Waist Deep in the Big Data: How the Digital Index of North American Archaeology (DINAA) Implements Ontological and Loosely Coupled Organization around the Construct of the Archaeological Site
Author(s): Joshua J. Wells
Archaeology’s disciplinary engagement with big data is confounded by the variety of information types recorded, variability of data due to differential preservation of materials and theoretical orientations of observers, and complexity of archaeological concepts daring to be caged in explicit digital expressions. The Digital Index of North American Archaeology (DINAA) is a linked open data hub, centered around the theoretically, practically, and interpretively fraught definition of "archaeological site," nevertheless a foundational concept in archaeological science. To develop big data from hundreds of thousands of archaeological site definitions gleaned from numerous incompatible systems of nomenclature and investigative frameworks, DINAA employs strategies of aggregated ontological definitions within DINAA and loose coupling to related informational resources outside it, often achieved via fairly standard intelligence key identifiers (i.e. Smithsonian trinomials and similar). This practice requires explicit and critical consideration of what DINAA’s big data elements "mean" in their original contexts as well as what else they might "mean" in combination in DINAA or to external investigators. The intellectual labor is not trivial, but is tractable, and exemplifies a replicable process for engaging other concepts of interest to archaeologists (artifactual categories, stylistic components, cultural horizons, etc.), operationalizing archaeology’s complexity in pursuit of big data.
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Waist Deep in the Big Data: How the Digital Index of North American Archaeology (DINAA) Implements Ontological and Loosely Coupled Organization around the Construct of the Archaeological Site. Joshua J. Wells. Presented at The 81st Annual Meeting of the Society for American Archaeology, Vancouver, British Columbia. 2017 ( tDAR id: 429454)
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Abstract Id(s): 16516