The complexity of querying indefinite data about linearly ordered domains
PODS '92 Proceedings of the eleventh ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem
Data Mining and Knowledge Discovery
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Quality Management using Business Process Modeling
SCC '06 Proceedings of the IEEE International Conference on Services Computing
Equivalence among relational expressions with the union and difference operation
VLDB '78 Proceedings of the fourth international conference on Very Large Data Bases - Volume 4
Principles of Model Checking (Representation and Mind Series)
Principles of Model Checking (Representation and Mind Series)
Query rewriting and answering under constraints in data integration systems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Automatic verification of data-centric business processes
BPM'11 Proceedings of the 9th international conference on Business process management
Foundations of relational artifacts verification
BPM'11 Proceedings of the 9th international conference on Business process management
Verification of relational data-centric dynamic systems with external services
Proceedings of the 32nd symposium on Principles of database systems
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Data completeness is an essential aspect of data quality, and has in turn a huge impact on the effective management of companies. For example, statistics are computed and audits are conducted in companies by implicitly placing the strong assumption that the analysed data are complete. In this work, we are interested in studying the problem of completeness of data produced by business processes, to the aim of automatically assessing whether a given database query can be answered with complete information in a certain state of the process. We formalize so-called quality-aware processes that create data in the real world and store it in the company's information system possibly at a later point. We then show how one can check the completeness of database queries in a certain state of the process or after the execution of a sequence of actions, by leveraging on query containment, a well-studied problem in database theory.