Consistent aggregations in databases with referential integrity errors

A data warehouse integrates tables coming from multiple source databases, where each database has dierent tables, columns with similar content across databases and dierent referential integrity constraints, enforced to dierent compli-ance levels. Some source databases may have more reliable data than others, if referential integrity is more strictly en-forced or their respective logical data model is more compre-hensive. Thus, a query in an integrated database is likely to refer to tables and columns with referential integrity errors. In this work, we improve aggregations to handle referential integrity errors on OLAP databases. Specically, when two tables are joined SQL ignores those tuples with invalid for-eign key values, eectively discarding potentially valuable information. We extend aggregations to return complete answer sets in the sense that no tuple is excluded. Two fam-ilies of extended aggregations are proposed: weighted refer-ential aggregations and full referential aggregations, which return an approximate answer set and perform a dynamic repair, respectively. Finally, we introduce a simple method to improve aggregation accuracy. Experiments analyze ap-proximation accuracy and time performance of our extended aggregations on a synthetic database, comparing them with standard SQL aggregations on databases with varying ref-erential error rates. The extra work to compute extended aggregations is reasonable and approximate answer sets are highly accurate, making our aggregations a good alternative to standard aggregations in a data warehouse. 1