The increase of data collection in various domains calls for an adaptation of methods of visualization to tackle magnitudes exceeding the number of available pixels on screens and challenging interactivity. This growth of datasets size has been supported by the advent of accessible and scalable storage and computing infrastructure. Similarly, visualization systems need perceptual and interactive scalability. We present a complete system, complying with the constraints of aforesaid environment, for visual exploration of large multidimensional data with parallel coordinates. Perceptual scalability is addressed with data abstraction while interactions rely on server-side data-intensive computation and hardware-accelerated rendering on the client-side. The system employs a hybrid computing method to accommodate pre-computing time or space constraints and achieves responsiveness for main parallel coordinates plot interaction tools on billions of records.
Sansen et al. REF present a system for visual exploration of large multi-dimensional data with parallel coordinates based on a big data infrastructure, which employs a hybrid computing method to accommodate pre-computing time and hardware-accelerated rendering to reduce rendering time.
Visual Exploration of Large Multidimensional Data Using Parallel Coordinates on Big Data Infrastructure
Informatics
Informatics
Computer Science