babbobox CEO

Video Big Data Whitepaper (FREE download)

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The term "Video Big Data" is rarely heard of. The reasons are pretty simple: 

  1. It's difficult to extract data from videos
  2. It's difficult make sense of unstructured video data

Therefore, it is not an understatement to say that video is the most difficult medium to search and extract intelligence from. However, given the amount of videos are that generated daily in the public domain (e.g. YouTube) and private domain (e.g. broadcasters, CCTV, education, etc.), it is also not an understatement to say that video is the King of Content. 

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.

This Video Big Data Whitepaper aims to explain how we can extract value and intelligence from videos with a 3 step approach:

  1. Extract video data 
  2. Transform unstructured video data
  3. Analyse to data into intelligence 

With this whitepaper, we hope to share some of our knowledge and experiences working with Video Big Data. From our calculations, we estimate that Video Big Data will dwarf Big Data as we know it. Thus, the importance of this whitepaper. We hope you enjoy and benefit from it!

Yours sincerely,

The VideoSpace Team

Bringing AI Video Search to Broadcast Asia

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We are super excited about bringing our A.I. Video Search to Broadcast Asia after starting out in UK, US and China in 2018. It feels so good to be home!

Babbobox CEO, Alex Chan will be talking about "The Age of AI" and how it will transform the entire broadcast and media industry with Video Search, Personalized Content and Video Big Data.

We will also be making a big announcement and showcasing it during the show! We are pretty sure it will blow you away! So do drop by and say Hi!

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.

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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.