Data Quality Management in a Database Cluster with Lazy Replication

Abstract We consider the use of a database cluster with lazy replication. In this context, controlling the quality of replicated data based on users’ requirements is important to improve performance. However, existing approaches are limited to a particular aspect of data quality. In this paper, we propose a general model of data quality which makes the difference between “freshness” and “validity” of data. Data quality is expressed through divergence measures from the data with perfect quality. Users can thus specify the minimum level of quality for their queries. This information can be exploited to optimize query load balancing. We implemented our approach in our Refresco prototype. The results show that freshness control can help increase query throughput significantly. They also show significant improvement when freshness requirements are specified at the relation level rather than at the database level.