Virtual: https://events.vtools.ieee.org/m/497694

#ieeeday Free Registration (with a Zoom account; you can get one for free if you don't already have it. This requirement is to avoid Zoom bombing. Please sign in using the email address tied to your Zoom account — not necessarily the one you used to register for the event.): https://sjsu.zoom.us/meeting/register/GGH84KjiSTiIr_W1K8MJVg Synopsis: In an era where data never sleeps, streaming algorithms offer a powerful toolkit for extracting meaningful insights from high-velocity data flows. This talk explores some foundational techniques that enable efficient, real-time analytics with minimal memory requirements. The algorithms covered include a clever bit-based strategy for approximating the count of 1s in a sliding window, ideal for binary streams where space efficiency is paramount. Another algorithm helps estimate statistical moments (mean, variance, skewness) using compact sketches, enabling a deeper understanding of stream distributions without storing the entire dataset. One other algorithm identifies trending items with exponential decay, giving more weight to recent data, a crucial method for dynamic environments like social media or sensor networks. Techniques like these form the backbone of intelligent stream processing. Through intuitive examples and practical applications, this session will demystify how these algorithms work, why they matter, and how they can be used to monitor, summarize, and react to data in motion. --------------------------------------------------------------- By registering for this event, you agree that IEEE and the organizers are not liable to you for any loss, damage, injury, or any incidental, indirect, special, consequential, or economic loss or damage (including loss of opportunity, exemplary or punitive damages). The event will be recorded and will be made available for public viewing. Speaker(s): Dr. Vishnu S. Pendyala Virtual: https://events.vtools.ieee.org/m/497694