Our focus in the paper is on the problem of update propagation at the server in ISDBs and the associated processing at the clients. The typical client-based approach involves the communication and processing of updates and transactions on a per client basis. The complexity of this approach is on the order of the number of connecting clients, thereby limiting the ultimate scalability of the server with an increasing number of clients. A single data fragment may be assigned to multiple data-groups as well. The proposed scheme results in server processing complexity on the order of the number of groups.
Clients receive updates for the groups they belong to, and filter out the irrelevant data.
In this talk we propose various techniques for aggregation and discuss the processing required at the clients to enable the aggregation approach. The per-client approach is expected to degrade with the increasing number of clients. In contrast, we expect that a properly designed aggregation scheme will sustain a number of clients that is an order of magnitude larger. The talk will propose a heuristic approach as well as a mathematical model to deal with the problem of data grouping. We have also implemented a multicasting protocol to go with this approach for improved performance.