Fundamentals of Database Systems
Fundamentals of Database Systems
Summarizability in OLAP and Statistical Data Bases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
Static Checking of Dynamically Generated Queries in Database Applications
Proceedings of the 26th International Conference on Software Engineering
An analysis of additivity in OLAP systems
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
Proving the Safety of SQL Queries
QSIC '05 Proceedings of the Fifth International Conference on Quality Software
A practical guide to SQL white-box testing
ACM SIGPLAN Notices
The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming and Delivering Data
Quality Assessment for Embedded SQL
SCAM '07 Proceedings of the Seventh IEEE International Working Conference on Source Code Analysis and Manipulation
Referential integrity quality metrics
Decision Support Systems
Consistency-aware evaluation of OLAP queries in replicated data warehouses
Proceedings of the ACM twelfth international workshop on Data warehousing and OLAP
Evaluating statistical tests on OLAP cubes to compare degree of disease
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Extended aggregations for databases with referential integrity issues
Data & Knowledge Engineering
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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Many database applications and OLAP tools dynamically generate SQL queries involving join operators and aggregate functions and send these queries to a database server for execution. This dynamically generated SQL code normally assumes the underlying tables and columns are clean and lacks the necessary robustness to deal with foreign keys with null and invalid or undefined values that are ubiquitous in databases with inconsistent or incomplete content. The outcome is that at query time, several issues arise mostly as inconsistencies in answer sets, difficult to detect and explain by users of OLAP tools. In this article, we present an automated query rewriting method for automatically generated OLAP queries that are executed over tables with foreign key columns having potentially null or invalid values. Our method is applicable in queries that use join operators and aggregate functions obeying the summarizability property (e.g. sum(), count()). If a user of an OLAP tool wants or requests it, using our method the queries that use join operators may be rewritten and he or she may be warned of the referential integrity condition of the underlying database and the answer sets may present alternative consistent results in the case aggregate functions are involved. Preliminary experimental evaluation shows rewritten queries provide valuable information on referential integrity and take almost the same time as original queries, highlighting efficiency is good and overhead is minimal.