A rank algebra to support multimedia mining applications

摘要

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 as an algebraic 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.