Versioned triple pattern fragments : a low-cost linked data interface feature

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Decibel: the relational dataset branching system

As scientific endeavors and data analysis become increasingly collaborative, there is a need for data management systems that natively support the versioning or branching of datasets to enable concurrent analysis, cleaning, integration, manipulation, or curation of data across teams of individuals. Common practice for sharing and collaborating on datasets involves creating or storing multiple copies of the dataset, one for each stage of analysis, with no provenance information tracking the relationships between these datasets. This results not only in wasted storage, but also makes it challenging to track and integrate modifications made by different users to the same dataset. In this paper, we introduce the Relational Dataset Branching System, Decibel, a new relational storage system with built-in version control designed to address these short-comings. We present our initial design for Decibel and provide a thorough evaluation of three versioned storage engine designs that focus on efficient query processing with minimal storage overhead. We also develop an exhaustive benchmark to enable the rigorous testing of these and future versioned storage engine designs.National Science Foundation (U.S.) (1513972)National Science Foundation (U.S.) (1513407)National Science Foundation (U.S.) (1513443)Intel Science and Technology Center for Big Dat

TrieDF: Efficient In-memory Indexing for Metadata-augmented RDF

International audienceMetadata, such as provenance, versioning, temporal annotations, etc., is vital for the maintenance of RDF data. Despite its importance in the RDF ecosystem, support for metadata-augmented RDF remains limited. Some solutions focus on particular annotation types but no approach so far implements arbitrary levels of metadata in an application-agnostic way. We take a step to tackle this limitation and propose an in-memory tuple store architecture that can handle RDF data augmented with any type of metadata. Our approach, called TrieDF, builds upon the notion of tries to store the indexes and the dictionary of a metadataaugmented RDF dataset. Our experimental evaluation on three use cases shows that TrieDF outperforms state-of-the-art in-memory solutions for RDF in terms of main memory usage and retrieval time, while remaining application-agnostic

Evaluating Query and Storage Strategies for RDF Archives

There is an emerging demand on efficiently archiving and (temporal) querying different versions of evolving semantic Web data. As novel archiving systems are starting to address this challenge, foundations/standards for benchmarking RDF archives are needed to evaluate its storage space efficiency and the performance of different retrieval operations. To this end, we provide theoretical foundations on the design of data and queries to evaluate emerging RDF archiving systems. Then, we instantiate these foundations along a concrete set of queries on the basis of a real-world evolving dataset. Finally, we perform an empirical evaluation of various current archiving techniques and querying strategies on this data that is meant to serve as a baseline of future developments on querying archives of evolving RDF data