A Method of Data Integration Based on Cloud

2013 ◽  
Vol 433-435 ◽  
pp. 1876-1879 ◽  
Author(s):  
Guo Ling Liu ◽  
Chun Hua Yang

This work studied the situation of today’s data integration. To better solve the complexity and diversity in data integration, we propose a new idea of data integration service platform based on SaaS. The model architecture of data integration and its work principles are introduced. In the platform source data and result data are two interface.in the form of service. In the data integration platform, data are filtered and combined. the data are produced by the users’ requirements. Users call SaaS service to get the data that they wanted. Finally we analyze the model’s characters. This model of platform is more intelligent, efficient and personalized in solving complicate data integration.

2010 ◽  
Vol 7 (3) ◽  
Author(s):  
Simon J Cockell ◽  
Jochen Weile ◽  
Phillip Lord ◽  
Claire Wipat ◽  
Dmytro Andriychenko ◽  
...  

SummaryDrug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.


2019 ◽  
Vol 15 (2) ◽  
pp. 69-87 ◽  
Author(s):  
Hongyan Yun ◽  
Ying He ◽  
Li Lin ◽  
Xiaohong Wang

The purpose of data integration is that integrates multi-source heterogeneous data. Ontology solves semantic describing of multi-source heterogeneous data. The authors propose a practical approach based on ontology modeling and an information toolkit named Karma modeling for fast data integration, and demonstrate an application example in detail. Armed Conflict Location & Event Data Project (ACLED) is a publicly available conflict event dataset designed for disaggregated conflict analysis and crisis mapping. The authors analyzed the ACLED dataset and domain knowledge to build an Armed Conflict Event ontology, then constructed Karma models to integrate ACLED datasets and publish RDF data. Through SPARQL query to check the correctness of published RDF data. Authors design and developed an ACLED Query System based on Jena API, Canvas JS, and Baidu API, etc. technologies, which provides convenience for governments and researches to analyze regional conflict events and crisis early warning, and it verifies the validity of constructed ontology and the correctness of Karma modeling.


2012 ◽  
Vol 13 (1) ◽  
Author(s):  
Felix Dreher ◽  
Thomas Kreitler ◽  
Christopher Hardt ◽  
Atanas Kamburov ◽  
Reha Yildirimman ◽  
...  

2013 ◽  
Vol 321-324 ◽  
pp. 2532-2538
Author(s):  
Xiao Guo Wang ◽  
Jian Shen ◽  
Chuan Sun

Considering the difficulty of information collection and integration due to the rapid growth of information, we need an efficient tool to do these jobs. A proposal is be put forward to build a data integration system to collect the source data and preprocess the heterogeneous data and then convert/extract data to the data warehouse. Through experiment and analysis, this paper designed an information process flow and implemented the data integration system, based on B/S framework with the database technology, to deal with the college related information.


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