Data-Driven Augmented Reality for Museum Exhibits and Lost Heritage Sites.

Paper
Rob Warren, Big Data Institute, Canada, David Evans, University of Derby, UK, Minsi Chen, University of Derby, UK, Mark Farrell, Dalhousie University, Canada, Daniel Mayles, University Of Derby, UK

Published paper: Data-driven enriched exhibits using augmented reality

We review the possibilities, pitfalls, and promises of recreating lost heritage sites and historical events using augmented reality and "Big Data" archival databases. We define augmented reality as any means of adding context or content, via audio/visual means, to the current physical space of a visitor to a museum or outdoor site. Examples range from simple prerecorded audio to graphics rendered in real time and displayed using a smartphone.

Previous work has focused on complex multimedia museum guides, whose utility remains to be evaluated as enabling or distracting. We propose the use of a data­-driven approach where the exhibits' augmentation is not static but dynamically generated from the totality of the data known about the location, artifacts, or event. For example, at Bletchley Park, reenacted audio conversations are played within rooms as visitors walk through them. These can be called "virtual contents," as the audio recordings are manufactured. Given that a number of documentary sources, such as meeting minutes, are available concerning the events that occurred within the site, a dynamic computer-generated script could add to the exhibits.

Visitors' experiences can therefore react to their movements, provide a different experience each time, and be factually correct without requiring any expensive redesign. Furthermore, the use of a data-driven approach allows for the updating of exhibits on the fly as researchers create or curate new data sources within the museum. If artifacts need to be removed from an exhibit, pictures, descriptions, or three-dimensional printed copies can be substituted, and the augmented reality of visitor experience can adapt accordingly.

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