Possibilities and Limits in Visualizing Large Amounts of Multidimensional Data

In this paper, we describe our concepts to visualize very large amounts of multidimensional data. Our visualization technique which has been developed to support querying of large scientific databases is designed to visualize as many data items as possible on current display devices. Even if we are able to use each pixel of the display device to visualize one data item, the number of data items that can be visualized is quite limited. Therefore, in our system we introduce reference points (or regions) in multidimensional space and consider only those data items which are 'close' to the reference point. The data items are arranged according to their distance from the reference point. Multiple windows are used for the different dimensions of the data with the distance of each of the dimensions from the reference point (or region) being represented by color. In exploring the database, the reference point (or region) may be changed interactively, allowing different portions of the database to be visualized. To visualize larger portions of the database, sequences of visualizations may be generated automatically by moving the reference point along some path in multidimensional space. Besides describing our visualization technique and several alternatives, we discuss some of the perceptual issues that arise in connection with our visualization technique

VisIVO - Integrated Tools and Services for Large-Scale Astrophysical Visualization

VisIVO is an integrated suite of tools and services specifically designed for the Virtual Observatory. This suite constitutes a software framework for effective visual discovery in currently available (and next-generation) very large-scale astrophysical datasets. VisIVO consists of VisiVO Desktop - a stand alone application for interactive visualization on standard PCs, VisIVO Server - a grid-enabled platform for high performance visualization and VisIVO Web - a custom designed web portal supporting services based on the VisIVO Server functionality. The main characteristic of VisIVO is support for high-performance, multidimensional visualization of very large-scale astrophysical datasets. Users can obtain meaningful visualizations rapidly while preserving full and intuitive control of the relevant visualization parameters. This paper focuses on newly developed integrated tools in VisIVO Server allowing intuitive visual discovery with 3D views being created from data tables. VisIVO Server can be installed easily on any web server with a database repository. We discuss briefly aspects of our implementation of VisiVO Server on a computational grid and also outline the functionality of the services offered by VisIVO Web. Finally we conclude with a summary of our work and pointers to future developments

OWLAP - using OLAP approach in anomaly detection

OWLAP (Operative Workbench for Large-scale Analytics and Presentation) is a visual analytics tool that allows the user to browse and drill down the multidimensional data on-line with the possibility to export result into a zooming presentation framework. We address the challenges of multidimensional visualization by aiding the cognitively hard task of understanding attributes, finding patterns and outliers. We successfully solved the challenge of real time Big Data OLAP reporting by a home developed multithreaded inmemory database manager. Our additional focus is the automatic management of summary preparation that we aid by scripting the presentation framework of Prezi Inc

Using Visualization to Support Data Mining of Large Existing Databases

In this paper. we present ideas how visualization technology can be used to improve the difficult process of querying very large databases. With our VisDB system, we try to provide visual support not only for the query specification process. but also for evaluating query results and. thereafter, refining the query accordingly. The main idea of our system is to represent as many data items as possible by the pixels of the display device. By arranging and coloring the pixels according to the relevance for the query, the user gets a visual impression of the resulting data set and of its relevance for the query. Using an interactive query interface, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. By using multiple windows for different parts of the query, the user gets visual feedback for each part of the query and, therefore, may easier understand the overall result. To support complex queries, we introduce the notion of approximate joins which allow the user to find data items that only approximately fulfill join conditions. We also present ideas how our technique may be extended to support the interoperation of heterogeneous databases. Finally, we discuss the performance problems that are caused by interfacing to existing database systems and present ideas to solve these problems by using data structures supporting a multidimensional search of the database

Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure