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

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 a...

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Vydáno v:IEEE transactions on parallel and distributed systems Ročník 35; číslo 1; s. 73 - 88
Hlavní autoři: Jawarneh, Isam Mashhour Al, Bellavista, Paolo, Corradi, Antonio, Foschini, Luca, Montanari, Rebecca
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms for data and knowledge management
Apache Spark
Big Data
Big Data Applications
Data Architecture
Data transmission
Geospatial analysis
Internet of Things
Microprocessors
Pipelines
QoS Data Management
Quality of service
Query Processing
Sampling designs
Smart cities
Sparks
Spatial data
Spatial databases and GIS
Spatial Indexes
Spatial Join
State of the art
Streams
Urban areas
Workload
Workloads
ISSN:1045-9219, 1558-2183
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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.
AbstractList 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.
Author Montanari, Rebecca
Corradi, Antonio
Bellavista, Paolo
Foschini, Luca
Jawarneh, Isam Mashhour Al
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  organization: Dipartimento di Informatica - Scienza e Ingegneria (DISI), University of Bologna, Bologna, Italy
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SubjectTerms Algorithms for data and knowledge management
Apache Spark
Big Data
Big Data Applications
Data Architecture
Data transmission
Geospatial analysis
Internet of Things
Microprocessors
Pipelines
QoS Data Management
Quality of service
Query Processing
Sampling designs
Smart cities
Sparks
Spatial data
Spatial databases and GIS
Spatial Indexes
Spatial Join
State of the art
Streams
Urban areas
Workload
Workloads
Title SpatialSSJP: QoS-Aware Adaptive Approximate Stream-Static Spatial Join Processor
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