Enacting Data Science Pipelines for Exploring Graphs: From Libraries to Studios

Abstract

We argue for the need of a new generation of data science solutions that can democratize recent advances in data engineering and artificial intelligence for non-technical users from various disciplines, enabling them to unlock the full potential of these solutions. To do so, we adopt an approach whereby computational creativity and conversational computing are combined to guide non-specialists intuitively to explore and extract knowledge from data collections. The paper introduces MATILDA, a creativity-based data science design platform, showing how it can support the design process of data science pipelines guided by human and computational creativity.

Towards a human-in-the-loop curation: A qualitative perspective

“…Curation approaches [29], [30] and tagging tools provide partial automatic solutions for managing research data and associated knowledge. Existing curation solutions have mainly proposed approaches to extracting structural and quantitative metadata, vocabularies for textual documents, or elements of interest when they adopt linguistic perspectives used in libraries.…”

Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium

“…The last paper, "Enacting Data Science Pipelines for Exploring Graphs: From Libraries to Studios" [29] provides an overview of data science pipeline libraries, IDE, and studios combining classic and artificial intelligence operations to query, process, and explore graphs. Then, data science pipeline environments are introduced and compared.…”