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November 6, Tuesday
12:00 – 13:00

This talk will briefly introduce the streaming computational model in the context of data mining. We will focus on working with matrices that are revealed over time and are too large to store. Working with such massive matrices requires creating a concise yet accurate approximation for them. These are called matrix sketches. This talk will shortly survey new results for matrix sketching in two streaming models. In the first, the matrix is presented to the algorithm one entry at a time. Examples include recommender systems whose input is of the form "user i rated item j 3 stars". In the second, the matrix is revealed row by row, for example, "document i contains terms j_1,…,j_k". This is the case in web crawling where the crawler cannot store all the documents it visits.