Managing periodically updated data in relational databases: A stochastic modeling approach

Recent trends in information management involve the periodic transcription of data onto secondary devices in a networked environment, and the proper scheduling of these transcriptions is critical for efficient data management. To assist in the scheduling process, we are interested in modeling data obsolescence, that is, the reduction of consistency over time between a relation and its replica. The modeling is based on techniques from the field of stochastic processes, and provides several stochastic models for content evolution in the base relations of a database, taking referential integrity constraints into account. These models are general enough to accommodate most of the common scenarios in databases, including batch insertions and lifespans both with and without memory. As an initial "proof of concept" of the applicability of our approach, we validate the insertion portion of our model framework via experiments with real data feeds. We also discuss a set of transcription protocols that make use of the proposed stochastic model.