“Two Computational Paradigms for Big Data”


This talk will cover two non-conventional computational models for analyzing big data.

The first is data streams: in this model, data arrives in a stream and the algorithm is tasked with computing a function of the data without explicitly storing it.

The second is map-reduce: in this model, data is distributed across many machines and computation is done as sequence of map and reduce operations.

We will present a few algorithms in these models and discuss their scalability.