2023
DOI: 10.3390/fi15080263
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Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees

Abstract: Numerous real-life smart city application scenarios require joint analytics on unified views of georeferenced mobility data with environment contextual data including pollution and meteorological data. particularly, future urban planning requires restricting vehicle access to specific areas of a city to reduce the adverse effect of their engine combustion emissions on the health of dwellers and cyclers. Current editions of big spatial data management systems do not come with over-the-counter support for simila… Show more

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Cited by 5 publications
(2 citation statements)
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“…Also, spatial-aware approximate query processing (SAQP) is employed for integrating calibrated granular heterogeneous spatially tagged pollution and mobility data for joint analytics at scale with QoS guarantees, as in [25]. Authors have designed EMDI (Environmental Mobility Data Integrator) for integrating mobility data with environmental data, e.g., pollution, climatological, and meteorological data, for complex unified analytics with QoS guarantees.…”
Section: Spatial Approximate Query Processingmentioning
confidence: 99%
“…Also, spatial-aware approximate query processing (SAQP) is employed for integrating calibrated granular heterogeneous spatially tagged pollution and mobility data for joint analytics at scale with QoS guarantees, as in [25]. Authors have designed EMDI (Environmental Mobility Data Integrator) for integrating mobility data with environmental data, e.g., pollution, climatological, and meteorological data, for complex unified analytics with QoS guarantees.…”
Section: Spatial Approximate Query Processingmentioning
confidence: 99%
“…A multitude of practical smart city application scenarios necessitate the integration of joint analytics on unified perspectives of geo-referenced mobility data and contextual environmental data, such as meteorological and pollution data. In [8], the authors present the design and prototyping of an innovative system, denoted as EMDI. This system enables a unified view of integrated analytics by augmenting human and vehicle mobility data with pollution information.…”
mentioning
confidence: 99%