KIIS COLLOQUIA - MOD 2

This course provides an introduction to the algorithms for processing data streams.
During the course, the students will study the different models of streams and some of the main algorithms for: extracting a sample from a stream, counting the elements that satisfy a property among the last K, filtering the elements of a stream belonging to large sets, counting the distinct elements in a stream, estimating the moments of the elements in a stream, identifying frequent elements (above a threshold, top-k), estimating the frequencies of the elements in a stream. Additionally, stream management systems will be described.

Knowledge of the algorithms described during the course.

Basic knowledge of probability calculation.
Knowledge of a programming language.
Knowledge of methodologies for the analysis of algorithms.

Algoritmi per:
extracting a sample from a stream
counting the elements that satisfy a property among the last K
filtering the elements of a stream belonging to large sets
counting the distinct elements in a stream
estimating the moments of the elements in a stream
identifying frequent elements (above a threshold, top-k)
estimating the frequencies of the elements in a stream
stream management systems

Chapter 4 of
LESKOVEC, Jure; RAJARAMAN, Anand; ULLMAN, Jeffrey David. Mining of massive data sets. Cambridge university press, 2020.
http://www.mmds.org/

Research papers made available by the teacher.

Project at the end of the course

The course will consist of frontal lessons

oral