HeriwarD Platform

is a complex software system that supports the preservation of artistic and Cultural Heritage.

Tourists can exploit it to easily discover Cultural Heritage through contextual info related to users’ locations and interests, and to provide feedbacks on their health state; technical experts can perform periodic professional assessments by leaving articulated digital reports; wireless sensors continuously monitor the Cultural Heritage surrounding environments by sending samples of physical and chemical parameters.

All these data are analyzed by the platform that suggests whether the heritage should be restored. Figure below shows a simplified activities flow supported by HeriwarD.

A Cultural Heritage is preliminarily defined as a digital and semantic entity (asset): it includes basic information describing the asset, plots of the main parts of the structure, overlapped layers to associate different features (materials, chemical phenomena, etc.) and chemical/physical parameters (collecting data from sensors) to each part. Once created, a cultural digital asset is immediately accessible from a mobile app that enables on-site fruition to tourists and experts.

HeriwarD app exploits device sensors and user profiles to implicitly search CH around the user’s location and offers a travel support to reach them. When on-site, the user can exploit an additional contextual infrastructure (based on iBeacon technology), enabled by the smart guide mode, to automatically receive, while walking, multimedia information about the visited areas.

During the visit, a user actively participates to the overall ecosystem: he/she can put the visited CH in his/her preferred list and leaves a brief review, can perform simplified assessments, in smart feedback mode, by answering to interactive questionnaires to collect information about the CH health state.

The information provided by users is then processed and completed by professional assessments. To this end, experts modify the layers of the digital cultural asset and this additional information is then used to automatically infer the need for possible maintenance interventions. When necessary, the system initiates a workflow process to address this need.

Main Features

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