Scalable and Reliable Deep Learning Model to handle real-time Streaming Data
L. Amudha1, R. Pushpalakshmi2

1L. Amudha*, Department of CSE, K.. Ramakrishnan College of Engineering. Tamilnadu, India.
2R. Pushpalakshmi, Department of Information Technology, PSNA College of Engineering and Technology, Tamilnadu. India.
Manuscript received on January 24, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 29, 2020. | PP: 3840-3844 | Volume-9 Issue-3, February 2020. | Retrieval Number:  C6272029320/2020©BEIESP | DOI: 10.35940/ijeat.C6272.029320
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Abstract: We have real-time data everywhere and every day. Most of the data comes from IoT sensors, data from GPS positions, web transactions and social media updates. Real time data is typically generated in a continuous fashion. Such real-time data are called Data streams. Data streams are transient and there is very little time to process each item in the stream. It is a great challenge to do analytics on rapidly flowing high velocity data. Another issue is the percentage of incoming data that is considered for analytics. Higher the percentage greater would be the accuracy. Considering these two issues, the proposed work is intended to find a better solution by gaining insight on real-time streaming data with minimum response time and greater accuracy. This paper combines the two technology giants TensorFlow and Apache Kafka. is used to handle the real-time streaming data since TensorFlow supports analytics support with deep learning algorithms. The Training and Testing is done on Uber connected vehicle public data set RideAustin. The experimental result of RideAustin shows the predicted failure under each type of vehicle parameter. The comparative analysis showed 16% improvement over the traditional Machine Learning algorithm.
Keywords: Real-time stream data, deep learning, Kafka, TensorFlow, Analytics