Recomputing Materialized Instances after Changes to Mappings and Data

A major challenge faced by today's information systems is that of evolution as data usage evolves or new data resources become available. Modern organizations sometimes exchange data with one another via declarative mappings among their databases, as in data exchange and collaborative data sharing systems. Such mappings are frequently revised and refined as new data becomes available, new cross-reference tables are created, and corrections are made. A fundamental question is how to handle changes to these mapping definitions, when the organizations each materialize the results of applying the mappings to the available data. We consider how to incrementally recompute these database instances in this setting, reusing (if possible) previously computed instances to speed up computation. We develop a principled solution that performs cost-based exploration of recomputation versus reuse, and simultaneously handles updates to source data and mapping definitions through a single, unified mechanism. Our solution also takes advantage of provenance information, when present, to speed up computation even further. We present an implementation that takes advantage of an off-the-shelf DBMS's query processing system, and we show experimentally that our approach provides substantial performance benefits.