In computer science, streaming algorithms are algorithms for processing data streams in which the input is presented as a sequence of items and can be examined in only a few passes, typically just one.

What are Streaming algorithms? •Algorithms for processing data streams •Input is presented as a sequence of items •Can be examined in only a few passes (typically just one) •Limited working memory 4

Algorithms take only a single pass over their input. Compute various functions using resources that are sublinear in the size of input. Only some approximation to the actual results. Good news - there are algorithms which give you very good bounds.

A streaming algorithm is an algorithm that receives its input as a \stream" of data, and that proceeds by making only one pass (or a small number of passes) through the data.

Sampling and Sketching are two basic techniques for designing streaming algorithms. Most sampling-based algorithms follow the same framework: Algorithm A chooses every coming item ` with a certain probability. If item ` is sampled, then algorithm A puts ` into the memory M[A], otherwise item ` is discarded.

I will discuss the emerging area of algorithms for processing data streams and associated applications, as an applied algorithms research agenda. That has its benefits: w e can be inspired by any application to study

-Introduce the data streaming model, and its concerns. -Analyze an algorithm for heavy hitters in the arrivals-only model. -Analyze an algorithm for heavy hitters with both arrivals and departures.

An emerging area in Computer Science is that of streaming algorithms, referring to very e cient algorithms that process very large amounts of data by making one or more passes over the data and recording very compact summaries, or sketches of the data.

We describe such a streaming algorithm that effectively clusters large data streams. We also provide empirical evidence of the algorithm’s performance on synthetic and real data streams. For many recent applications, the concept of a data stream is more appropriate than a data set.

The data streaming model involves processing a finite sequence of n integers drawn from a finite domain of size m. However, unlike traditional datasets, this sequence is not readily available for random access. Instead, the data arrives incrementally in the form of a continuous ’stream,’ with each integer being presented one at a time.