Progressive Data Science: Potential and Challenges

TL;DR

Progressive data science aims to accelerate the iterative data analysis process by providing quick, approximate results at each step, enabling early detection of issues and more efficient refinement.

Contribution

This paper discusses the challenges and potential solutions for implementing progressive computation in various stages of the data science pipeline.

Findings

Progressive approximations can speed up data science workflows.

Early feedback helps detect errors and guide modifications.

Challenges include computing accurate approximations early in the pipeline.

Abstract

Data science requires time-consuming iterative manual activities. In particular, activities such as data selection, preprocessing, transformation, and mining, highly depend on iterative trial-and-error processes that could be sped-up significantly by providing quick feedback on the impact of changes. The idea of progressive data science is to compute the results of changes in a progressive manner, returning a first approximation of results quickly and allow iterative refinements until converging to a final result. Enabling the user to interact with the intermediate results allows an early detection of erroneous or suboptimal choices, the guided definition of modifications to the pipeline and their quick assessment. In this paper, we discuss the progressiveness challenges arising in different steps of the data science pipeline. We describe how changes in each step of the pipeline impact the subsequent steps and outline why progressive data science will help to make the process more effective. Computing progressive approximations of outcomes resulting from changes creates numerous research challenges, especially if the changes are made in the early steps of the pipeline. We discuss these challenges and outline first steps towards progressiveness, which, we argue, will ultimately help to significantly speed-up the overall data science process.