Efficient Monitoring and Querying of Distributed, Dynamic Data via Approximate Replication

Christopher Olston and Jennifer Widom


It is increasingly common for an application's data to reside at multiple disparate locations, while the application requires centralized access to its data. A simple solution is to replicate all relevant data at a central point, forwarding updates from master copies to replicas without any special processing or filtering along the way. This scheme maintains up-to-date centralized data, but incurs signficant communication overhead when the data is highly dynamic, because the volume of updates is large. If communication resources are precious, communication can be reduced by prioritizing and filtering updates inside the network, at or near the data sources. When updates are dropped, the replicas become approximate rather than exact. Fortunately, many real-world applications involving distributed, dynamic data can tolerate approximate data values to some extent, so approximate replication is an important technique for balancing replica precision against the communication resources to achieve it.

This paper studies the problem of making efficient use of communication resources in approximate replication environments. After motivating and formalizing the problem, high-level descriptions of several complementary solution are provided. The details of these solution are found in previous papers by the authors, which are referenced here. This paper is intended to serve primarily as an introduction to and roadmap for the authors' prior work on approximate replication, as well as providing a significant bibliography of related work.

Invited Journal Article (IEEE Data Engineering Bulletin, March 2005): [PDF]