Solving summarizability problems in fact-dimension relationships for multidimensional models

Abstract

Multidimensional analysis allows decision makers to efficiently and effectively use data analysis tools, which mainly depend on multidimensional (MD) structures of a data warehouse such as facts and dimension hierarchies to explore the information and aggregate it at different levels of detail in an accurate way. A conceptual model of such MD structures serves as abstract basis of the subsequent implementation according to one specific technology. However, there is a semantic gap between a conceptual model and its implementation which complicates an adequate treatment of summarizability issues, which in turn may lead to erroneous results of data analysis tools and cause the failure of the whole data warehouse project. To bridge this gap for relationships between facts and dimension, we present an approach at the conceptual level for (i) identifying problematic situations in fact-dimension relationships, (ii) defining these relationships in a conceptual MD model, and (iii) applying a normalization process to transform this conceptual MD model into a summarizability-compliant model that avoids erroneous analysis of data. Furthermore, we also describe our Eclipsebased implementation of this normalization process.

Detecting summarizability in OLAP

“…Both in statistical databases and OLAP research, most of the work in summarizability focuses on the multidimensional structure of the OLAP cube, for example [32,25,10,19,21,2,24]. The paper by Mazon et al is a recent review of the field [22].…”

A Model-Driven Approach for Enforcing Summarizability in Multidimensional Modeling

“…As observed in [9], minimum and maximum multiplicities of associations characterize sub-structures where summarizability is violated. In earlier work [8], we have shown how to normalize MD schemata with non-summarizable fact-dimension associations into summarizable ones. With respect to non-summarizable Rolls-UpTo associations, however, a similar approach still needs to be designed.…”

A survey on summarizability issues in multidimensional modeling

“…For example, non-strict relations between facts and dimensions do occur in real-world scenarios, hence need to be modeled conceptually and then transformed correctly into their corresponding implementation, addressing summarizability problems [37,31]. Towards this direction, the novel work presented in [24] focuses on identifying problematic situations in fact-dimension relationships, defining these relationships in a conceptual multidimensional model, and applying a normalization process with which to transform this conceptual multidimensional model into a summarizability-compliant model that avoids erroneous analysis of data. Furthermore, we argue that object-based approaches are a good choice to proceed: Importantly, approaches that use UML constructs [1,19,33] can represent generalization relationships explicitly to ensure context-sensitive summarizability.…”