HTTP Extensions for the Management of Highly Dynamic Data ResourcesDownload PDF

11 Dec 2020, 11:17 (edited 16 Mar 2021)ESWC 2021 ResearchReaders: Everyone
  • Keywords: HTTP Memento Protocol, FAIR Data Management, Decentralization, Version Management, State Synchronization, Linked Data, RDF
  • Abstract: As Semantic Web Technologies are increasingly employed for the management of highly-dynamic data resources, e.g., the Industrial Internet of Things, resource versioning, state synchronization and distributed data management infrastructures are gaining practical relevance. The HTTP Memento protocol has recently been discussed as a promising building block for the implementation of such services for Findable, Accessible, Interoperable and Reusable (FAIR) Data. While this standard already enables the management and discovery of persistent, immutable and versioned resources on the Web and in Knowledge Graphs, it lacks support for the management of data updated at high frequencies or the interactions during resource modification and only provides inefficient means for managing resources with many revisions. To address these shortcomings, we propose three extensions to the HTTP Memento protocol: arbitrary resolution timestamps, resource creation support and range requests for TimeMaps. We provide a reference implementation of our proposals as open source software and quantitatively evaluate the extensions’ performance, showcasing superior results in terms of resource capacity, insertion correctness, latency and amount of transferred data. Based on a qualitative analysis, we conclude that in conjunction with our proposed extensions, the HTTP Memento protocol addresses a variety of data management challenges including data archiving, citation, retrieval, discovery, synchronization and sustainability for highly dynamic data on the Web and in Knowledge Graphs, providing a promising foundation for prospective standardized and interoperable data management solutions.
  • First Author Is Student: Yes
  • Subtrack: Semantic Data Management, Querying and Distributed Data
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