Dataset search: a survey

• Published in: The VLDB journal Vol. 29; no. 1; pp. 251 - 272
• Main Authors: Chapman, Adriane, Simperl, Elena, Koesten, Laura, Konstantinidis, George, Ibáñez, Luis-Daniel, Kacprzak, Emilia, Groth, Paul
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2020; Springer Nature B.V
• Subjects: Computer Science; Data retrieval; Database Management; Datasets; Information retrieval; Questions; Search engines; Special Issue Paper; Dataset retrieval; Information search and retrieval; Dataset search; Dataset
• ISSN: 1066-8888; 0949-877X
• DOI: 10.1007/s00778-019-00564-x

Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta-released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems and discuss what makes dataset search a field in its own right, with unique challenges and open questions. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to tackle these questions as well as immediate next steps that will take the field forward.