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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.
- Why stream processing is important
- Hash functions, probability, and fundamental data structures
- Data stream model
- Data stream algorithms: Sampling, sketching, distinct items, frequent items, frequency moments, etc.
- Data stream mining: clustering, histograms, query tracking
- Graph streams: connectivity, matchings, covers
Intended learning outcomes
On completion of this subject the student is expected to:
- Design streaming algorithms and data structures for fundamental problems and variants
- Conduct mathematical analysis of such algorithms and data structures
- Implement efficient schemes for streamed large data sets
- Reason about, contrast and compare streaming methods with those for random-access, disk-bound, and parallel computation
On completion of this subject students should have the following skills:
- Ability to apply knowledge of science and engineering fundamentals
- Ability to communicate effectively, with the team and with the community at large
- Capacity for lifelong learning and professional development
- Profound respect for truth and intellectual integrity, and for the ethics of scholarship
Last updated: 29 April 2020