961 citations · 1.6k across the 32 of their papers we have counts for
22 papers · 1 filter
The Data Station: Combining Data, Compute, and Market Forces
Raul Castro Fernandez, Kyle Chard, Ben Blaiszik +7
This paper introduces Data Stations, a new data architecture that we are designing to tackle some of the most challenging data problems that we face today: access to sensitive data…
Fast and Reliable Missing Data Contingency Analysis with Predicate-Constraints
Xi Liang, Zechao Shang, Aaron J. Elmore +2
Today, data analysts largely rely on intuition to determine whether missing or withheld rows of a dataset significantly affect their analyses. We propose a framework that can produ…
Data Market Platforms: Trading Data Assets to Solve Data Problems
Raul Castro Fernandez, Pranav Subramaniam, Michael J. Franklin
Data only generates value for a few organizations with expertise and resources to make data shareable, discoverable, and easy to integrate. Sharing data that is easy to discover an…
BoostClean: Automated Error Detection and Repair for Machine Learning
Sanjay Krishnan, Michael J. Franklin, Ken Goldberg +1
Predictive models based on machine learning can be highly sensitive to data error. Training data are often combined with a variety of different sources, each susceptible to differe…
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models
Sanjay Krishnan, Jiannan Wang, Eugene Wu +2
Data cleaning is often an important step to ensure that predictive models, such as regression and classification, are not affected by systematic errors such as inconsistent, out-of…
Asynchronous Complex Analytics in a Distributed Dataflow Architecture
Joseph E. Gonzalez, Peter Bailis, Michael I. Jordan +4
Scalable distributed dataflow systems have recently experienced widespread adoption, with commodity dataflow engines such as Hadoop and Spark, and even commodity SQL engines routin…