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Entity resolution (also known as entity matching) is the process of stitching together data related to the same real-world thing, such as a person, business, or household, so you can better understand how each one relates to one another, reconcile data inconsistencies and merge your data together.

A simple example is to compare whether both a name and a unique ID (e.g., a social security numbers or driver's license number) are the same in two records. Entity resolution is of course more difficult when such unique identifiers are not available, such that various other attributes may need to be analyzed.

Entity resolution is the process that resolves entities and detects relationships. The pipelines perform entity resolution as they process incoming identity records in three phases: recognize, resolve, and relate.

In general, there will be (2p -1) words in the defining relation for a 2k-p fractional factorial. The length of the shortest word in the defining relation is called the resolution of the design.

Resolution: A term which describes the degree to which estimated main effects are aliased (or confounded) with estimated 2-level interactions, 3-level interactions, etc. In general, the resolution of a design is one more than the smallest order interaction that some main effect is confounded (aliased) with.

Resolution is a measure of the degree of confounding in an experimental design. A factor or factor interaction is said to be confounded (or aliased) with another when the individual effects of the factors or factor interactions cannot be separated one from the other because of the limitations of the experimental data.

To ensure good entity resolution for any industry use case, two things must happen: one, the data must be correctly related to the same person or product, and two, the data regarding the person or the product must be accessible in a split second.