Next generation analytics for open pervasive display networks
Creators: name: Mikusz, Mateusz
Publisher: name: Lancaster University
Year: 2019

description: Public displays and digital signs are becoming increasingly widely
      deployed as many spaces move towards becoming highly interactive and
      augmented environments. Market trends suggest further significant
      increases in the number of digital signs and both researchers and
      commercial entities are working on designing and developing novel uses for
      this technology. Given the level of investment, it is increasingly
      important to be able to understand the effectiveness of public displays.
      Current state-of-the-art analytics technology is limited in the extent to
      which it addresses the challenges that arise from display deployments
      becoming open (increasing numbers of stakeholders), networked (viewer
      engagement across devices and locations) and pervasive (high density of
      displays and sensing technology leading to potential privacy threats for
      viewers). 
      
      
      In this thesis, we provide the first exploration into achieving next
      generation display analytics in the context of open pervasive display
      networks. In particular, we investigated three areas of challenge:
      analytics data capture, reporting and automated use of analytics data.
      Drawing on the increasing number of stakeholders, we conducted an
      extensive review of related work to identify data that can be captured by
      individual stakeholders of a display network, and highlighted the
      opportunities for gaining insights by combining datasets owned by
      different stakeholders. Additionally, we identified the importance of
      viewer-centric analytics that use traditional display-oriented analytics
      data combined with viewer mobility patterns to produce entirely new sets
      of analytics reports. We explored a range of approaches to generating
      viewer-centric analytics including the use of mobility models as a way to
      create 'synthetic analytics' - an approach that provides highly
      detailed analytics whilst preserving viewer privacy. 
      
      
      We created a collection of novel viewer-centric analytics reports
      providing insights into how viewers experience a large network of
      pervasive displays including reports regarding the effectiveness of
      displays, the visibility of content across the display network, and the
      visibility of content to viewers. We further identified additional reports
      specific to those display networks that support the delivery of
      personalised content to viewers. Additionally, we highlighted the
      similarities between digital signage and Web analytics and introduced
      novel forms of digital signage analytics reports created by leveraging
      existing Web analytics engines. 
      
      
      Whilst the majority of analytics systems focus solely on the capture and
      reporting of analytics insights, we additionally explored the automated
      use of analytics data. One of the challenges in open pervasive display
      networks is accommodating potentially competing content scheduling
      constraints and requirements that originate from the large number of
      stakeholders - in addition to contextual changes that may originate from
      analytics insights. To address these challenges, we designed and developed
      the first lottery scheduling approach for digital signage providing a
      means to accommodate potentially conflicting scheduling constraints, and
      supporting context- and event-based scheduling based on analytics data fed
      back into the digital sign. 
      
      
      In order to evaluate the set of systems and approaches presented in this
      thesis, we conducted large-scale, long-term trials allowing us to show
      both the technical feasibility of the systems developed and provide
      insights into the accuracy and performance of different analytics capture
      technologies. Our work provides a set of tools and techniques for next
      generation digital signage analytics and lays the foundation for more
      general people-centric analytics that go beyond the domain of digital
      signs and enable unique analytical insights and understanding into how
      users interact across the physical and digital world.