Evaluating query and storage strategies for RDF archives

Abstract

Abstract.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 datasets. Finally, we perform an extensive empirical evaluation of current archiving techniques and querying strategies, which is meant to serve as a baseline of future developments on querying archives of evolving RDF data.

A more decentralized vision for Linked Data

“…Towards a solution path: It seems that in order to avoid both such discrepancies and bottlenecks for downloads and query processing, a combination of (i) dumps provided in HDT [4], a compressed and queryable RDF format, as well as (ii) Triple Pattern Fragments (TPF) endpoints [10] as the standard access method for Linked Datasets could alleviate some of these problems: the triple-patterns fragment interface -essentially limits queries to an endpoint to simple triple matching queries which offloads processing of complex joins and other operations to the client-side, while still not having to download complete dumps. HDT, 5 on the other hand is an already compressed dump-format that allows such triple pattern queries without decompression and also guarantees duplicate-freeness. Notably, we note that e.g.…”

Evaluation of metadata representations in RDF stores

“…Although the authors do not specifically evaluate datasets with metadata, they highlight the need for benchmarks, which cover provenance and temporal data as well. Finally, the semantic web community has been actively proposed testbeds for evaluating: Federated query engines [31,40,42], triple/graph or dataset versioning [11,29,36], streaming [48], and geospatial 6 querying [15]. However, to the best of our knowledge, there is no benchmark that generates metadata-rich datasets or queries over fine grained meta information.…”

A Semantic Retrieval System in Remote Sensing Web Platforms

“…The algorithm of graph mining works sometimes through an API connected directly to the triple store, sometimes with a common SPARQL endpoint, depending on the reference thesaurus. Both query and storage strategies have their advantages and disadvantages but none is neglected in the scope of our research (Fernández et al, 2018).…”