Research on a Scheme of Heterogeneous Data Integration

2012 ◽  
Vol 546-547 ◽  
pp. 381-386
Author(s):  
Bao Sheng Wang ◽  
Sheng Yao Sun ◽  
Yan Yang Ma ◽  
Xin Feng Yang
Keyword(s):  
Data Integration ◽  
Web Service ◽  
Data Access ◽  
Heterogeneous Data ◽  
Grid Services ◽  
Component Structure ◽  
Heterogeneous Data Integration ◽  
Design Ideas ◽  
Open Grid Services Architecture ◽  
Test Result

Aiming at the limitation of Open Grid Services Architecture-Data Access and Integration middleware only face to JDBC, learning from the design ideas of OGSA-DAI, based on XML and web service, this paper proposes a middleware named XML Transformation-Data Access and Integration using ADO.NET. It analyzes the component structure of the middleware, the interaction of the workflow and the underlying data resources as well as the execution process of the workflow. It describes the conversion process between XML and database. Test result shows that the middleware gets good versatility and easy scalability in heterogeneous data access.

Integrating distributed data sources with OGSA–DAI DQP and V iews

2010 ◽  
Vol 368 (1926) ◽  
pp. 4133-4145 ◽  
Author(s):  
Bartosz Dobrzelecki ◽  
Amrey Krause ◽  
Alastair C. Hume ◽  
Alistair Grant ◽  
Mario Antonioletti ◽  
...  
Keyword(s):  
Data Integration ◽  
Query Processing ◽  
Data Access ◽  
Data Sources ◽  
Distributed Data ◽  
Grid Services ◽  
Distributed Query Processing ◽  
Processing Resource ◽  
Distributed Query ◽  
Open Grid Services Architecture

OGSA-DAI (Open Grid Services Architecture Data Access and Integration) is a framework for building distributed data access and integration systems. Until recently, it lacked the built-in functionality that would allow easy creation of federations of distributed data sources. The latest release of the OGSA-DAI framework introduced the OGSA-DAI DQP (Distributed Query Processing) resource. The new resource encapsulates a distributed query processor, that is able to orchestrate distributed data sources when answering declarative user queries. The query processor has many extensibility points, making it easy to customize. We have also introduced a new OGSA-DAI V iews resource that provides a flexible method for defining views over relational data. The interoperability of the two new resources, together with the flexibility of the OGSA-DAI framework, allows the building of highly customized data integration solutions.

Modern Manufacturing Enterprises Heterogeneous Data Integration System Based on Web Services

2012 ◽  
Vol 529 ◽  
pp. 356-360
Author(s):  
Xiao Xiao Liang ◽  
Shi Wen Li ◽  
Xu Zhan ◽  
Chong Gang Wei
Keyword(s):  
Web Services ◽  
Data Integration ◽  
Web Service ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Integration System ◽  
Heterogeneous Data Integration ◽  
Manufacturing Enterprises ◽  
Data Integration System ◽  
Modern Manufacturing

This webinar introduces an approach to data access across heterogeneous data sources and Web service integration based on XML and XQuery. The webinar covers: using XML to create logical views of a variety of physical data sources, aggregating XML documents with relational data, consuming Web services in XQuery, and exposing XQuery as data services while writing, testing and deploying real XML and XQuery-based solutions. The paper conducts web service and analyzes Modern Manufacturing Enterprises heterogeneous data source and proposes a heterogeneous data integration system based Web Services. The system provides a possible approach to share and interactive enterprise data.

Data Integration

2009 ◽  
pp. 460-470
Author(s):  
Ismael Navas-Delgado ◽  
Jose F. Aldana-Montes
Keyword(s):  
Data Integration ◽  
Large Data ◽  
Data Access ◽  
Heterogeneous Data ◽  
Data Types ◽  
Data Repositories ◽  
Heterogeneous Data Integration ◽  
Wide Range ◽  
Data Heterogeneity ◽  
Available Information

The growth of the Internet has simplified data access, which has involved an increment in the creation of new data sources. Despite this increment, in most cases, these large data repositories are accessed manually. This problem is aggravated by the heterogeneous nature and extreme volatility of the information on the Web. This heterogeneity includes three types: intentional (differences in the contents), semantic (differences in the interpretation), and schematic (data types, labeling, structures, etc.). Thus, the increase of the available information and the complexity of dealing with this amount of information have involved a considerable amount of research into the subject of heterogeneous data integration. The database community, one of the most important groups dealing with data heterogeneity and dispersion, has provided a wide range of solutions to this problem. However, this issue has also been addressed and solutions have been offered by the information retrieval and knowledge representation communities, making this area a connection point between the three communities.

VGEs-Oriented Multi-sourced Heterogeneous Data Integration

2010 ◽  
Vol 11 (3) ◽  
pp. 292-298
Author(s):  
Hongjun SU ◽  
Yehua SHENG ◽  
Yongning WEN ◽  
Min CHEN
Keyword(s):  
Data Integration ◽  
Heterogeneous Data ◽  
Heterogeneous Data Integration

Semantic-Based Geospatial Data Integration With Unique Features

2019 ◽  
pp. 254-277 ◽  
Author(s):  
Ying Zhang ◽  
Chaopeng Li ◽  
Na Chen ◽  
Shaowen Liu ◽  
Liming Du ◽  
...  
Keyword(s):  
Data Integration ◽  
High Performance ◽  
Data Access ◽  
Heterogeneous Data ◽  
Geospatial Data ◽  
Experimental Results ◽  
Data Sources ◽  
Data Format ◽  
Access Protocols ◽  
Data Source

Since large amount of geospatial data are produced by various sources, geospatial data integration is difficult because of the shortage of semantics. Despite standardised data format and data access protocols, such as Web Feature Service (WFS), can enable end-users with access to heterogeneous data stored in different formats from various sources, it is still time-consuming and ineffective due to the lack of semantics. To solve this problem, a prototype to implement the geospatial data integration is proposed by addressing the following four problems, i.e., geospatial data retrieving, modeling, linking and integrating. We mainly adopt four kinds of geospatial data sources to evaluate the performance of the proposed approach. The experimental results illustrate that the proposed linking method can get high performance in generating the matched candidate record pairs in terms of Reduction Ratio(RR), Pairs Completeness(PC), Pairs Quality(PQ) and F-score. The integrating results denote that each data source can get much Complementary Completeness(CC) and Increased Completeness(IC).

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