Measures in SQL

TL;DR

This paper introduces a new 'measure' column type in SQL that enables composable, reusable calculations within tables, combining the power of multidimensional languages with SQL semantics.

Contribution

It proposes a novel measure concept for SQL, along with context-sensitive expressions and evaluation semantics, enhancing SQL's expressiveness for business intelligence.

Findings

Measures are composable and closed in SQL queries.

Measures can be expanded into simple SQL expressions.

The approach retains SQL semantics while supporting multidimensional calculations.

Abstract

SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries. SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL. To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.