A Survey on Live Video Stream using Distributed Technologies
Abhinav Pandey1, Harendra Singh2
1Abhinav Pandey, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence, Bhopal (M.P), India.
2Mr. Harendra Singh, Assistant Professor, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence, Bhopal (M.P), India.
Manuscript received on 8 February 2018 | Revised Manuscript received on 15 February 2018 | Manuscript Published on 28 February 2018 | PP: 1-3 | Volume-7 Issue-5, February 2018 | Retrieval Number: E2487027518/18©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: As data is growing in multiple dimensions with lots of verities and gradually becoming humungous so there is high demand of frameworks for processing Big Data. There were frameworks available for processing structured and semi structured data but for processing un-structured data with real time analysis, very few options were availabe which can process only limited amount of data and high volume of data was a bottleneck for IT industries. It was not a big deal to work with ‘data in rest’ and only few frameworks available for analyzing data in motion like Apache Storm, got lots of motivation. Recently, for live streaming analysis and instant decision making Spark Streaming got introduced by Data Bricks and this is gaining lots of limelight due to its easy configuration and setup with loads of machine learning techniques and reliability at distributed platform. Using Storm prediction and face recognition were implemented. Through this study we will implement Real time video streaming analysis using Spark Streaming.
Keywords: Distributed System, Hadoop, Spark, Spark Streaming, Opne CV
Scope of the Article: Streaming Data