29 citations · 110 across the 17 of their papers we have counts for
14 papers · 1 filter
Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities
Doris Xin, Hui Miao, Aditya Parameswaran +1
Machine learning (ML) is now commonplace, powering data-driven applications in various organizations. Unlike the traditional perception of ML in research, ML production pipelines a…
Enhancing the Interactivity of Dataframe Queries by Leveraging Think Time
Doris Xin, Devin Petersohn, Dixin Tang +5
We propose opportunistic evaluation, a framework for accelerating interactions with dataframes. Interactive latency is critical for iterative, human-in-the-loop dataframe workloads…
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…
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…
Helix: Holistic Optimization for Accelerating Iterative Machine Learning
Doris Xin, Stephen Macke, Litian Ma +3
Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved. Un…
ShapeSearch: A Flexible and Efficient System for Shape-based Exploration of Trendlines
Tarique Siddiqui, Zesheng Wang, Paul Luh +2
Identifying trendline visualizations with desired patterns is a common and fundamental data exploration task. Existing visual analytics tools offer limited flexibility and expressi…