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  • 标题:Mashup Tool for Automatic Query Generation for Data Web
  • 本地全文:下载
  • 作者:K Annapurna Madhavi ; Prof.S.V.Achutha Rao
  • 期刊名称:International Journal of Computer Trends and Technology
  • 电子版ISSN:2231-2803
  • 出版年度:2013
  • 卷号:4
  • 期号:8-4
  • 出版社:Seventh Sense Research Group
  • 摘要:In real world we know the people who don’t know about that system are called as computer illiterates these people doesn’t know about the system and applications if any application that is giving some services to them that they don’t know about the Query language of application which can help to application in providing service and here if anything that they want extra other than that service then they need to face problem and to overcome this problem we are proposing this solution that provides the custom defined services in this proposed approach We present a query formulation language (called MashQL) in order to easily query and fuse structured data on the web. Here it is very usefull for the people who are not having at having technical knowledge and here they are having the option to get their own service by creating the custom services by them own. Mashups are applications that aggregate functionality, presentation, and/or contents from existing sources to create a new application. Contents are usually generated either using web feeds or an application programming interface (API). Both approaches have limitations as web feeds do not provide powerful data models for complex data structures and lack powerful features of database systems. On the other hand, API’s are usually limited to a specific application thus requiring different implementations for each of the sources used in the mashups. We propose a query based aggregation of multiple heterogeneous data sources by combining powerful querying features of XQuery and SPARQL with an easy interface of a mashup tool for data sources in XML and RDF. Our mashup editor allows for automatic generation of mashups with an easy to use visual interface.
  • 关键词:Query Formulation; Semantic Web; Data Web; RDF; SPARQL; Indexing Methods; Query Optimization; Mash up
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