43 citations · 288 across the 47 of their papers we have counts for
4 papers · 1 filter
Fine-Grained Lineage for Safer Notebook Interactions
Stephen Macke, Hongpu Gong, Doris Jung-Lin Lee +3
Computational notebooks have emerged as the platform of choice for data science and analytical workflows, enabling rapid iteration and exploration. By keeping intermediate program…
Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees
Stephen Macke, Maryam Aliakbarpour, Ilias Diakonikolas +2
Aggregating data is fundamental to data analytics, data exploration, and OLAP. Approximate query processing (AQP) techniques are often used to accelerate computation of aggregates…
Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development
Angela Lee, Doris Xin, Doris Lee +1
It is well-known that the process of developing machine learning (ML) workflows is a dark-art; even experts struggle to find an optimal workflow leading to a high accuracy model. U…
Towards Scalable Dataframe Systems
Devin Petersohn, Stephen Macke, Doris Xin +7
Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the remarkable success of dataframe libraries in Rand Python, dataframes face performance issu…