BUILDING A VIRTUAL VIEW OF HETEROGENEOUS DATA SOURCE VIEWS

Lerina Aversano, Roberto Intonti, Clelio Quattrocchi, Maria Tortorella

Abstract

In order to make possible the analysis of data stored in heterogeneous data sources, it could be necessary a preliminary building of an aggregated view of these sources, also referred as virtual view. The problem is that the data sources can use different technologies and represent the same information in different ways. The use of a virtual view allows the unified access to heterogeneous data sources without knowing details regarding each single source. This paper proposes an approach for creating a virtual view of the views of the heterogeneous data sources. The approach provides features for the automatic schema matching and schema merging. It exploits both syntax-based and semantic-based techniques for performing the matching; it also considers both semantic and contextual features of the concepts. The usefulness of the approach is validated through a case study.

References

  1. Lee, L., and Ling, W., 2003. A Methodology for Structural Conflict Resolution in the Integration of EntityRelationship Schemas. Knowledge and Information Systems, Vol.5, No. 2, Springer-Verlag London Ltd.
  2. Denivaldo, L., Hammoudi, S., de Souza, J, and Bontempo, A., 2006. Metamodel Matching: Experiments and Comparison. Proceedings of the International Conference on Software Engineering Advances (ICSEA'06), IEEE Computer Society, 2006.
  3. Rahm, E., and Bernstein, P.A., 2001. A survey of approaches to automatic schema matching. The International Journal on Very Large Data Bases. Springer-Verlag.
  4. Jayant, M., Bernstein, P. A., and Rahm, E., 2001. Generic Schema Matching with Cupid, International Conference on Very Large Data Base, Morgan Kaufmann Publishers.
  5. Cohen, W. W., Ravikumar, P., and Fienberg, S. E., 2003. A Comparison of String Distance Metrics for NameMatching Tasks, Workshop on Information Integration on the Web, American Association for Artificial Intelligence.
  6. Giunchiglia, F., and Shvaiko Pavel, 2003. Semantic Matching. The Knowledge Engineering Review journal.
  7. Giunchiglia, F., and Yatskevich, M., 2004. Element Level Semantic Matching, Meaning Coordination and Negotiation workshop.
  8. Giunchiglia, F., Shvaiko, P., and Yatskevich M., 2004. SMatch: an Algorithm and an Implementation of Semantic Matching. European Semantic Web Symposium. Lecture Notes in Computer Science.
  9. AnHai,D., Madhavan, J., Domingos, P., Halevy, A., 2003. Ontology Matching: A Machine Learning Approach. Handbook on Ontologies in Information Systems
  10. Huang J., Laura, R., Gutiérrez, Z., Mendoza García, B., Huhns M. N., 2005. A Schema-Based Approach Combined with Inter-Ontology Reasoning to Construct Consensus Ontologies. AAAI Workshop on Contexts and Ontologies: Theory, Practice and Applications. American Association for Artificial Intelligence.
  11. Fridman Noy, N., and Musen, M. A, 2000. Algorithm and Tool for Automated Ontology Merging and Alignment. American Association for Artificial Intelligence.
  12. Ursino, D., 2003. Extraction and Exploitation of Intentional Knowledge from Heterogeneous Information Sources. Springer Verlag.
  13. Bergamaschi, S., 1997. Un Approccio Intelligente all'Integrazione di Sorgenti Eterogenee di Informazione.
  14. Fong, J., Pang, F., Wong, D., and Fong, A., 2006. Schema Integration For Object-Relational Databases With Data Verification.
  15. Chiticariu, L., Kolaitis, P. G. and Popa, L., 2008. Interactive Generation of Integrated Schemas. SIGMOD'08. ACM Press.
  16. Hyunjang, K., Myunggwon, H., and Pankoo, K., 2005. A New Methodology for Merging the Heterogeneous Domain Ontologies based on the WordNet. International Conference on Next Generation Web Services Practices. IEEE Computer Society.
  17. Pedersen, T., and Patwardhan, S., 2004. WordNet: Similarity - Measuring the Relatedness of Concepts.
  18. Pattwardhan, S., Banerjee, S., and Pedersen, T., 2003. Using Measures of Semantic Relatedness for Word Sense Disambiguation.
  19. Tan, P.N., Steinbach, M., and Kumar, V., 2005. Introduction to Data Mining. ISBN 0-321-32136-7.
Download


Paper Citation


in Harvard Style

Aversano L., Intonti R., Quattrocchi C. and Tortorella M. (2010). BUILDING A VIRTUAL VIEW OF HETEROGENEOUS DATA SOURCE VIEWS . In Proceedings of the 5th International Conference on Software and Data Technologies - Volume 1: ICSOFT, ISBN 978-989-8425-22-5, pages 266-275. DOI: 10.5220/0003011702660275


in Bibtex Style

@conference{icsoft10,
author={Lerina Aversano and Roberto Intonti and Clelio Quattrocchi and Maria Tortorella},
title={BUILDING A VIRTUAL VIEW OF HETEROGENEOUS DATA SOURCE VIEWS},
booktitle={Proceedings of the 5th International Conference on Software and Data Technologies - Volume 1: ICSOFT,},
year={2010},
pages={266-275},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003011702660275},
isbn={978-989-8425-22-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Software and Data Technologies - Volume 1: ICSOFT,
TI - BUILDING A VIRTUAL VIEW OF HETEROGENEOUS DATA SOURCE VIEWS
SN - 978-989-8425-22-5
AU - Aversano L.
AU - Intonti R.
AU - Quattrocchi C.
AU - Tortorella M.
PY - 2010
SP - 266
EP - 275
DO - 10.5220/0003011702660275