Grouping techniques for update propagation in intermittently connected databases

Abstract

We consider an environment where one or more servers carry databases that are of interest to a community of clients. The clients are only intermittently connected to the server for brief periods of time. Clients carry a part of the database for their own processing and accumulate local updates while disconnected. We call this the Intermittently Connected Database (ICDB) environment. ICDBs have a wide variety of applications including sales force automation, insurance claim processing, and mobile workforces. Our focus is on the problem of update propagation at the server in ICDBs and the associated processing at the clients. The typical client-centric approach involves the communication and processing of updates and transactions on a per-client basis, ignoring the overlap of data between clients. The complexity of this approach is in the order of the number of connecting clients, thereby limiting the scalability of the server. We propose a data-centric approach which clusters data into groups and assigns to each client one or more of these groups. The proposed scheme results in server processing complexity on the order of the number of groups, which we control. We propose various techniques for grouping and discuss the processing required at the clients to enable the grouping approach. While the client-centric approach is expected to significantly degrade with the increasing number of clients, we expect that a properly designed grouping scheme will sustain a number of clients that is significantly larger. A prototype has been developed and performance studies are in progress.