DRAS-TIC Linked Data: Evenly Distributing the Past

Gregory Jansen, Aaron Coburn, Adam Soroka, Will Thomas and Richard Marciano
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Gregory Jansen: School of Information Studies, University of Maryland, College Park, MD 20742, USA
Aaron Coburn: Information Technology Services, Amherst College, Amherst, MA 01002, USA
Adam Soroka: Office of the CIO, The Smithsonian Institution, Washington, DC 20002, USA
Will Thomas: School of Information Studies, University of Maryland, College Park, MD 20742, USA
Richard Marciano: School of Information Studies, University of Maryland, College Park, MD 20742, USA

Publications, 2019, vol. 7, issue 3, 1-13

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.

Keywords: distributed database; linked data platform; Fedora Commons repository; horizontal scaling (search for similar items in EconPapers)
JEL-codes: A2 D83 L82 (search for similar items in EconPapers)
Date: 2019
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