SEARCH
ISTC-CC NEWSLETTER
RESEARCH HIGHLIGHTS
Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
ISTC-CC provides a listing of useful benchmarks for cloud computing.
Another list highlighting Open Source Software Releases.
Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
ISTC-CC Abstract
Knowing When You're Wrong: Building Fast and Reliable Approximate Query Processing Systems
Proceedings of ACM SIGMOD (SIGMOD’14), June 2014.
Sameer Agarwal†, Henry Milner†, Ariel Kleiner†, Ameet Talwalkar†, Barzan Mozafari†, Michael Jordan‡, Samuel Madden*, Ion Stoica†
† UC Berkeley
‡ Massachusetts Institute of Technology
* University of Michigan, Ann Arbor
Modern data analytics applications typically process massive amounts of data on clusters of tens, hundreds, or thousands ofmachines to support near-real-time decisions.The quantity of data and limitations of disk and memory bandwidth often make it infeasible to deliver answers at interactive speeds. However, it has been widely observed that many applications can tolerate some degree of inaccuracy. This is especially true for exploratory queries on data, where users are satisfied with “close-enough” answers if they can come quickly. A popular technique for speeding up queries at the cost of accuracy is to execute each query on a sample of data, rather than the whole dataset. To ensure that the returned result is not too inaccurate, past work on approximate query processing has used statistical techniques to estimate “error bars” on returned results. However, existing work in the sampling-based approximate query processing (S-AQP) community has not validated whether these techniques actually generate accurate error bars for real query workloads. In fact, we find that error bar estimation often fails on real world production workloads. Fortunately, it is possible to quickly and accurately diagnose the failure of error estimation for a query. In this paper, we showthat it is possible to implement a query approximation pipeline that produces approximate answers and reliable error bars at interactive speeds.
FULL PAPER: pdf
