDBMS). This ontology defines a framework for storing fuzzy data by defining those using classes, slots, and instances. An Ontology is an explicit and formal specification of a conceptualization. Ontologies provides a shared understanding of a domain which allows interoperability between semantics.
    VL  - 4
    IS  - 3-1
    ER  - 

    Copy | Download

Author Information
  • Department of Computer science and Engineering, SRM University, NCR Campus, Modinagar, Ghaziabad, India

  • Department of Computer science and Engineering, SRM University, NCR Campus, Modinagar, Ghaziabad, India

  • Department of Computer science and Engineering, SRM University, NCR Campus, Modinagar, Ghaziabad, India

  • Department of Computer science and Engineering, SRM University, NCR Campus, Modinagar, Ghaziabad, India

  • Sections