Dealing with data streams: An online, row-by-row estimation tutorial

G.J.E. Ippel*, M.C. Kaptein, J.K. Vermunt

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)

Abstract

Novel technological advances allow distributed and automatic measurement of human behavior. While these technologies provide exciting new research opportunities, they also provide challenges: datasets collected using new technologies grow increasingly large, and in many applications the collected data are continuously augmented. These data streams make the standard computation of well-known estimators inefficient as the computation has to be repeated each time a new data point enters. In this tutorial paper, we detail online learning, an analysis method that facilitates the efficient analysis of Big Data and continuous data streams. We illustrate how common analysis methods can be adapted for use with Big Data using an online, or ``row-by-row'', processing approach. We present several simple (and exact) examples of the online estimation and we discuss Stochastic Gradient Descent as a general (approximate) approach to estimate more complex models. We end this article with a discussion of the methodological challenges that remain.
Original languageEnglish
Pages (from-to)124-138
JournalMethodology: European Journal of Research Methods for the Behavioral and Social Sciences
Volume12
Issue number4
DOIs
Publication statusPublished - 2017

Keywords

  • big data
  • Data streams
  • online learning
  • machine learning
  • stochastic gradient descent

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