Distributed spatial and spatio-temporal join on apache spark
RT Whitman, BG Marsh, MB Park, EG Hoel - ACM Transactions on …, 2019 - dl.acm.org
Effective processing of extremely large volumes of spatial data has led to many
organizations employing distributed processing frameworks. Apache Spark is one such
open source framework that is enjoying widespread adoption. Within this data space, it is
important to note that most of the observational data (ie, data collected by sensors, either
moving or stationary) has a temporal component or timestamp. To perform advanced
analytics and gain insights, the temporal component becomes equally important as the …
organizations employing distributed processing frameworks. Apache Spark is one such
open source framework that is enjoying widespread adoption. Within this data space, it is
important to note that most of the observational data (ie, data collected by sensors, either
moving or stationary) has a temporal component or timestamp. To perform advanced
analytics and gain insights, the temporal component becomes equally important as the …
Spatio-temporal join on apache spark
RT Whitman, MB Park, BG Marsh, EG Hoel - Proceedings of the 25th …, 2017 - dl.acm.org
Effective processing of extremely large volumes of spatial data has led to many
organizations employing distributed processing frameworks. Apache Spark is one such
open-source framework that is enjoying widespread adoption. Within this data space, it is
important to note that most of the observational data (ie, data collected by sensors, either
moving or stationary) has a temporal component, or timestamp. In order to perform
advanced analytics and gain insights, the temporal component becomes equally important …
organizations employing distributed processing frameworks. Apache Spark is one such
open-source framework that is enjoying widespread adoption. Within this data space, it is
important to note that most of the observational data (ie, data collected by sensors, either
moving or stationary) has a temporal component, or timestamp. In order to perform
advanced analytics and gain insights, the temporal component becomes equally important …