Data integration: A logic-based perspective (original) (raw)

An Ontology Approach to Data Integration

Journal of Computer Science and Technology - JCST

The term "Federated Databases" refers to the data integration of distributed, autonomous and heterogeneous databases. However, a federation can also include information systems, not only databases. At integrating data, several issues must be addressed. Here, we focus on the problem of heterogeneity, more specifically on semantic heterogeneity - that is, problems rela ted to semantically equivalent concepts or semantically related/unrelated concepts. In order to address this problem, we apply the idea of ontologies as a tool for data integration. In this paper, we explain this concept and we briefly describe a method for constructing an ontology by using a hybrid ontology approach.

Ontology-based Data Integration

2019

The data integration concept formalization issues have been considered within an XML-oriented data model. An ontology for data integration concept is proposed. Three kinds mechanisms are used to formalize the data integration concept: content dictionary, signature file and reasoning file (collections of reasoning rules). The reasoning rules are based on an algebra of integrable data and formalized by an XML DTD. The data translation mechanisms are non-sensitive to extension of the considered algebra. It is important that the considered data model is extensible and we use a computationally complete language to support the data integration concept.

Conceptual modeling for data integration

2009

The goal of data integration is to provide a uniform access to a set of heterogeneous data sources, freeing the user from the knowledge about where the data are, how they are stored, and how they can be accessed. One of the outcomes of the research work carried out on data integration in the last years is a clear architecture, comprising a global schema, the source schema, and the mapping between the source and the global schema.

Ontology based framework for data integration

WSEAS Transactions on …, 2008

One of the most complex issues of the data integration is the case where there are multiple sources for the same data element. It is not easy to generate and maintain the integrated scheme. In this paper we describe a framework which encompasses the entire data integration process. The data source schemas as well as the integrated schema are expressed using an extension of an ontology definition language which allows the incorporation of metadata to support the integration process. The proposed model allows the user to concentrate in modeling the problem itself and not in the issues of dealing with the temporal and the spatial aspects concerning to many of the data sources usually used in the enterprises information systems.

Ontology-based integration of data sources

2007 10th International Conference on Information Fusion, 2007

Many applications, e.g., data/information fusion, data mining, and decision aids, need to access multiple heterogeneous data sources. These data sources may come from internal and external databases.

On an Approach to Data Integration: Concept, Formal Foundations and Data Model

2017

In the frame of an extensible canonical data model a formalization of data integration concept is proposed. We provide virtual and materialized integration of data as well as the possibility to support data cubes with hierarchical dimensions. The considered approach of formalization of data integration concept is based on the so-called content dictionaries. Namely, by means of these dictionaries we are formally defining basic concepts of database theory, metadata about these concepts, and the data integration concept. A computationally complete language is used to extract data from several sources, to create the materialized view, and to effectively organize queries on the multidimensional data. In memory of Garush Manukyan, my father. This work was supported by the RA MES State Committee of Science, in the frames of the research project N 15T-18350.

Data integration needs reasoning

2001

Data integration is the problem of combining the data residing at different sources, and providing a unified view of these data, called global schema, which can be queried by the user. The interest in this kind of systems has been continuously growing in the last years. However, the design of a data integration system is a very complex task, and several issues remains open, including how to express the relation between the global schema and the sources, and how to process queries expressed on the global schema.

Data Integration through DL-LiteA Ontologies

2008

The goal of data integration is to provide a uniform access to a set of heterogeneous data sources, freeing the user from the knowledge about where the data are, how they are stored, and how they can be accessed. One of the outcomes of the research work carried out on data integration in the last years is a clear conceptual architecture, comprising a global schema, the source schema, and the mapping between the source and the global schema. In this paper, we present a comprehensive approach to, and a complete system for, ontology-based data integration. In this system, the global schema is expressed in terms of a TBox of the tractable Description Logics textitDL−LitemathcalA{\textit{DL-Lite}_{\mathcal A}}textitDL−LitemathcalA , the sources are relations, and the mapping language allows for expressing GAV sound mappings between the sources and the global schema. The mapping language has specific mechanisms for addressing the so-called impedance mismatch problem, arising from the fact that, while the data sources store values, the instances of concepts in the ontology are objects. By virtue of the careful design of the various languages used in our system, answering unions of conjunctive queries can be done through a very efficient technique (LogSpace with respect to data complexity) which reduces this task to standard SQL query evaluation. We also show that even very slight extensions of the expressive abilities of our system lead beyond this complexity bound.

The Role of Ontologies in Data Integration

2000

In this paper, we discuss the use of ontologies for data integra- tion. We consider two different settings depending on the system architecture: central and peer-to-peer data integration. Within those settings, we discuss five different cases studies that illustrate the use of ontologies in metadata representation, in global conceptualization, in high-level querying, in declarative mediation, and in mapping support. Each