In light of the growing popularity of Exploratory Data Analysis (EDA), understanding the
underlying causes of the knowledge acquired by EDA is crucial. However, it remains under …

Abstract Machine learning (ML) has already fundamentally changed several businesses.
More recently, it has also been profoundly impacting the computational science and …

When analyzing large datasets, analysts are often interested in the explanations for
unexpected results produced by their queries. In this work, we focus on aggregate SQL …

cData provenance, or data lineage, describes the life cycle of data. In scientific workflows on
HPC systems, scientists often seek diverse provenance (eg, origins of data products, usage …

In many data analysis applications there is a need to explain why a surprising or interesting
result was produced by a query. Previous approaches to explaining results have directly or …

Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on
HPC systems, scientists often seek diverse provenance (eg, origins of data products, usage …

Humans reason about the world around them by seeking to understand why and how
something occurs. The same principle extends to the technology that so many of human …

Quantifying the contribution of database facts to query answers has been studied as means
of explanation. The Banzhaf value, originally developed in Game Theory, is a natural …

Porting a scientific data analysis workflow (DAW) to a cluster infrastructure, a new software
stack, or even only a new dataset with some notably different properties is often challenging …

When exploring a new dataset, Data Scientists often apply analysis queries, look for insights
in the resulting dataframe, and repeat to apply further queries. We propose in this paper a …