Updating derived relations: detecting irrelevant and autonomously computable updates
ACM Transactions on Database Systems (TODS)
A decision procedure for conjunctive query disjointness
PODS '89 Proceedings of the eighth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Behavior of database production rules: termination, confluence, and observable determinism
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Incremental Computation of Time-Varying Query Expressions
IEEE Transactions on Knowledge and Data Engineering
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Choosing a View Update Translator by Dialog at View Definition Time
VLDB '86 Proceedings of the 12th International Conference on Very Large Data Bases
Deriving Production Rules for Incremental View Maintenance
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
Expiration of Historical Databases
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
Extending Relational Database Systems to Automatically Enforce Privacy Policies
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Triggers over nested views of relational data
ACM Transactions on Database Systems (TODS)
Threats to privacy in the forensic analysis of database systems
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
On the updatability of relational views
VLDB '78 Proceedings of the fourth international conference on Very Large Data Bases - Volume 4
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Trustworthy vacuuming and litigation holds in long-term high-integrity records retention
Proceedings of the 13th International Conference on Extending Database Technology
RELAW '09 Proceedings of the 2009 Second International Workshop on Requirements Engineering and Law
Efficient audit-based compliance for relational data retention
Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security
Auditing a database under retention policies
The VLDB Journal — The International Journal on Very Large Data Bases
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The recent introduction of several pieces of legislation mandating minimum and maximum retention periods for corporate records has prompted the Enterprise Content Management (ECM) community to develop various records retention solutions. Records retention is a significant subfield of records management, and legal records retention requirements apply over corporate records regardless of their shape or form. Unfortunately, the scope of existing solutions has been largely limited to proper identification, classification and retention of documents, and not of data more generally. In this paper we address the problem of managed records retention in the context of relational database systems. The problem is significantly more challenging than it is for documents for several reasons. Foremost, there is no clear definition of what constitutes a business record in relational databases; it could be an entire table, a tuple, part of a tuple, or parts of several tuples from multiple tables. There are also no standardized mechanisms for purging, anonymizing and protecting relational records. Functional dependencies, user defined constraints, and side effects caused by triggers make it even harder to guarantee that any given record will actually be protected when it needs to be protected or expunged when the necessary conditions are met. Most importantly, relational tuples may be organized such that one piece of data may be part of various legal records and subject to several (possibly conflicting) retention policies. We address the above problems and present a complete solution for designing, managing, and enforcing records retention policies in relational database systems. We experimentally demonstrate that the proposed framework can guarantee compliance with a broad range of retention policies on an off-the-shelf system without incurring a significant performance overhead for policy monitoring and enforcement.