Foresight

Demiralp, Çağatay; Haas, Peter J.; Parthasarathy, Srinivasan; Pedapati, Tejaswini

doi:10.14778/3137765.3137813

Cited by 86 publications

(20 citation statements)

References 9 publications

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“…Systems that propose visualizations could suggest better ways to present user-selected features [20], [21], [22], visually support users in feature selection [23], or suggest interesting features to visualize [24], [25], [26], [27]. For example, Stolper et al [28] offers progressive visual analysis, a paradigm that allows users to explore semantically relevant partial findings in integrated, interactive visualizations as analyses are performed.…”

Section: B Guidance For Visual Data Analysismentioning

confidence: 99%

VALS: Supporting Visual Data Analysis in Longitudinal Clinical Studies

Betancur

Charpak²,

Montealegre‐Pomar³

et al. 2023

IEEE Access

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Visual data analysis helps to understand different types of phenomena by allowing experts to explore for relationships, patterns, outliers, unexpected changes, and more. Experts need tools that help them find useful and actionable information in the data so they can test their hypotheses and come up with new ones. This need becomes more evident in cases of longitudinal studies, where there are usually a large number of variables and the process being analyzed can be complex as well. We present VALS (Visual Analytics in Longitudinal Studies), a framework for visually exploring longitudinal clinical data. VALS includes a data model, a task categorization model, and an approach to guidance through feature engineering techniques and interactive visualizations, all to help analysts perform their analysis tasks. VALS has been designed in collaboration with healthcare experts with experience in longitudinal studies. We have also developed a tool prototype for a case study using real-world datasets. The evidence collected in the case study shows how useful a VALS-based visual analytics tool can be.

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Section: B Guidance For Visual Data Analysismentioning

confidence: 99%

VALS: Supporting Visual Data Analysis in Longitudinal Clinical Studies

Betancur

Charpak²,

Montealegre‐Pomar³

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…More recently, researchers have started to recommend interesting visualizations based on the statistical properties of data and search more deeply into any possible data insights from the data table. For example, Voyager [60] and Voyager 2 [61] recommend visualization charts based on statistical and perceptual measures; DataSite proactively recommends data analysis results with a set of heuristic algorithms [13]; Foresight recommends data visualization ranked within different types of data insights for large scale data [14]; Tang et al [52] and Vartak et al [56] further recommended top-k insights with respect to an importance or interestingness measure; Srinivasan et al explore how system-generated data facts can be illustrated with visualizations [48]. Industry systems such as Microsoft Power BI and Google Sheets [15] also recommend visual charts based on the data insights detected by the insight mining engines.…”

Section: Visualization Recommendationmentioning

confidence: 99%

DataShot: Automatic Generation of Fact Sheets from Tabular Data

Wang

Sun

Zhang

et al. 2020

IEEE Trans. Visual. Comput. Graphics

111

118

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Fact sheets with vivid graphical design and intriguing statistical insights are prevalent for presenting raw data. They help audiences understand data-related facts effectively and make a deep impression. However, designing a fact sheet requires both data and design expertise and is a laborious and time-consuming process. One needs to not only understand the data in depth but also produce intricate graphical representations. To assist in the design process, we present DataShot which, to the best of our knowledge, is the first automated system that creates fact sheets automatically from tabular data. First, we conduct a qualitative analysis of 245 infographic examples to explore general infographic design space at both the sheet and element levels. We identify common infographic structures, sheet layouts, fact types, and visualization styles during the study. Based on these findings, we propose a fact sheet generation pipeline, consisting of fact extraction, fact composition, and presentation synthesis, for the auto-generation workflow. To validate our system, we present use cases with three real-world datasets. We conduct an in-lab user study to understand the usage of our system. Our evaluation results show that DataShot can efficiently generate satisfactory fact sheets to support further customization and data presentation.

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“…Another related area is automated insight generation (auto-insights). An auto-insight system mines a dataset for statistical properties of interest [22,47,50,54,65,67]. Unlike these systems, DataPrep.EDA is a programming-based EDA tool that has several advantages over GUIbased EDA systems including seamless integration in the Python data science ecosystem, and flexibility since the data scientist is not restricted to one GUI's functionalities.…”

Section: Related Workmentioning

confidence: 99%

DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python

Peng

Lockhart

et al. 2021

Proceedings of the 2021 International Conference on Management of Data

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Exploratory Data Analysis (EDA) is a crucial step in any data science project. However, existing Python libraries fall short in supporting data scientists to complete common EDA tasks for statistical modeling. Their API design is either too low level, which is optimized for plotting rather than EDA, or too high level, which is hard to specify more fine-grained EDA tasks. In response, we propose DataPrep.EDA, a novel task-centric EDA system in Python. Dat-aPrep.EDA allows data scientists to declaratively specify a wide range of EDA tasks in different granularity with a single function call. We identify a number of challenges to implement Dat-aPrep.EDA, and propose effective solutions to improve the scalability, usability, customizability of the system. In particular, we discuss some lessons learned from using Dask to build the data processing pipelines for EDA tasks and describe our approaches to accelerate the pipelines. We conduct extensive experiments to compare DataPrep.EDA with Pandas-profiling, the state-of-the-art EDA system in Python. The experiments show that DataPrep.EDA significantly outperforms Pandas-profiling in terms of both speed and user experience. DataPrep.EDA is open-sourced as an EDA component of DataPrep: https://github.com/sfu-db/dataprep.

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Foresight

Cited by 86 publications

References 9 publications

VALS: Supporting Visual Data Analysis in Longitudinal Clinical Studies

VALS: Supporting Visual Data Analysis in Longitudinal Clinical Studies

DataShot: Automatic Generation of Fact Sheets from Tabular Data

DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python

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