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SliceLens is a tool for exploring labeled, tabular, machine learning datasets. To explore a dataset, the user selects combinations of features in the dataset that they are interested in. The tool splits those features into bins and then visualizes the ...

The effective reporting of climate hazards, such as flash floods, hurricanes, and earthquakes, is critical. To quickly and correctly assess the situation and deploy resou rces, emergency services often rely on citizen reports that must be timely, ...

Using GUI-based workflows for data analysis is an iterative process. During each iteration, an analyst makes changes to the workflow to improve it, generating a new version each time. The results produced by executing these versions are materialized to ...

Efforts to scale spreadsheets either follow a 'virtual' strategy that layers a spreadsheet interface on top of an existing database engine or a 'materialized' strategy based on re-engineering a spreadsheet engine. Because databases are not optimized for ...

Algorithmic rankers are ubiquitously applied in automated decision systems such as hiring, admission, and loan-approval systems. Without appropriate explanations, decision-makers often cannot audit or trust algorithmic rankers' outcomes. In recent years,...

Computational notebooks promote exploration by structuring code, output, and explanatory text, into cells. The input code and rich outputs help users iteratively investigate ideas as they explore or analyze data. The links between these cells--how the ...

Building interactive data interfaces is hard because the design of an interface depends on the data processing needs for the underlying analysis task, yet we do not have a good representation for analysis tasks. To fill this gap, this paper advocates ...

Analysts often struggle with analyzing data from multiple tables in a database due to their lack of knowledge on how to join and aggregate the data. To address this, data engineers pre-specify "semantic layers" which include the join conditions and "...

The widespread adoption of open datasets across various domains has emphasized the significance of joining and computing their utility. However, the interplay between computation and human interaction is vital for informed decision-making. To address ...

Many online tabular datasets are maintained in centralized repositories and annotated with descriptive tags. These tags are helpful for data practitioners to search and understand tables. However, manually annotating descriptive tags for new tables ...

Open knowledge, including open data and publicly available knowledge bases, offers a rich opportunity for data scientists for analysis and query answering, but comes with big obstacles due to the diverse, noisy, and incomplete nature of its data eco-...

With the goal of identifying common practices in data science projects, this paper proposes a framework for logging and understanding incremental code executions in Jupyter notebooks. This framework aims to allow reasoning about how insights are ...

The importance of data cleaning systems has continuously grown in recent years. Especially for real-time streaming applications, it is crucial, to identify and possibly remove anomalies in the data on the fly before further processing. The main ...