Optimization of Monotonic Linear Progressive Queries Based on Dynamic Materialized Views

There is an increasing demand to efficiently process emerging types of queries, such as progressive queries (PQs), from contemporary database applications including telematics, e-commerce and social media. Unlike conventional queries, a PQ consists of a set of step-queries (SQ). A user formulates a new SQ on the fly based on the result(s) from the previous SQ(s). Existing database management systems were not designed to efficiently process such queries. In this paper, we present a novel technique to efficiently process a special type of PQ, called monotonic linear PQs, based on dynamically materialized views. The key idea is to create a superior relationship graph for SQs from historical PQs that can be used to estimate the benefit of keeping the current SQ result as a materialized view. The materialized views are used to improve the performance of future SQs. A new storage structure for the materialized views set is designed to facilitate efficient search for a usable view to answer a given SQ. Algorithms/strategies to efficiently construct a superior relationship graph, dynamically select materialized views, effectively manage the materialized views set and efficiently search for usable views are discussed. Experiment results demonstrate that our proposed technique is quite promising.