Data Analysis Expressions
E185656
Data Analysis Expressions is a formula and query language used in Microsoft Power BI, Excel, and other Microsoft BI tools to create custom calculations and analyze data in tabular models.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Data Analysis Expressions canonical | 3 |
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
Microsoft technology
ⓘ
expression language ⓘ formula language ⓘ query language ⓘ |
| abbreviation | DAX ⓘ |
| basedOn | Excel formula syntax ⓘ |
| dataModelType | tabular data model ⓘ |
| developer | Microsoft ⓘ |
| documentationURL | https://learn.microsoft.com/dax ⓘ |
| domain |
business intelligence
ⓘ
data analytics ⓘ |
| executionEngine |
xVelocity in-memory analytics engine
ⓘ
surface form:
VertiPaq
|
| hasFunctionCategory |
aggregation functions
ⓘ
date and time functions ⓘ filter functions ⓘ information functions ⓘ logical functions ⓘ parent-child functions ⓘ statistical functions ⓘ table manipulation functions ⓘ text functions ⓘ time intelligence functions ⓘ |
| influencedBy | SQL ⓘ |
| purpose |
analyze data in tabular models
ⓘ
create custom calculations ⓘ define calculated columns ⓘ define calculated tables ⓘ define measures ⓘ filter and query data ⓘ |
| supportsEvaluation |
columnar storage
ⓘ
in-memory analytics ⓘ |
| supportsFeature |
calculated columns
ⓘ
calculated tables ⓘ context transition ⓘ filter context ⓘ iterator functions ⓘ measures ⓘ relationship navigation ⓘ row context ⓘ time intelligence functions ⓘ |
| typicalUseCase |
enterprise BI
ⓘ
self-service BI ⓘ |
| usedIn |
Azure Analysis Services
ⓘ
Excel ⓘ
surface form:
Microsoft Excel
Power BI ⓘ
surface form:
Microsoft Power BI
Power BI ⓘ
surface form:
Power BI Desktop
Power BI ⓘ
surface form:
Power BI Service
Power Pivot ⓘ SQL Server Analysis Services Tabular ⓘ tabular models ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Data Analysis Expressions Description of subject: Data Analysis Expressions is a formula and query language used in Microsoft Power BI, Excel, and other Microsoft BI tools to create custom calculations and analyze data in tabular models.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.