Design and Implementation of Oilfield Heterogeneous Data Integration Model Based on Ontology

2014 ◽  
Vol 912-914 ◽  
pp. 1201-1204
Author(s):  
Gang Huang ◽  
Xiu Ying Wu ◽  
Man Yuan
Keyword(s):  
Data Integration ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Semantic Heterogeneity ◽  
Integration Model ◽  
Integration Framework ◽  
Heterogeneous Data Integration ◽  
Semantic Level ◽  
Heterogeneous Data Sources ◽  
Semantic Difference

This paper provides an ontology-based distributed heterogeneous data integration framework (ODHDIF). The framework resolves the problem of semantic interoperability between heterogeneous data sources in semantic level. By metadatas specifying the distributed, heterogeneous data and by describing semantic information of data source , having "ontology" as a common semantic model, semantic match is established through ontology mapping between heterogeneous data sources and semantic difference institutions are shielded, so that semantic heterogeneity problem of the heterogeneous data sources can be effectively solved. It provides an effective technology measure for the interior information of enterprises to be shared in time accurately.

Research and Application on Oilfield Product Heterogeneous Data Integration Based on Ontology

2014 ◽  
Vol 530-531 ◽  
pp. 809-812
Author(s):  
Gang Huang ◽  
Xiu Ying Wu ◽  
Man Yuan ◽  
Rui Fang Li
Keyword(s):  
Data Integration ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Distributed Data ◽  
Semantic Heterogeneity ◽  
Gas Industry ◽  
Heterogeneous Data Integration ◽  
Semantic Level ◽  
Heterogeneous Data Sources ◽  
Integrated Operations

The Oil & Gas industry is moving forward with Integrated Operations (IO). There are different ways to achieve data integration, and ontology-based approaches have drawn much attention. This paper introduces an ontology-based distributed data integration framework (ODDIF). The framework resolves the problem of semantic interoperability between heterogeneous data sources in semantic level. By metadatas specifying the distributed, heterogeneous data and by describing semantic information of data source , having "ontology" as a common semantic model, semantic match is established through ontology mapping between heterogeneous data sources and semantic difference institutions are shielded, so that semantic heterogeneity problem of the heterogeneous data sources can be effectively solved. The proposed method reduces developing difficulty, improves developing efficiency, and enhances the maintainability and expandability of the system.

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.

scholarly journals Integração, Relacionamento e Representação de Dados em Cidades Inteligentes: Uma Revisão de Literatura

2018 ◽  
Author(s):  
Larysse Silva ◽  
José Alex Lima ◽  
Nélio Cacho ◽  
Eiji Adachi ◽  
Frederico Lopes ◽  
...  
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Data Integration ◽  
Smart Cities ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Application Development ◽  
Continuous Integration ◽  
Heterogeneous Data Sources ◽  
Computational Systems

A notable characteristic of smart cities is the increase in the amount of available data generated by several devices and computational systems, thus augmenting the challenges related to the development of software that involves the integration of larges volumes of data. In this context, this paper presents a literature review aimed to identify the main strategies used in the development of solutions for data integration, relationship, and representation in smart cities. This study systematically selected and analyzed eleven studies published from 2015 to 2017. The achieved results reveal gaps regarding solutions for the continuous integration of heterogeneous data sources towards supporting application development and decision-making.

scholarly journals Enabling semantic queries across federated bioinformatics databases

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Ana Claudia Sima ◽  
Tarcisio Mendes de Farias ◽  
Erich Zbinden ◽  
Maria Anisimova ◽  
Manuel Gil ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Integration ◽  
Heterogeneous Data ◽  
Biological Data ◽  
Data Sources ◽  
Biological Knowledge ◽  
Biological Databases ◽  
Semantic Level ◽  
Sparql Endpoint ◽  
Description Framework

Abstract Motivation: Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data available publicly. However, the heterogeneity of the different data sources, both at the syntactic and the semantic level, still poses significant challenges for achieving interoperability among biological databases. Results: We introduce an ontology-based federated approach for data integration. We applied this approach to three heterogeneous data stores that span different areas of biological knowledge: (i) Bgee, a gene expression relational database; (ii) Orthologous Matrix (OMA), a Hierarchical Data Format 5 orthology DS; and (iii) UniProtKB, a Resource Description Framework (RDF) store containing protein sequence and functional information. To enable federated queries across these sources, we first defined a new semantic model for gene expression called GenEx. We then show how the relational data in Bgee can be expressed as a virtual RDF graph, instantiating GenEx, through dedicated relational-to-RDF mappings. By applying these mappings, Bgee data are now accessible through a public SPARQL endpoint. Similarly, the materialized RDF data of OMA, expressed in terms of the Orthology ontology, is made available in a public SPARQL endpoint. We identified and formally described intersection points (i.e. virtual links) among the three data sources. These allow performing joint queries across the data stores. Finally, we lay the groundwork to enable nontechnical users to benefit from the integrated data, by providing a natural language template-based search interface.

