TAVO: A Treelike Analytical View for OLAP Online Analytical Processing provides multidimensional, summarized views of business data. It enables users to drill down or roll up from a level to another level in a dimension, which forms an analytical paththread. In many scenarios, this analytical path is very useful for users to keep their analysis in context. But most visualization tools display only the result in one chart after drilling down or rolling up. The analytical context or the intermediate process is lost or indirect. This paper presents a novel OLAP visualization approach called TAVO  a Treelike Analysis View for OLAP. It visualizes the multidimensional data by various charts and organizes them in a tree, where each node is a chart and connects other charts when a user navigates along a dimension by levels or to another dimension. By this way, the drilldownrollup semantic relationship is clearly expressed. Users can easily explore relative history queries by rolling up in a chart. We implemented TAVO by dynamically generating multidimensional expressions (MDX) on the fly and then visualizing the results by visualization components such as Microsoft Office Web Component. The system prototype demonstrates the effectiveness of TAVO.

Summarization and Visualization of MultiLevel and MultiDimensional Itemsets Abstract   Frequent itemset (FI) mining aims at discovering relevant patterns from sets of transactions. In this work we focus on multilevel and multidimensional data, which provide a rich description of subjects through multiple features each at different levels of detail. Summarization of FIs has been only marginally addressed so far with specific reference to multilevel and multidimensional FIs. In this paper we fill this gap by proposing SUSHI, a framework for summarizing and visually exploring multilevel and multidimensional FIs. Specifically, SUSHI is based on (i) a similarity function for FIs which takes into account both their extensional (supportbased) and intensional (featurebased) natures; (ii) theoretical results concerning antimonotonicity of support and similarity in multilevel settings, which allow us to propose an efficient clustering algorithm to generate hierarchical summaries; and (iii) two integrated approaches to summary visualization and exploration: a graphbased one, which highlights intercluster relationships, and a treebased one, which emphasizes the relationships between the representative of each cluster and the other FIs in that cluster. SUSHI is evaluated using both a real and a synthetic dataset in terms of effectiveness, efficiency, and understandability of the summary, with reference to three different strategies for choosing cluster representatives. Overall, SUSHI shows to outperform previous approaches and to be a valuable tool to expedite the analysis of FIs. Besides, one of the three strategies for choosing cluster representatives shows to be the most effective one.