Stream Computing and Applications

AIM With exponential growth in data generated from sensor data streams, search engines, spam filters, medical services, online analysis of financial data streams, and so forth, there is demand for fast monitoring and storage of huge amounts of data in real-time. Traditional technologies were not aimed to such fast streams of data. Usually they required data to be stored and indexed before it could be processed. Stream computing was created to tackle those problems that require processing and classification of continuous, high volume of data streams. It is highly used on applications such as Twitter, Facebook, High Frequency Trading and so forth. This subject will focus on the algorithms and data structures behind the analysis and management of streams. Theoretical underpinnings are emphasized, with implementation of some fundamental algorithms.

• 1Design streaming algorithms and data structures for fundamental problems and variants
• 2Conduct mathematical analysis of such algorithms and data structures
• 3Implement efficient schemes for streamed large data sets
• 4Reason about, contrast and compare streaming methods with those for random-access, disk-bound, and parallel computation