Authors - Shailaja B. Jadhav, D. V. Kodavade, Suhasini S. Goilkar Abstract - Data centric applications are increasing worldwide, inspiring data scientists to devise more sophisticated methods capable of modelling highly dynamic, extremely speedy data. There are existing approaches which adopt concept learning, dynamism, combining different approaches and heterogeneous classifiers. But, very few of them consider real time data generated through live data savvy applications. This necessitates Streaming data analytics as emerging area of research traditional data mining is not sufficient to achieve desired efficacy. This research aims to focus on streaming data classification particularly flight stream data and presents a comprehensive design framework of multi-layered ensemble built through pool of classifiers selected with prequential evaluation. The model is experimented with various known platforms of streaming data analysis like scikit multiflow, MOA etc. through systematic experimental work. Also, considering the volume of streaming data the experiments have also utilised GPU environments and Google TensorFlow wherever necessary. This Research addresses data streaming analytics majorly, as it needs more attention from research community. There is still scarcity of established benchmarks and standardized frameworks. Major observations, evaluation of design finds that the designed model is able to capture the dynamic nature and improves the classification accuracy as compared with that of the available traditional ensemble models.
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