scholarly journals Peer Review #2 of "An approach for semantic integration of heterogeneous data sources (v0.1)"

2020 ◽  
Keyword(s):  
Peer Review ◽  
Heterogeneous Data ◽  
Semantic Integration ◽  
Data Sources ◽  
Heterogeneous Data Sources

scholarly journals An approach for semantic integration of heterogeneous data sources

2020 ◽  
Vol 6 ◽  
pp. e254
Author(s):  
Giuseppe Fusco ◽  
Lerina Aversano
Keyword(s):  
Data Integration ◽  
Heterogeneous Data ◽  
Semantic Integration ◽  
Data Sources ◽  
Complex Data ◽  
Semantic Heterogeneity ◽  
Heterogeneous Information ◽  
Heterogeneous Data Sources ◽  
Autonomous Data Sources ◽  
Unified View

Integrating data from multiple heterogeneous data sources entails dealing with data distributed among heterogeneous information sources, which can be structured, semi-structured or unstructured, and providing the user with a unified view of these data. Thus, in general, gathering information is challenging, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure is unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. In this paper, we present an approach for the semantic integration of heterogeneous data sources, DIF (Data Integration Framework), and a software prototype to support all aspects of a complex data integration process. The proposed approach is an ontology-based generalization of both Global-as-View and Local-as-View approaches. In particular, to overcome problems due to semantic heterogeneity and to support interoperability with external systems, ontologies are used as a conceptual schema to represent both data sources to be integrated and the global view.

Semantic Integration of Manufacturing Data Sources

2006 ◽  
Vol 532-533 ◽  
pp. 1156-1159
Author(s):  
Ming Wei Wang ◽  
Shu Sheng Zhang ◽  
Jing Tao Zhou ◽  
Han Zhao
Keyword(s):  
Business Processes ◽  
Heterogeneous Data ◽  
Semantic Integration ◽  
Data Sources ◽  
Access Methods ◽  
Semantic Level ◽  
Heterogeneous Data Sources ◽  
Data Semantics ◽  
Autonomous Data Sources ◽  
Manufacturing Data

In order to gain insight into business processes, multiple autonomous data sources residing in the manufacture enterprise need to integrate not only on storage and access methods but also capturing the meaning of data to get a coherent and meaningful data views for different applications requirements. This paper presents a semantic-based architecture for the integration of heterogeneous manufacturing data sources. The integration is realized on a semantic level by the explicit presentation of data semantics with ontology and relationships between ontologies. During applications usage, heterogeneous data sources which represent relations of relevance are dynamically organized in terms of their semantics. The paper discusses some major problems in the architecture: unified schema transformation, semi-automatic ontology generation and mediation.

Proposition of a New Ontology-Based P2P System for Semantic Integration of Heterogeneous Data Sources

2018 ◽  
pp. 240-270
Author(s):  
Naïma Souâd Ougouti ◽  
Hafida Belbachir ◽  
Youssef Amghar
Keyword(s):  
Heterogeneous Data ◽  
Semantic Integration ◽  
Data Sources ◽  
Similarity Function ◽  
Distributed Data ◽  
P2p Systems ◽  
Heterogeneous Data Sources ◽  
Data Integration System ◽  
P2p System ◽  
Super Peer

Semantic web offers new opportunities to multi-sources integration field, and many approaches like P2P systems are revisited taking into account the new requirements. In this chapter, the authors present their P2P heterogeneous and distributed data integration system. It is a super-peer system, where peers are regrouped by type of data (relational, image, text, etc.) around a super-peer which contains a domain ontology. Peers data sources are exported in a common format in the form of a semantically rich ontology. Schemas reconciliation is done by matching domain and local ontologies by the use of a similarity function whose contribution is based on the direct and indirect semantic neighborhood. Queries are described using ontologies, then routed towards relevant peers thanks to a semantic topology built on top of the existing physical one.

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