SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics

By Manasi Vartak, Sajjadur Rahman, Samuel Madden, Aditya G. Parameswaran, Neoklis Polyzotis. 2015

318 citations recorded by the source corpus.

Data analysts often build visualizations as the first step in their analytical workflow. However, when working with high-dimensional datasets, identifying visualizations that show relevant or desired trends in data can be laborious. We propose SeeDB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, SeeDB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most “useful” or “interesting”. The two major obstacles in recommending interesting visualizations are (a) scale: evaluating a large number of candidate visualizations while responding within interactive time scales, and (b) utility: identifying an appropriate metric for assessing interestingness of visualizations. For the former, SeeDB introduces pruning optimizations to quickly identify high-utility visualizations and sharing optimizations to maximize sharing of computation across visualizations. For the latter, as a first step, we adopt a deviation-based metric for visualization utility, while indicating how we may be able to generalize it to other factors influencing utility. We implement SeeDB as a middleware layer that can run on top of any DBMS. Our experiments show that our framework can identify interesting visualizations with high accuracy. Our optimizations lead to multiple orders of magnitude speedup on relational row and column stores and provide recommendations at interactive time scales. Finally, we demonstrate via a user study the effectiveness of our deviation-based utility metric and the value of recommendations in supporting visual analytics.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Automated Machine Learning: State-of-The-Art and Open Challenges (2019, methodology): “seeDB [125] has been introduced as a visualization recommendation engine that eases the analysis of data subsets in an efficient way by showing a large number of recommended visualizations in addition to choosing the appropriate metrics to measure the visualization interest in a near interactive per”
Data Lifecycle Challenges in Production Machine Learning (2018, methodology): “Other metrics that can be used include the L1 and L∞ metrics or Earth Mover’s distance [32, 70].”
Data collection and quality challenges in deep learning: a data-centric AI perspective (2021, background): “SeeDB[164] is a seminal framework that repeatedly generates visualizations of interest.”
VizLinter: A Linter and Fixer Framework for Data Visualization (2021, background): “They derive rules from statistical features [46, 54, 61], visual effectiveness measures [9, 14], and user behaviors [20] or tasks [43].”
Controlling False Discoveries During Interactive Data Exploration (2016, unknown): “Finally, in the context of data exploration there has been recent work on automatically recommending visualization [36, 23, 37] or correlations [8].”
Effortless Data Exploration with zenvisage: An Expressive and Interactive Visual Analytics System (2016, background): “SeeDB [30] recommends visualizations that best display the difference between two sets of data.”