SPARQL2Flink: Evaluation of SPARQL Queries on Apache Flink

Ceballos, Oscar; Restrepo, Carlos Alberto Ramírez; Pabón, María Constanza; Castillo, Andrés M.; Corcho, Óscar

doi:10.3390/app11157033

Cited by 5 publications

(1 citation statement)

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“…In this work, the data flows through Kafka processing components and is passed through the Flink ecosystem, which is connected through rules that are generated through DT rules, and based on that, the query can be processed. The SPARQL query enables the Flink to SPARQL component to efficiently run the query and fetch the result in real time [26]. b.…”

Section: Flink To Sparql Connectivitymentioning

confidence: 99%

Real-Time Approach for Smart Building Operations Prediction Using Rule-Based Complex Event Processing and SPARQL Query

KUMAR,

Chandra,

Agarwal

2023

Preprint

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Due to intelligent, adaptive nature towards various operations and their ability to provide maximum comfort to the occupants residing in them, smart buildings are becoming a pioneering area of research. Since these architectures leverage the Internet of Things (IoT), there is a need for monitoring different operations (Occupancy, Humidity, Temperature, CO2 , etc.) to provide sustainable comfort to the occupants. This paper proposes a novel approach for intelligent building operations monitoring using rule-based complex event processing and query-based approaches for dynamically monitoring the different operations. Siddhi is a complex event processing engine designed for handling multiple sources of event data in real time and processing it according to predefined rules using a decision tree. Since streaming data is dynamic in nature, to keep track of different operations, we have converted the IoT data into an RDF dataset. The RDF dataset is ingested to Apache Kafka for streaming purposes and for stored data we have used the GraphDB tool that extracts information with the help of SPARQL query. Consequently, the proposed approach is also evaluated by deploying the large number of events through the Siddhi CEP engine and how efficiently they are processed in terms of time. Apart from that, a risk estimation scenario is also designed to generate alerts for end users in case any of the smart building operations need immediate attention. The output is visualized and monitored for the end user through a tableau dashboard.

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Section: Flink To Sparql Connectivitymentioning

confidence: 99%