Towards an RDF Analytics Language: Learning from Successful Experiences.
COLD'13: Proceedings of the Fourth International Conference on Consuming Linked Data - Volume 1034(2013)
摘要
SPARQL, the W3C standard querying language for RDF, provides rich capabilities for slicing and dicing RDF data. The latest version, SPARQL 1.1, added support for aggregation, nested and distributed queries among others. Nevertheless, the purely declarative nature of SPARQL and the lack of support for common programming patterns, such as recursion and iteration, make it challenging to perform complex data processing and analysis in SPARQL. In the database community, similar limitations of SQL resulted in a surge of proposals of analytics languages and frameworks. These languages are carefully designed to run on top of distributed computation platforms. In this paper, we review these efforts of the database community, identify a number of common themes they bear and discuss their applicability in the Semantic Web and Linked Data realm. In particular, design decisions related to the data model, schema restrictions, data transformation and the programming paradigm are examined and a number of related challenges for defining an RDF analytics language are outlined.
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