  We present a comprehensive set of conditions and rules to control the
correctness of aggregation queries within an interactive data analysis session.
The goal is to extend self-service data preparation and BI tools to
automatically detect semantically incorrect aggregate queries on analytic
tables and views built by using the common analytic operations including
filter, project, join, aggregate, union, difference, and pivot. We introduce
aggregable properties to describe for any attribute of an analytic table which
aggregation functions correctly aggregates the attribute along which sets of
dimension attributes. These properties can also be used to formally identify
attributes which are summarizable with respect to some aggregation function
along a given set of dimension attributes. This is particularly helpful to
detect incorrect aggregations of measures obtained through the use of
non-distributive aggregation functions like average and count. We extend the
notion of summarizability by introducing a new generalized summarizability
condition to control the aggregation of attributes after any analytic
operation. Finally, we define propagation rules which transform aggregable
properties of the query input tables into new aggregable properties for the
result tables, preserving summarizability and generalized summarizability.
