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Everything about Dataspace

Abstraction in data management

A dataspace is an abstraction in data management that aims to overcome some of the problems encountered in a data integration system. A dataspace is defined as a set of "participants", or data sources, and the relations between them: for example that dataset A is a duplicate of dataset B. It can contain all data sources of an organization regardless of their format, physical location, or data model. The data space then provides a unified interface to query data regardless of format, sometimes in a "best-effort" fashion, and ways to further integrate the data when necessary. It is very different than a traditional relational database, which requires that all data be in the same format. The aim of the concept is to reduce the effort required to set up a data integration system by relying on existing matching and mapping generation techniques, and to improve the system in "pay-as-you-go" fashion as it is used. Labor-intensive aspects of data integration are postponed until they are absolutely needed. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: DataspaceA 5-minute orientation
  2. Skim a recent paper: Empowering Dataspace 4.0: Unveiling Promise of Decentralized Data-SharingInfluential work since 2022
  3. Pick up a classic book: PortalThe most-reprinted book on the subject
  4. Read the landmark paper: Architecture for manufacturing-X: Bringing asset administration shell, eclipse dataspace connector and OPC UA togetherCited 72 times
  5. Go sideways: Data integrationA closely related topic
  6. Go sideways: Semantic heterogeneityA closely related topic

📖 Definitions

Noun A collection of data that is less formally structured than a database.

🗂️ The subject at a glance

  1. History

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics