Interactive exploration and visualization of OLAP cubes

Conference Paper · January 2011with10 Reads
DOI: 10.1145/2064676.2064691 · Source: DBLP
Conference: DOLAP 2011, ACM 14th International Workshop on Data Warehousing and OLAP, Glasgow, United Kingdom, October 28, 2011, Proceedings
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.
    • "In the future, interesting visualization challenges will arise if Cinecubes departs from its reporting nature (which is very well served by the 2D crosstabs) and allow interactivity (plz., refer to the Discussion section). Then, visualization will need to consider interactive settings like [29], [31], [30], [33]. We would also like to highlight that our method is synchronized with the key findings of [18], as (a) the main criterion that we use for suggestions in Act I is what [18] identified as the key characteristic feature of cube queries, i.e., the selection condition and (b) the key feature for explaining results in Act II is the second most characteristic feature, groupers. "
    [Show abstract] [Hide abstract] ABSTRACT: In this paper we demonstrate that it is possible to enrich query answering with a short data movie that gives insights to the original results of an OLAP query. Our method, implemented in an actual system, CineCubes, includes the following steps. The user submits a query over an underlying star schema. Taking this query as input, the system comes up with a set of queries complementing the information content of the original query, and executes them. For each of the query results, we execute a set of highlight extraction algorithms that identify interesting patterns and values in the data of the results. Then, the system visualizes the query results and accompanies this presentation with a text commenting on the result highlights. Moreover, via a text-to-speech conversion the system automatically produces audio for the constructed text. Each combination of visualization, text and audio practically constitutes a movie, which is wrapped as a PowerPoint presentation and returned to the user.
    Full-text · Article · Oct 2015
    • "Riedewald et al., proposed a modular framework that combined onedimensional On-Line Analytical Processing (OLAP) aggregation methods in order to better enhance the data cube's ability to reserve space for information [12]. Two dimensional OLAP was proposed in 2011 by Ordonez et al., that displays interactive visualized data which separates and distinguishes the differences in statistical measurements [13]. This critical distinction is one that we leverage in the RDF Data Cube Browser. "
    [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.
    Full-text · Conference Paper · Jan 2013
  • [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.
    Conference Paper · Jan 2011 · Information Systems
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