In this review, we discuss the role of sensor analytics point solutions (SNAPS), a reduced
complexity machine-assisted decision support tool. We summarize the approaches used for …

Statistical knowledge and domain expertise are key to extract actionable insights out of data,
yet such skills rarely coexist together. In Machine Learning, high-quality results are only …

© 2018 VLDB Endowment. In order to democratize data science, we need to fundamentally
rethink the current analytics stack, from the user interface to the “guts.“Most importantly …

Machine learning (ML) has become integral to fields like healthcare, finance, and
autonomous systems, but developing robust models requires significant computational …

Data preparation is crucial in achieving optimized results for machine learning (ML).
However, having a good data preparation pipeline is highly non-trivial for ML practitioners …

Outlier detection is critical in real world. Due to the existence of many outlier detection
techniques which often return different results for the same data set, the users have to …

Conversational interfaces require two types of curation: data curation by data science
workers and content curation by domain experts. Recent years have seen the possibilities …

In the age big data, uncertainty in data constantly grows with its volume, variety, and velocity.
Data is noisy, biased and error-prone; blindly applying even the most advanced data …

Many industries are now investing heavily in data science and automation to replace
manual tasks and/or to help with decision making, especially in the realm of leveraging …

Monte Carlo simulation studies are used to examine how eight factors impact predictions of
a binary target outcome in data science pipelines:(1) the choice of four DMMs [Logistic …