DeepAI AI Chat
Log In Sign Up

Enhancing the Interactivity of Dataframe Queries by Leveraging Think Time

03/03/2021
by   Doris Xin, et al.
0

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.

READ FULL TEXT
07/29/2021

Interactive Region-of-Interest Discovery using Exploratory Feedback

In this paper, we propose a geospatial data management framework called ...
01/21/2021

Modeling and Leveraging Analytic Focus During Exploratory Visual Analysis

Visual analytics systems enable highly interactive exploratory data anal...
12/13/2009

Learning an Interactive Segmentation System

Many successful applications of computer vision to image or video manipu...
06/05/2018

Making Sense of Asynchrony in Interactive Data Visualizations

Asynchronous interfaces allow users to concurrently issue requests while...
06/06/2019

The Architectural Implications of Facebook's DNN-based Personalized Recommendation

The widespread application of deep learning has changed the landscape of...
12/12/2022

Reinforced Approximate Exploratory Data Analysis

Exploratory data analytics (EDA) is a sequential decision making process...
11/19/2021

Improving a High Productivity Data Analytics Chapel Framework

Most state of the art exploratory data analysis frameworks fall into one...