The widespread adoption of Internet of Things (IoT) motivated the emergence of mixed workloads in smart cities, where fast arriving geo-referenced big data streams are joined with archive tables, aiming at enriching streams with descriptive attributes that enable insightful analytics. Applications are now relying on finding, in real-time, to which geographical regions data streaming tuples belong. This problem requires a computationally intensive stream-static join for joining a dynamic stream with a disk-resident static table. In addition, the time-varying nature of fluctuation in geospatial data arriving online calls for an approximate solution that can trade-off QoS constraints while ensuring that the system survives sudden spikes in data loads. In this paper, we present SpatialSSJP, an adaptive spatial-aware approximate query processing system that specifically focuses on stream-static joins in a way that guarantees achieving an agreed set of Quality-of-Service goals and maintains geo-statistics of stateful online aggregations over stream-static join results. SpatialSSJP employs a state-of-art stratified-like sampling design to select well-balanced representative geospatial data stream samples and serve them to a stream-static geospatial join operator downstream. We implemented a prototype atop Spark Structured Streaming. Our extensive evaluations on big real datasets show that our system can survive and mitigate harsh join workloads and outperform state-of-art baselines by significant magnitudes, without risking rigorous error bounds in terms of the accuracy of the output results. SpatialSSJP achieves a relative accuracy gain against plain Spark joins of approximately 10% in worst cases but reaching up to 50% in best case scenarios.

Jawarneh, I.M.A., Bellavista, P., Corradi, A., Foschini, L., Montanari, R. (2024). SpatialSSJP: QoS-Aware Adaptive Approximate Stream-Static Spatial Join Processor. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 35(1), 73-88 [10.1109/tpds.2023.3330669].

SpatialSSJP: QoS-Aware Adaptive Approximate Stream-Static Spatial Join Processor

Jawarneh, Isam Mashhour Al;Bellavista, Paolo;Corradi, Antonio;Foschini, Luca;Montanari, Rebecca
2024

Abstract

The widespread adoption of Internet of Things (IoT) motivated the emergence of mixed workloads in smart cities, where fast arriving geo-referenced big data streams are joined with archive tables, aiming at enriching streams with descriptive attributes that enable insightful analytics. Applications are now relying on finding, in real-time, to which geographical regions data streaming tuples belong. This problem requires a computationally intensive stream-static join for joining a dynamic stream with a disk-resident static table. In addition, the time-varying nature of fluctuation in geospatial data arriving online calls for an approximate solution that can trade-off QoS constraints while ensuring that the system survives sudden spikes in data loads. In this paper, we present SpatialSSJP, an adaptive spatial-aware approximate query processing system that specifically focuses on stream-static joins in a way that guarantees achieving an agreed set of Quality-of-Service goals and maintains geo-statistics of stateful online aggregations over stream-static join results. SpatialSSJP employs a state-of-art stratified-like sampling design to select well-balanced representative geospatial data stream samples and serve them to a stream-static geospatial join operator downstream. We implemented a prototype atop Spark Structured Streaming. Our extensive evaluations on big real datasets show that our system can survive and mitigate harsh join workloads and outperform state-of-art baselines by significant magnitudes, without risking rigorous error bounds in terms of the accuracy of the output results. SpatialSSJP achieves a relative accuracy gain against plain Spark joins of approximately 10% in worst cases but reaching up to 50% in best case scenarios.
2024
Jawarneh, I.M.A., Bellavista, P., Corradi, A., Foschini, L., Montanari, R. (2024). SpatialSSJP: QoS-Aware Adaptive Approximate Stream-Static Spatial Join Processor. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 35(1), 73-88 [10.1109/tpds.2023.3330669].
Jawarneh, Isam Mashhour Al; Bellavista, Paolo; Corradi, Antonio; Foschini, Luca; Montanari, Rebecca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/962266
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