Year: 2021
Type: preprint
Abstract: We propose opportunistic evaluation, a framework for accelerating interactions with dataframes. Interactive latency is critical for iterative, human-in-the-loop dataframe workloads for supporting exploratory data analysis. Opportunistic evaluation significantly reduces interactive latency by 1) prioritizing computation directly relevant to the interactions and 2) leveraging think time for asynchronous background computation for non-critical operators that might be relevant to future interactions. We show, through empirical analysis, that current user behavior presents ample opportunities for optimization, and the solutions we propose effectively harness such opportunities.
Source: arXiv (Cornell University)
Authors: Xin, Doris, Petersohn, Devin, Tang, Dixin, Wu, Yifan, Gonzalez, Joseph E., Hellerstein, Joseph M., Joseph, Anthony D., Parameswaran, Aditya G.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3135035073