Scalable Update Propagation in Partially Replicated, Disconnected Client Server Databases

Modern databases allow mobile clients, that subscribe to replicated data, to process the replica forgoing continuous connectivity, and to receive the updates while connected to the server. Based on the overlap in client interest pattern, the server can do update processing for manageable number of data-groups instead of perclient basis, and hence decouple the update processing cost from the client population. In this paper, we propose an efficient update propagation method that can be applied to a relational database system irrespective of its inherent data organization. We present computationally efficient algorithms for group design and maintenance based on a heuristic function. We provide experimental results that demonstrate that our approach achieves a significant increase in overall scalability over the client-centric approach.

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