Query Processing over Large RDF using SPARQL in Big Data
| Autor: | Chetan Ingole, Kiran A. Dongre, Priti Khodke, Saurabh Lawange, Amol P. Bhagat |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Information retrieval
business.industry Semantic Web Rule Language Computer science 010401 analytical chemistry 02 engineering and technology Linked data computer.file_format 01 natural sciences Social Semantic Web 0104 chemical sciences World Wide Web 020204 information systems 0202 electrical engineering electronic engineering information engineering Semantic analytics SPARQL Semantic Web Stack business Semantic Web computer Data Web |
| Zdroj: | Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies. |
| DOI: | 10.1145/2905055.2905124 |
| Popis: | Internet search is done by exploring the link graph and keyword frequency. In 2012, Google released "Knowledge Graph" --Semantic Web. The human reasoning can be enhanced by the use semantic web an emerging area. Most of the current applications link open data views due to which there is huge flow of data in semantic web, particularly Resource Description Framework (RDF) data. In the semantic web research community this leads to design and development of scalable data processing techniques for RDF data. The aim of semantic web is to make available semantically connected data across the globe. This is a review paper giving analysis of techniques implemented to achieve the aim of semantic web, various approaches to processes RDF data. Within the semantic web community, RDF is a common acronym because it forms one of the basic building blocks for forming the web of semantic data, called a "graph database". This paper compares various methodologies followed by different researchers along with the results analysis of implemented techniques over different datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|