Ontology-Based Heterogeneous Data Integration Technology

2013 ◽  
Vol 380-384 ◽  
pp. 3900-3903
Author(s):  
Kang Li ◽  
Xin Ming Li ◽  
Dong Liu ◽  
Yun Fei Cui
Keyword(s):  
Data Integration ◽  
Heterogeneous Data ◽  
Information Resources ◽  
Semantic Mapping ◽  
Semantic Description ◽  
Integration Framework ◽  
Heterogeneous Data Integration ◽  
Integration Technology ◽  
Technology Analysis ◽  
Semantic Conflicts

For the vast amounts of heterogeneous data is difficult to effectively deal with the problem, propose an ontology-based data integration technology. Analysis of the types of semantic conflicts in data integration, ontology semantic description of traditional data integration framework for data integration middleware module. Ontology concept in the domain of information resources, and take the initiative to find and build semantic mapping between semantic conflicts.

DATA INTEGRATION MODEL DESIGN FOR SUPPORTING DATA CENTER PATIENT SERVICES DISTRIBUTED INSURANCE PURCHASE WITH VIEW BASED DATA INTEGRATION

2018 ◽  
Vol 3 (2) ◽  
pp. 162
Author(s):  
Slamet Sudaryanto Nurhendratno ◽  
Sudaryanto Sudaryanto
Keyword(s):  
Data Integration ◽  
Data Center ◽  
Data Exchange ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Query Rewriting ◽  
Schema Mapping ◽  
Data Interoperability ◽  
Integration Model ◽  
Data Source

 Data integration is an important step in integrating information from multiple sources. The problem is how to find and combine data from scattered data sources that are heterogeneous and have semantically informant interconnections optimally. The heterogeneity of data sources is the result of a number of factors, including storing databases in different formats, using different software and hardware for database storage systems, designing in different data semantic models (Katsis & Papakonstantiou, 2009, Ziegler & Dittrich , 2004). Nowadays there are two approaches in doing data integration that is Global as View (GAV) and Local as View (LAV), but both have different advantages and limitations so that proper analysis is needed in its application. Some of the major factors to be considered in making efficient and effective data integration of heterogeneous data sources are the understanding of the type and structure of the source data (source schema). Another factor to consider is also the view type of integration result (target schema). The results of the integration can be displayed into one type of global view or a variety of other views. So in integrating data whose source is structured the approach will be different from the integration of the data if the data source is not structured or semi-structured. Scheme mapping is a specific declaration that describes the relationship between the source scheme and the target scheme. In the scheme mapping is expressed in in some logical formulas that can help applications in data interoperability, data exchange and data integration. In this paper, in the case of establishing a patient referral center data center, it requires integration of data whose source is derived from a number of different health facilities, it is necessary to design a schema mapping system (to support optimization). Data Center as the target orientation schema (target schema) from various reference service units as a source schema (source schema) has the characterization and nature of data that is structured and independence. So that the source of data can be integrated tersetruktur of the data source into an integrated view (as a data center) with an equivalent query rewriting (equivalent). The data center as a global schema serves as a schema target requires a "mediator" that serves "guides" to maintain global schemes and map (mapping) between global and local schemes. Data center as from Global As View (GAV) here tends to be single and unified view so to be effective in its integration process with various sources of schema which is needed integration facilities "integration". The "Pemadu" facility is a declarative mapping language that allows to specifically link each of the various schema sources to the data center. So that type of query rewriting equivalent is suitable to be applied in the context of query optimization and maintenance of physical data independence.Keywords: Global as View (GAV), Local as View (LAV), source schema ,mapping schema

Research on the Expert System for Sweet Corn Standard Production Based on Heterologous Data Integration Technology

2013 ◽  
Vol 655-657 ◽  
pp. 1730-1733
Author(s):  
Lin Peng ◽  
Qiang Zheng ◽  
Zhao Rong Liu
Keyword(s):  
Expert System ◽  
Data Integration ◽  
Data Management ◽  
Sweet Corn ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Integration Technology ◽  
Shared Data ◽  
Heterogeneous Data Sources ◽  
Standard Production

To better share agricultural information in existed agricultural informatization condition, and to meet agro-departments new needs about local self-governed and global shared data management during standardized production of the sweet corn, this paper provides a method of integrated sharing of heterogeneous data sources to apply to standardized product of the sweet corn. This method solves the data integration and sharing problems during standardized production of the sweet corn. In this paper, the expert system for sweet corn standard production which is ability to combine heterogeneous data is constructed. This system is proved to be reliable, perform well and it is easy to operate.

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