Managing Periodically Updated Data in Relational Databases: A Stochastic Modeling Approach

TL;DR

This paper introduces a stochastic modeling approach to manage data obsolescence in relational databases with periodic updates, aiding in scheduling transcriptions for efficient data consistency.

Contribution

It presents a novel stochastic modeling framework for data content evolution in databases, considering referential integrity and various update scenarios.

Findings

Validated the insertion model with real data experiments

Proposed transcription protocols based on the stochastic model

Framework accommodates common database update scenarios

Abstract

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 the reduction of consistency over time between a relation and its replica, termed obsolescence of data. 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 life spans 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 which make use of the proposed stochastic model.