Optimizing Queries with Expensive Video Predicates Based on Estimation of Attribute Cardinality

With rapid advances in video processing technologies, video data increased rapidly and becomes popular in our daily life for both professional and consumer applications, e.g., surveillance, education, entertainment. Such needs require the data management system not only can store and access the video content, but also able to optimize the queries with expensive video predications in an effective and efficient way. In previous research literature, query optimizations in relational database systems (RDBMS) are often based on disk I/O cost of involved operations. However, for a query that contains expensive video predicates, traditional cost estimation model does not work well. Although researchers have proposed some approaches which can solve the problem in certain situations, there are still some unresolved issues, and it needs further optimization. In this paper, motivate from a real-world large supermarket´s business data and video surveillance data management scenario, through considering the characteristics of video data and its expensive processing, we propose a novel query optimization approach that caches operators´ results and reconstructs the join order based estimation for attribute cardinality. This approach reduces the invoking times of expensive video predicate in a greater degree and gives a better solution for mixed query optimization which contains traditional data types and large object operations. The experiment result is satisfactory while compare with existing expensive predicates query optimization methods.