Abstract
In large-scale context-aware applications, a central design concern is capturing, managing and acting upon location and context data. The ability to understand the collected data and define meaningful contextual events, based on one or more incoming (contextual) data streams, both for a single and multiple users, is hereby critical for applications to exhibit location- and context-aware behaviour. In this article, we describe a context-aware, data-intensive metrics platform —focusing primarily on its geospatial support—that allows exactly this: to define and execute metrics, which capture meaningful spatio-temporal and contextual events relevant for the application realm. The platform (1) supports metrics definition and execution; (2) provides facilities for real-time, in-application actions upon metrics execution results; (3) allows post-hoc analysis and visualisation of collected data and results. It hereby offers contextual and geospatial data management and analytics as a service, and allow context-aware application developers to focus on their core application logic. We explain the core platform and its ecosystem of supporting applications and tools, elaborate the most important conceptual features, and discuss implementation realised through a distributed, micro-service based cloud architecture. Finally, we highlight possible application fields, and present a real-world case study in the realm of psychological health.
Highlights
Notwithstanding the early promise of location- and context-aware applications, only in the last decade have we witnessed the required technological and infrastructural enablers to truly unleash their potential [2,3]
The common denominator is a lack of solutions to support both spatial and non-spatial analytic computing for location- and context-aware applications. In response to this lack, we describe an analytics platform to enable the definition and execution of spatio-temporal metrics as part of location-aware applications
In location-aware games, the role of spatio-temporal metrics is as important as other metrics [32], and more generally, in locationand context-aware applications, spatio-temporal metrics can help developers to quantify and better understand the phenomena and dynamics that occur in real-world applications [44,45]
Summary
Notwithstanding the early promise of location- and context-aware applications (see e.g., [1] for a survey of early systems), only in the last decade have we witnessed the required technological and infrastructural enablers to truly unleash their potential [2,3]. Research works (e.g., [19,20,21,22,23]) have made substantial progress over the past years to go well beyond desktop-based environments to bring geospatial workflows to the cloud and distributed computing environments, contributing to the field of Geoprocessing Web [24,25] To this regard, leading voices recently called for an entirely new brand of geospatial platforms and systems to analyse and process real-time data streams [26,27,28,29]. We critically discuss, present possible use cases and application fields, and a running case in the field of psychological health
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