DAF: An adaptive computing framework for multimedia data streams analysis

We consider the problem of efficiently online computing/filtering or analysis multimedia streams. In this scenario, we register a large scale of continuous analysis queries to filter pornographic stream items. Each query is a conjunction of filters. For instance, the query “does this image contain a people basking in the beach?” can be resolved by applying the conjunction of water, people, sand, sea filters successively on the stream item. However, the online evaluation of multimedia filters is indeed very expensive, fortunately there usually exist multiple filters shared among a lot of queries. In other words, each filter may occur in multiple queries. An open problem in such a filtering scenario is how to order the filters in an optimal sequence to achieve significant performance. Existing methods are based on a greedy strategy which orders the filters according to three factors (selectivity, popularity, cost). Although all these methods achieve good results, there are still some problems that haven’t addressed yet. First, the selectivity factor is set empirically, which can not adaptively adjust with multimedia stream. Second, the proportion relationships among the three factors (selectivity, cost, popularity) were not considerably explored. Under these observations,in this paper, we propose a Dynamic-Analytic hierarchy process Framework (DAF) which use a time-based compositional forecasting method, which is based on the idea of exponential smoothing, to deal with the factors’ proportion relationships dynamics. Experiments on both synthetic and real lift multimedia streams demonstrate that our proposed framework (DAF) provides much great adaptability in modeling the factors proportion relationships changing over multimedia stream environment.