Video Big Data (Part 3) – From Mess to Intelligence?

The objective of Big Data is to gain Business Intelligence. Video Big Data is no different. The obvious difference is the source and the type of data that can be extracted out from videos. In there, lies the main challenges - Extraction, Transformation and Analysis.

In this instalment, we will explain why Artificial Intelligence is central to the “mess” in video big data.

In the first installment (Part 1), we explained:

• Why Video Big Data will absolutely dwarf current Big Data, and
• How Video is the most difficult medium to extract data

In the previous instalment (Part 2), we examined:

• the kind of data elements that we can extract from videos (speech, text, objects, activities, motion, faces, emotions)

But first, let’s examine why there is a mess in video data. The short explanation is because a large part of video data is unstructured data. In particular, data from speech and text. For example, text extracted from a 30 minutes news segment could cover multiple topics and events, mentioned numerous places and persons. To add to the complexities, we have to time-aligned when these words are spoken. In many ways, text (e.g. slide presentations that appear in videos) are the same.

Thus, we have to answer 2 key question:

1. How do we meet sense of ‘messy’ video data?
2. How can we extract knowledge or intelligence from that mess?

The answer lies in another form of Artificial Intelligence (A.I.) - the study of Natural Language Processing (NLP). That is because it can process and attempt to make sense of unstructured text in the following areas:

• Topic detection
• Key phrase extraction
• Sentiment analysis

The reason is because NLP can be used to turn unstructured video data into structured data. Only then can we start making sense and manipulating the data into either intelligence or actionable items like alerts, triggers, etc.

The field of Video Big Data is just starting. Without the advancement in multiple areas of Artificial Intelligence in multiple areas (Speech Recognition, Computer Vision, Facial Analysis, Text Analytics, etc.), Video Big Data wouldn’t even exist as it needs these fields to work in tandem or in sequence.

Given the rate that we are producing videos, alongside our ability to extract video data using A.I. The only way is up and we are not even close to uncovering the tip of Video Big Data iceberg.

Video Big Data will be bigger than BIG. 

VideoSpace will be right in the middle of it all. Let’s put this prediction into a time capsule and revisit it in a few years.

Video Big Data (Part I) - An Introduction

Fact: YouTube sees more than 300 hours of videos uploaded every minute. That's 18,000 years worth of videos in a year. And that's just YouTube ONLY! If we add all other videos in the public domain, we wouldn't even know where to start with the numbers. 

However, the even bigger numbers are actually hidden in the private domain from sources like broadcasters, media companies, CCTVs, GoPros, bodycams, smart devices, etc. We are recording videos at an unprecedented pace and scale. 

There is one word to describe this - BIG!

Which brings us to Video Big Data. Or should I say the lack of it. Even the term "Video Big Data" is rarely heard of. The reason is pretty simple - this stems from the inability to extract video data and making sense of it. But there is so much information embedded inside videos that is waiting to be discovered, it's an absolute goldmine! 

So the real question is... how can we extract value from videos?

However, the problem with video is that it is the most difficult medium to work with. There are a few reasons why: 

• There are so many elements inside a video (speech, text, faces, objects, etc)
• It is not static.
• It is very difficult to extract the various elements of video data. 
• Each video element requires a different data extraction technique.
• It is very difficult to make sense of video data because of its unstructured nature.
• It's expensive to extract data at scale

These problems are real and is preventing the arrival of Age of Video Big Data. But there is hope yet. With substantial use of Artificial Intelligence, VideoSpace is beginning to crack this enigma. 

In the next segment of this "Video Big Data" Series, we will examine how we can tackle these problems and extract value from videos.