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Big Data Video Analytics using Hadoop Latest Technology

Big Data Video Analytics using Hadoop Latest Technology

The analysis is one of the important actions that need to be done once the content is available in the marketplace.The analysis provides information about the number of viewers, views and also the comments/reviews by the user on the same. Data is available in two forms – structured and unstructured. Everything about big data video analytics is explained here.

Big Data Video Analytics

Where there are many established analytics tools for use on structured data, analysis of unstructured (large scale) data in the form of video format is still a grey area as far as analysis is concerned.Use of video recording gadgets has been steadily increasing thereby increasing the data and also the requirement to analyse the same.

A quick look at the data collected all over the world indicates that 80% of all the data is in unstructured format whereas the presently available analysis tools can analyse the data that is structured one.

There is another indicative information that YouTube has been getting uploads of over 300 hours of video data every day. This huge data requires another equally solid analytical tool for analysis.J

ust in time, Hadoop (from Apache) comes handy for solving the issue of analysis of big video data. The success of Hadoop in an analysis of structured data is naturally attracting the interest of many stakeholders, and they strongly believe that Hadoop can effectively analyse the unstructured video big data.

Some of the concepts like Transcoding, MapReduce Architecture can come handy for Hadoop to start the analysis of unstructured video big data.However, Hadoop has limitations regarding structured query capabilities. Hadoop should improve its query capabilities to be able to start the analysis of the big data.

As a start up in the direction of analysis of video big data, following proposals are under way:

a) Conversion of the compressed video data to a sequence file of image frames through Transcoding.

b) Using Hadoop’s MapReduce jobs.

Conclusion:

It is apt to learn that Hadoop is having competition from similar projects like HPCC by Lexis Nexis, SciDB (cluster computing for scientific data), Disco by Nokia (yet another Map/Reduce implementation) etc.

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