Microeconomic analysis using dominant relationship analysis

The concept of dominance has recently attracted much interest in the context of skyline computation. Given an N-dimensional data set S, a point p is said to dominate q if p is better than q in at least one dimension and equal to or better than it in the remaining dimensions. In this article, we propose extending the concept of dominance for business analysis from a microeconomic perspective. More specifically, we propose a new form of analysis, called Dominant Relationship Analysis (DRA), which aims to provide insight into the dominant relationships between products and potential buyers. By analyzing such relationships, companies can position their products more effectively while remaining profitable. To support DRA, we propose a novel data cube called DADA (Data Cube for Dominant Relationship Analysis), which captures the dominant relationships between products and customers. Three types of queries called Dominant Relationship Queries (DRQs) are consequently proposed for analysis purposes: (1) Linear Optimization Queries (LOQ), (2) Subspace Analysis Queries (SAQ), and (3) Comparative Dominant Queries (CDQ). We designed efficient algorithms for computation, compression and incremental maintenance of DADA as well as for answering the DRQs using DADA. We conducted extensive experiments on various real and synthetic data sets to evaluate the technique of DADA and report results demonstrating the effectiveness and efficiency of DADA and its associated query-processing strategies.