An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics

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. IntroductionNotwithstanding 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]. For the technological enablers, the increasing availability of a variety of context-capturing machinery, in which embedded sensors, local processing and communication capabilities are combined, allows for large-scale, high-volume capturing and streaming of a broad variety of context data. Examples notably include sensor-packed smart vehicles, mobile hand-held devices (e.g., smart phones, tables) and smart, wearable devices (e.g., smart watches and bracelets, sport trackers, smart clothing), which can effectively collect an individual's real-time location, along with other relevant contextual information (e.g., [4]). A second technological milestone is the rapid evolution and proliferation of powerful mobile hand-held computing devices, a condition sine qua non to run full-fledged, context-aware applications [5].On the other hand, infrastructure-related enablers are now in place: fully rolled-out communication networks (e.g., 3G and 4G) with resulting ubiquitous internet access, and commercially available, economised cloud-based storage and computing infrastructures [6], provide unprecedented means to build the next-generation of context-aware applications and services, based on multi-user, real-time and high-frequency input streams; real-time data handling, processing and analytics; and real-time, location-and context-based r...

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