Sensing the Pulse of a Data Stream in Real Time: An IEEE Day Event

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IEEE Computer Society

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#Streaming algorithms, Real-time analytics, High-velocity data, Low-memory computation, Sliding window, Bit-based counting, Binary data streams, Compact sketches, Statistical moments, Mean #variance #skewness, Exponential decay, Trending item detection, Data summarization, Stream distributions, Dynamic data environments, Sensor networks, Social media analytics, Intelligent stream processing, Data in motion, Approximate computing

From counting bits to computing statistical moments on the fly, to spotting trending items using exponential decay, this talk helps understand streaming intelligence. It's a deep dive into the algorithms powering real-time data analytics.

Slides: https://scholarworks.sjsu.edu/oer/17/
More details: https://events.vtools.ieee.org/m/497694

Errata:
1. The number used in the Flajolet-Martin algorithm example is 32 and not 33 as spoken.

2. On the slide titled "Updating the buckets for Stream: 1 0 1 1 0 1 1 1", the steps should be:

4 1 Create bucket (size 1) → Merge two size-1 buckets (1,4), (2,3)
5 0 Ignore → (1,4), (2,3)
6 1 Create bucket (size 1) → (1,6), (1,4), (2,3)
7 1 Create bucket (size 1) → Merge two size-1 buckets (1,7), (2,6), (2,3)
8 1 Create bucket (size 1) → (1,8),(1,7),(2,6),(2,3)