Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations.
 Business data collection is growing exponentially in recent years. A variety of industries and businesses have adopted new technologies of data storages such as data warehouses. On Line Analytical Processing (OLAP) has become an important tool for executives, managers, and analysts to explore, analyze, and extract interesting patterns from enormous amount of data stored in data warehouses and multidimensional databases. However, it is difficult for human analysts to interpret and extract meaningful information from large amount of data if the data is presented in textual form as relational tables. Visualization and interactive tools employ graphical display formats that help analysts to understand and extract useful information fast from huge data sets. This paper presents a new visual interactive exploration technique for an analysis of multidimensional databases. Users can gain both overviews and refine views on any particular region of interest of data cubes through the combination of interactive tools and navigational functions such as drilling down, rolling up, and slicing. Our technique allows users who are not experts in OLAP technology to explore and analyze OLAP data cubes and data warehouses without generating sophisticated queries. Furthermore, the visualization in our technique displays the exploration path enhancing the user's understanding of the exploration. In the On Line Analytical Processing (OLAP) context, exploration of huge and sparse data cubes is a tedious task which does not always lead to efficient results. In this paper, we couple OLAP with the Multiple Correspondence Analysis (MCA) in order to enhance visual representations of data cubes and thus, facilitate their interpretations and analysis. We also provide a quality criterion to measure the relevance of obtained representations. The criterion is based on a geometric neighborhood concept and a similarity metric between cells of a data cube. Experimental results on real data proved the interest and the efficiency of our approach. Data visualization is one of the major issues of database research. OLAP a decision support technology, is clearly in the center of this effort. Thus far, visualization has not been incorporated in the abstraction levels of DBMS architecture (conceptual, logical, physical); neither has it been formally treated in this context. In this paper we start by reconsidering the separation of the aforementioned abstraction levels to take visualization into consideration. Then, we present the Cube Presentation Model (CPM), a novel presentational model for OLAP screens. The proposal lies on the fundamental idea of separating the logical part of a data cube computation from the presentational part of the client tool. Then, CPM can be naturally mapped on the Table Lens, which is an advanced visualization technique from the Human-Computer Interaction area, particularly tailored for cross-tab reports. Based on the particularities of Table Lens, we propose automated proactive support to the user for the interaction with an OLAP screen. Finally, we discuss implementation and usage issues in the context of an academic prototype system (CubeView) that we have implemented.