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Big Data and Usability

Big Data and Usability. H. V. Jagadish Univ. of Michigan. Scaling. Computing systems can be made to scale. Human capacity does not scale Scaling is used to address Big Data. So solutions only address the computing part and ignore the human. Ex.: Graduate Student Appl.

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Big Data and Usability

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  1. Big Data and Usability H. V. Jagadish Univ. of Michigan

  2. Scaling • Computing systems can be made to scale. • Human capacity does not scale • Scaling is used to address Big Data. • So solutions only address the computing part and ignore the human.

  3. Ex.: Graduate Student Appl. • If you have 5 applications, as a human expert you can form a pretty good judgment. • If you have 5000 applications, what will you do?

  4. How Big is Big Data? • Standard is “too big to handle using current techniques” • Doesn’t have to be Petabytes.

  5. Possible Solution • Have machine replace human. • Can be made to work in some domains. • But need human intelligence in many others. • Have to think about how to get machines to support humans better.

  6. Ex.: Fast Browsing • The Skimmer system [SIGMOD 2012] can be used to browse faster. • Works by adaptively down-sampling rows depending on user’s scroll speed.

  7. Not All Vs Are Equal • Big Data is defined as having: • Volume • Velocity • Variety • (Lack of) Veracity • … • But everyone immediately latches on to size.

  8. Variety >> Volume • Volume is very easy to measure. • Companies (and even system research groups) can easily boast about their accomplishments in handling big volumes of data. • Variety has no accepted metric. • It is possibly a much bigger challenge than size. • Yet gets very little attention.

  9. Where Does Variety Arise? • Across data sets. • Need to integrate many disparate sources of information. • Within one data set. • Ex.: Customers are all different • May be ignored at great peril!

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