2 research outputs found
Benchmarking Summarizability Processing in XML Warehouses with Complex Hierarchies
Business Intelligence plays an important role in decision making. Based on
data warehouses and Online Analytical Processing, a business intelligence tool
can be used to analyze complex data. Still, summarizability issues in data
warehouses cause ineffective analyses that may become critical problems to
businesses. To settle this issue, many researchers have studied and proposed
various solutions, both in relational and XML data warehouses. However, they
find difficulty in evaluating the performance of their proposals since the
available benchmarks lack complex hierarchies. In order to contribute to
summarizability analysis, this paper proposes an extension to the XML warehouse
benchmark (XWeB) with complex hierarchies. The benchmark enables us to generate
XML data warehouses with scalable complex hierarchies as well as
summarizability processing. We experimentally demonstrated that complex
hierarchies can definitely be included into a benchmark dataset, and that our
benchmark is able to compare two alternative approaches dealing with
summarizability issues.Comment: 15th International Workshop on Data Warehousing and OLAP (DOLAP
2012), Maui : United States (2012
A Novel Query-Based Approach for Addressing Summarizability Issues in XOLAP
International audienceThe business intelligence and decision-support systems used in many application domains casually rely on data warehouses, which are decision-oriented data repositories modeled as multidimensional (MD) structures. MD structures help navigate data through hierarchical levels of detail. In many real-world situations, hierarchies in MD models are complex, which causes data aggregation issues, collectively known as the summarizability problem. This problem leads to incorrect analyses and critically affects decision making. To enforce summarizability, existing approaches alter either MD models or data, and must be applied a priori, on a case-by-case basis, by an expert. To alter neither models nor data, a few query-time approaches have been proposed recently, but they only detect summarizability issues without solving them. Thus, we propose in this paper a novel approach that automatically detects and processes summarizability issues at query time, without requiring any particular expertise from the user. Moreover, while most existing approaches are based on the relational model, our approach focus on an XML MD model, since XML data is customarily used to represent business data and its format better copes with complex hierarchies than the relational model. Finally, our experiments show that our method is likely to scale better than a reference approach for addressing the summarizability problem in the MD context