A rank algebra to support multimedia mining applications

Ordering objects with respect to their various relevant properties before and during processing is a basic step in many multimedia mining problems. Examples include mining frequent patterns in sensory data and mining popularity orders in digital television. Designing multimedia and multi-modal mining techniques for complex and adaptive systems, requires the capability of dealing with rankings of diverse collection of inputs and outputs of a complex mining task, in a uniform, declarative manner. In this paper, we present a model and algebra which treat ranks of the media as first class objects to support complex mining tasks. We model each mining task, declaratively as a boolean combination of multiple subtasks, thus providing a declarative framework in which the ranked results returned by individual subtasks are combined under appropriate semantics. We also present a novel order distance function, which enables partitioning and aggregation support for mining.