The widespread adoption of sensor-enabled and mobile ubiquitous devices has caused an avalanche of big data that is mostly geospatially tagged. Most cloud-based big data processing systems are designed for general-purpose workloads, neglecting spatial-characteristics. However, interesting analytics often seek answers for proximity-alike queries. We fill this gap by providing custom geospatial service layer atop of Apache Spark. To be more specific, we leverage Spark to design a custom spatial-aware partitioning method to boost geospatial query performances. Our results show that our patches outperform state-of-the-art implementations by significant fractions.
Al Jawarneh, I.M., Bellavista, P., Corradi, A., Foschini, L., Montanari, R., Zanotti, A. (2018). In-memory Spatial-Aware Framework for Processing Proximity-Alike Queries in Big Spatial Data. Institute of Electrical and Electronics Engineers Inc. [10.1109/CAMAD.2018.8514950].
In-memory Spatial-Aware Framework for Processing Proximity-Alike Queries in Big Spatial Data
Al Jawarneh, Isam Mashhour;Bellavista, Paolo;Corradi, Antonio;Foschini, Luca;Montanari, Rebecca;
2018
Abstract
The widespread adoption of sensor-enabled and mobile ubiquitous devices has caused an avalanche of big data that is mostly geospatially tagged. Most cloud-based big data processing systems are designed for general-purpose workloads, neglecting spatial-characteristics. However, interesting analytics often seek answers for proximity-alike queries. We fill this gap by providing custom geospatial service layer atop of Apache Spark. To be more specific, we leverage Spark to design a custom spatial-aware partitioning method to boost geospatial query performances. Our results show that our patches outperform state-of-the-art implementations by significant fractions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
