We describe FastCat Catalogues, a web application that supports researchers studying archival material, such as historians, in exploring and quantitatively analysing the data (transcripts) of archival documents. The application was designed based on real ...

Visualization Recommendation Systems help users discover important insights during data exploration. These systems should understand users' exploration behaviors and goals to suggest relevant visualizations. However, users' mental models constantly ...

We demonstrate the Approximate Selection Query Processing (ASQP-RL) system, which uses Reinforcement Learning to select a subset of a large external dataset to process locally in a notebook during data exploration. Given a query workload over an external ...

Approximate query processing (AQP) is a technique for obtaining approximate answers to queries over large datasets. AQP techniques trade off accuracy for speed, making them ideal for scenarios where exact answers are not required or the cost of obtaining ...

Large low Earth orbit satellite constellations require machine learning methods for enabling autonomy in health keeping of the satellites. Autonomy in health keeping entail's fault detection, isolation and reconfiguration. However, prior to model ...

The concept of “GovTech” has emerged as a business-oriented model and practice for enabling the public sector to take advantage of digital solutions as service towards the citizen, while the private for-profit sector is responsible for innovation, ...

Thicket is an open-source Python toolkit for Exploratory Data Analysis (EDA) of multi-run performance experiments. It enables an understanding of optimal performance configuration for large-scale application codes. Most performance tools focus on a ...

In this work, we develop novel data science methodologies for ensemble performance data that have the potential to uncover orders of magnitude of performance that is unknowingly being left on the table. Building on years of successful performance tool ...

The ability to extract insights from large amounts of data in a timely manner is a crucial problem. Exploratory Data Analysis (EDA) is commonly used by analysts to uncover insights using a sequence of SQL commands and associated visualizations. However, ...

Data-centric NLP is a highly iterative process requiring careful exploration of text data throughout entire model development lifecycle. Unfortunately, existing data exploration tools are not suitable to support data-centric NLP because of workflow ...

Recent advances in automatic code generation have made tools like GitHub Copilot attractive for programmers, as they allow for the creation of code blocks by simply providing descriptive prompts to the AI. While researchers have studied the performance ...

Data collected about routine human activity and mobility is used in diverse applications to improve our society. Robust models are needed to address the challenges of our increasingly interconnected world. Methods capable of portraying the dynamic ...

Choosing the type of chart to represent data may be challenging. Various books and websites provide catalogs of data visualizations to help choose the correct chart, usually taking into account data types and general tasks. However, they often do not ...

Exploratory Data Analysis (EDA) provides guidance to users to help them refine their needs and find items of interest in large volumes of structured data. In this paper, we develop GUIDES, a framework for guided Text-based Item Exploration (TIE). TIE ...

It is widely accepted that data preparation is one of the most time-consuming steps of the machine learning (ML) lifecycle. It is also one of the most important steps, as the quality of data directly influences the quality of a model. In this tutorial, ...

Users often can see from overview-level statistics that some results look "off", but are rarely able to characterize even the type of error. Reptile is an iterative human-in-the-loop explanation and cleaning system for errors in hierarchical data. Users ...

Data science is characterized by evolution: since data science is exploratory, results evolve from moment to moment; since it can be collaborative, results evolve as the work changes hands. While existing tools help data scientists track changes in code,...

We tackle the longstanding question of checking hypotheses on the social Web. In particular, we address the challenges that arise in the context of testing an input hypothesis on many data samples, in our case, user groups. This is referred to as ...

Voice assistants have become hugely popular in the home as domestic and entertainment devices. Recently, there has been a move towards developing them for work settings. For example, Alexa for Business and IBM Watson for Business were designed to improve ...

Hundreds of people dying from heart disease almost every day that is how terrific a delayed diagnosis can be. Living in an advanced era full of intelligent systems, the increasing number of deaths can be reduced. This research paper focuses on the ...