Our society has entered a data-driven era, in which not only enormous amounts of data are being generated every day, but there are also growing expectations placed on the analysis of these data. OpenData programs, in which data are available for free, are growing in number. A number of popular web sites have opened access to their data through web services in exchange for pecuniary retribution. Analyzing these massive and complex datasets is essential to making new discoveries and creating benefits for people, but it remains a very difficult task. In many cases, the ability to make timely decisions based on available data is crucial to business success, clinical treatments, cyber and national security, and disaster management, but most data have become simply too large and often have too short a lifespan, i.e. it changes too rapidly for classical visualization or analysis methods to be able to handle it properly. The key is not only to visualize data, but also to allow users to interact with the data. This is particularly the case with movement data, such as traffic data on roads or in an airspace, because of the intrinsinc time-dependant nature of these data. Analyzing and understanding time-dependent data poses non-trivial challenges to information visualization. First, such datasets are by their very nature several orders of magnitude larger than static datasets, which underlines the importance of relying on efficient interactions with multiple objects and fast algorithms. Secondly, while patterns of interest in static data can be naturally depicted by specific representations in still visualizations, we do not yet know how to best visualize dynamic patterns, which are inherent to time-dependent data. These are the two challenges that this project aims at addressing. Interaction and representation, with large data, heavily rely on algorithms: algorithms to compute and display the representation, and algorithms to transform the manipulation of the user into updates of the view and the data. Not only do the performance of these algorithms determine what representations can be used in practice, their nature also has a strong influence on what the visualizations look like. The algorithms that are used classiscally in InfoVis are expressed in the data space (e.g. computation on geographic locations). In this project, we will investigate an alternative approach: algorithms expressed in the graphic space (image-based algorithms). This consists of two steps: first, a data representation is built using straightforward InfoVis techniques; second, the resulting image undergoes purely graphical transformations using image processing techniques. This approach takes advantage of changes in the bottlenecks of computer graphics: since data storage and memory limitation is less and less of an issue, we can plan to reduce computation time by using memory as a new tool to solve computationally challenging problems. Furthermore, graphic cards are nowadays used to perform parallel computations (so called GPGPU techniques). We have recently tested this approach to compute static and dynamic bundling of transport flows, and it proved to be a most efficient way of producing representations fast enough to be interactive. This opens a whole field of study, including the scientific validation of the method, its limitations, and its generalization to different types of datasets, other algorithms, and other time-dependent representation patterns. Our goal in this project is to explore new computing techniques with pixel based algorithm to provide efficient visualizations and user interfaces for the exploration of large datasets of time-dependent data. This project theme lies within the basic research category. It is positioned within the areas of Information Visualization, Visual Analytics, Computer Graphics and Human Computer Interaction.