Abstract: Through proactive structured dataset management, suggestions are made for dataset enrichment, cleansing, and other enhancements. Users need not go looking for potentially helpful datasets online or in an enterprise filesystem, because relevant datasets and services will be automatically suggested, based on a samplex cast from the user's dataset. Enrichment may add typed attributes, rows, and/or cell values from a matching dataset, may cleanse data, and may add another dataset which has data about the same entity as the original dataset. Proactive dataset and service matching occurs inside a security perimeter to protect confidentiality. Confidence in suggested modifications may be shown by color differences. Modifications may be previewed, and kept or reverted after acceptance. Suggestions are made in from-example or from-scratch scenarios.

Abstract: Through proactive structured dataset management, suggestions are made for dataset enrichment, cleansing, and other enhancements. Users need not go looking for potentially helpful datasets online or in an enterprise filesystem, because relevant datasets and services will be automatically suggested, based on a samplex cast from the user's dataset. Enrichment may add typed attributes, rows, and/or cell values from a matching dataset, may cleanse data, and may add another dataset which has data about the same entity as the original dataset. Proactive dataset and service matching occurs inside a security perimeter to protect confidentiality. Confidence in suggested modifications may be shown by color differences. Modifications may be previewed, and kept or reverted after acceptance. Suggestions are made in from-example or from-scratch scenarios.