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Learn fundamental concepts and techniques in streaming algorithms through this comprehensive tutorial lecture delivered at the International Centre for Theoretical Sciences. Explore how to design and analyze algorithms that process massive data streams using limited memory, covering essential topics such as frequency estimation, distinct element counting, and heavy hitters identification. Discover the mathematical foundations underlying streaming algorithms, including sketching techniques, randomized algorithms, and approximation methods that enable efficient computation on data too large to store in memory. Examine practical applications of streaming algorithms in big data processing, network monitoring, and real-time analytics while understanding the theoretical guarantees and limitations of different approaches. Gain insights into the probabilistic and geometric techniques that make streaming algorithms possible, including the use of hash functions, sampling methods, and dimensionality reduction. Master the analysis of space and time complexity trade-offs inherent in streaming algorithm design, and understand how to prove correctness and approximation bounds for various streaming problems. This tutorial forms part of a broader discussion meeting on the intersection of geometry, probability, and algorithms, providing essential background for understanding how streaming algorithms leverage probabilistic and geometric insights to solve computational challenges in the era of big data.
