| Plan of attack Frequent Items / Heavy Hitters Counting Distinct Elements Clustering items in Streams What are we going to do today? Frequent items – Classical algorithms (2008) | |||||||||||||||
Abstract | |||||||||||||||
| – New approaches for this problem and its extensions. Some questions We see a large number of individual transactions (such as Amazon book sales) – What are the top sellers today? We are monitoring network traffic – Which hosts/subnets are responsible for most of the traffic? (data collection may be distributed) We have a network of satellites monitoring events over large areas – Which areas are experiencing the most activity over a week / day / hour? The problem We will imagine that we are observing a stream of data This stream consists of a sequence of integers in the range 1...U for some upper bound, U We will be interested in which integers occur most frequently within the stream | |||||||||||||||
Details der Publikation | |||||||||||||||
| |||||||||||||||