Eikosstatistics & data science

Machine Learning & Artificial Intelligence · Big Data & Distributed Computing

Big Data: Real-time data stream analysis (data streams)

Public introduction · full guide in Eikos

Introduction

What this guide covers

Modeling of stochastic flows, probabilistic sublinear memory algorithms (Sketches), streaming data structures and online learning in the face of concept drift.

Overview

Topics covered

  • Introduction
  • Analysis of Real-Time and Infinite Data Flows
  • Mathematical and Theoretical Foundations of Data Streams
  • 1.1 Mathematical Definition of a Data Flow
  • 1.2 Streaming Model Restrictions
  • 1.3 Streaming Computing Paradigms: Cash-Register and Turnstile

Related concepts

Main connections

  • Linear algebra & matrices
  • Calculus & differentiation
  • Mean & expected value
  • Unsupervised learning & clustering
  • Quantiles & percentiles
  • Median & robust location

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

S=⟨(e1,t1),(e2,t2),(e3,t3),…⟩
View LaTeX sourceS = \langle (e_1, t_1), (e_2, t_2), (e_3, t_3), \dots \rangle