Authors:
Silvia Borowicc
1
and
Solange Alves-Souza
2
Affiliations:
1
School of Arts, Sciences and Humanities, Universidade de São Paulo, Arlindo Bettio 1000, São Paulo, Brazil
;
2
Polytechnic School, Universidade de São Paulo, São Paulo, Brazil
Keyword(s):
Data Integration, Heterogeneous Data, Ontology.
Abstract:
Data have been collected by communities for analysis, visualization, predictions and other activities to support data-driven decision. Obtaining value from data assets directly depends on the data integration task. However, Big Data poses new challenges to integration due to data heterogeneity. It is essential to understand the main problems and to know technologies and techniques that have been employed to improve the ability to obtain value by heterogeneous data integration. This paper presents a literature scope review that highlights the main techniques applied to heterogeneous data integration. The literature reviewed presents solutions mostly focusing on a specific purpose or part of the integration process instead of a clear understanding of how the techniques can be used in a complete integration process. Therefore, this work shows a whole picture of a data integration process organizing the techniques according to their functionalities and presents a workflow with tasks asso
ciated to techniques and resources, focusing on semantic mediation, such as mapping and matching tasks. Ontologies and semantic web technologies are promising to address data heterogeneity and have been used in the semantic enrichment of data and semantic mediation between data sources and global model. However, some aspects remain to be further investigated, such as ontology and terminology construction, data processing scalability and semantic mediation, especially for mapping definition.
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