Streaming data or data streams can be defined as an infinite and continuous inflow of data coming at a very high pace. Some examples include continuous data captured in IoT (Internet of Things) devices, Stock tick data, location data in GPS-enabled taxis and more. Streaming Data comes with its set of challenges viz. one-time pass, infinite data, very high speed of data accumulation, limitations of memory and Concept Drift i.e. change in the distribution of incoming data.
Due to these unique challenges of Streaming data analysis, conventional batch processing methods are not effective and there is a need for new methodologies. One way to perform analysis of streaming data is by online learning or incremental learning where the focus is more on the recent data (instead of the older data points) and decisions are taken in real-time or near real-time. So, the objective of Online learning is to give “good decisions fast”. Analysis of Streaming data has applications in various fields such as stock markets, intelligent transportation, healthcare and many more.
Key Takeaways:
The key takeaways for the audience from this talk are:
- Understanding of Streaming data and Online learning
- Why analysis of Streaming data is required and how conventional batch processing methods are not sufficient?
- Understanding of various applications of Streaming data and Online Learning
Check out the video below to know more about the talk.
