G-OLA: Generalized Online Aggregation for Interactive Analysis on Big Data
Nearly 15 years ago, Hellerstein, Haas and Wang proposed online aggregation (OLA), a technique that allows users to (1) observe the progress of a query by showing iteratively refined approximate answers, and (2) stop the query execution once its result achieves the desired accuracy. In this demonstration, we present G-OLA, a novel mini-batch execution model that generalizes OLA to support general
Rethinking Data-Intensive Science Using Scalable Analytics Systems
“Next generation” data acquisition technologies are allowing scientists to collect exponentially more data at a lower cost. These trends are broadly impacting many scientific fields, including genomics, astronomy, and neuroscience. We can attack the problem caused by exponential data growth by applying horizontally scalable techniques from current analytics systems to accelerate scientific process
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http://yellowstone.cs.ucla.edu/~yang/paper/sigmod2016-p958.pdf
https://cs.uwaterloo.ca/~ilyas/papers/ZuhairSIGMOD2015.pdf
