Tutorial 1: IoT, big (input) data, and learning complexity

Summary form only given. The Internet of Things (IoT) comprises objects that have the ability to communicate with other objects that are ultimately connected to the Internet. A majority of IoT objects in use at the moment are RFID (Radio-Frequency Identification) tags. These tags generate a large amount of data due to their real-time communication ability as well as the amount of data generated during each such communication instance. The availability of such rich real-time data is unprecedented in a large number of these applications. While it is nice to have access to such data, it is even more important to be able to process that data for actionable intelligence. Scalability is a significant issue that is faced by learning algorithms that are used in all Big Data applications. We discuss some steps that can be taken to preprocess the data in order to reduce its complexity, and thereby improve ease of learning/analysis.