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Open AccessArticle

DRAS-TIC Linked Data: Evenly Distributing the Past

1
School of Information Studies, University of Maryland, College Park, MD 20742, USA
2
Information Technology Services, Amherst College, Amherst, MA 01002, USA
3
Office of the CIO, The Smithsonian Institution, Washington, DC 20002, USA
*
Author to whom correspondence should be addressed.
Publications 2019, 7(3), 50; https://doi.org/10.3390/publications7030050
Received: 1 March 2019 / Revised: 12 June 2019 / Accepted: 27 June 2019 / Published: 4 July 2019
(This article belongs to the Special Issue Selected Papers from Open Repositories 2018)
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PDF [9237 KB, uploaded 4 July 2019]
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Abstract

Memory institutions must be able to grow a fully-functional repository incrementally as collections grow, without expensive enterprise storage, massive data migrations, and the performance limits that stem from the vertical storage strategies. The Digital Repository at Scale that Invites Computation (DRAS-TIC) Fedora research project, funded by a two-year National Digital Platform grant from the Institute for Museum and Library Services (IMLS), is producing open-source software, tested cluster configurations, documentation, and best-practice guides that enable institutions to manage linked data repositories with petabyte-scale collections reliably. DRAS-TIC is a research initiative at the University of Maryland (UMD). The first DRAS-TIC repository system, named Indigo, was developed in 2015 and 2016 through a collaboration between U.K.-based storage company, Archive Analytics Ltd., and the UMD iSchool Digital Curation Innovation Center (DCIC), through funding from an NSF DIBBs (Data Infrastructure Building Blocks) grant (NCSA “Brown Dog”). DRAS-TIC Indigo leverages industry standard distributed database technology, in the form of Apache Cassandra, to provide open-ended scaling of repository storage without performance degradation. With the DRAS-TIC Fedora initiative, we make use of the Trellis Linked Data Platform (LDP), developed by Aaron Coburn at Amherst College, to add the LDP API over similar Apache Cassandra storage. This paper will explain our partner use cases, explore the system components, and showcase our performance-oriented approach, with the most emphasis given to performance measures available through the analytical dashboard on our testbed website. View Full-Text
Keywords: distributed database; linked data platform; Fedora Commons repository; horizontal scaling distributed database; linked data platform; Fedora Commons repository; horizontal scaling
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Jansen, G.; Coburn, A.; Soroka, A.; Thomas, W.; Marciano, R. DRAS-TIC Linked Data: Evenly Distributing the Past. Publications 2019, 7, 50.

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