Asymmetric Batch Incremental View Maintenance

Incremental view maintenance has found a growing number of applications recently, including data warehousing, continuous query processing, publish/subscribe systems, etc. Batch processing of base table modifications, when applicable, can be much more efficient than processing individual modifications one at a time. In this paper, we tackle the problem of finding the most efficient batch incremental maintenance strategy under a refresh response time constraint; that is, at any point in time, the system, upon request, must be able to bring the view up to date within a specified amount of time. The traditional approach is to process all batched modifications relevant to the view whenever the constraint is violated. However, we observe that there often exists natural asymmetry among different components of the maintenance cost; for example, modifications on one base table might be cheaper to process than those on another base table because of some index. We exploit such asymmetries using an unconventional strategy that selectively processes modifications on some base tables while keeping batching others. We present a series of analytical results leading to the development of practical algorithms that approximate an "oracle algorithm" with perfect knowledge of the future. With experiments on a TPC-R database, we demonstrate that our strategy offers substantial performance gains over traditional deferred view maintenance techniques.