Interactive exploration and visualization of OLAP cubes.
ABSTRACT An OLAP cube is typically explored with multiple aggregations selecting different subsets of cube dimensions to analyze trends or to discover unexpected results. Unfortunately, such analytic process is generally manual and fails to statistically explain results. In this work, we propose to combine dimension lattice traversal and parametric statistical tests to identify significant metric differences between cube cells. We present a 2D interactive visualization of the OLAP cube based on a checkerboard that enables isolating and interpreting significant measure differences between two similar cuboids, which differ in one dimension and have the same values on the remaining dimensions. Cube exploration and visualization is performed by automatically generated SQL queries. An experimental evaluation with a medical data set presents statistically significant results and interactive visualizations, which link risk factors and degree of disease.
- [Show abstract] [Hide abstract]
ABSTRACT: The ACM 14th International Workshop on Data Warehousing and OLAP (DOLAP 2011), held in Glasgow, Scotland, UK on October 28, 2011, in conjunction with the ACM 20th International Conference on Information and Knowledge Management (CIKM 2011), presents research on data warehousing and On-Line Analytical Processing (OLAP). The DOLAP 2011 program has three interesting sessions on data warehouse modeling and maintenance, ETL and performance, and OLAP visualization and extensions, and a panel discussing analytics in data warehouses.Proceedings of the 20th ACM Conference on Information and Knowledge Management, CIKM 2011, Glasgow, United Kingdom, October 24-28, 2011; 01/2011
- [Show abstract] [Hide abstract]
ABSTRACT: Increasingly, experts and interested laypeople are turning to the explosion of online data to form and explore hypotheses about relationships between public health intervention strategies and their possible impacts. We have engaged in a multi-year collaboration to use and design semantic techniques and tools to support the current and next generation of these explorations. We introduce a tool, qb.js, to enable access to multidimensional statistical data in ways that allow non-specialists to explore and create specific visualizations of that data. We focus on explorations of health data - in particular aimed at helping to support the formation and analysis of hypotheses about public health intervention strategies and their correlation with health-related behavior changes. We used qb.js to formulate and explore the hypothesis that youth tobacco access laws have consistent, measurable impacts on the rate of change in cigarette smoking among high school students over time. While focused in this instance on one particular intervention strategy (i.e., limiting youth access to tobacco), this analytics platform may be used for a wide range of correlational analyses. To address this hypothesis, we converted population science data on tobacco-related policy and behavior from Impacteen to a Resource Description framework (RDF) representation that was annotated with the RDF Data Cube vocabulary. A Semantic Data Dictionary enabled mapping between the original datasets and the RDF representation. This allowed for the creation and publication of data visualizations using qb.js. The RDF Data Cube representation made it possible to discover a significant downward effect from the introduction of nine youth tobacco access laws on the rate of change in smoking prevalence among high school-aged youth.System Sciences (HICSS), 2013 46th Hawaii International Conference on; 01/2013