Cubes convexes

TL;DR

This paper introduces the convex cube, a unifying, compact data structure for capturing constrained tuples in data cubes, optimizing computation and storage, and also proposes the emerging cube for trend analysis.

Contribution

The paper presents the convex cube as a new, unified framework for various cube types, improving efficiency and simplicity in data cube characterization.

Findings

Convex cube provides a compact representation of constrained data tuples.

It unifies different cube variants into a single framework.

Emerging cube captures significant trend inversions.

Abstract

In various approaches, data cubes are pre-computed in order to answer efficiently OLAP queries. The notion of data cube has been declined in various ways: iceberg cubes, range cubes or differential cubes. In this paper, we introduce the concept of convex cube which captures all the tuples of a datacube satisfying a constraint combination. It can be represented in a very compact way in order to optimize both computation time and required storage space. The convex cube is not an additional structure appended to the list of cube variants but we propose it as a unifying structure that we use to characterize, in a simple, sound and homogeneous way, the other quoted types of cubes. Finally, we introduce the concept of emerging cube which captures the significant trend inversions. characterizations.