Visualization

Abstract

The exploration of large data sets is an important but difficult problem. Information visualization techniques can be useful in solving this problem. Visual data exploration has a high potential, and many applications such as fraud detection and data mining can use information visualization technology for improved data analysis. Avenues for future work include the tight integration of visualization techniques with traditional techniques from such disciplines as statistics, machine learning, operations research, and simulation. Integration of visualization techniques and these more established methods would combine fast automatic data analysis algorithms with the intuitive power of the human mind, improving the quality and speed of the data analysis process. Visual data analysis techniques also need to be tightly integrated with the systems used to manage the vast amounts of relational and semistructured information, including database management and data warehouse systems. The ultimate goal is to bring the power of visualization technology to every desktop to allow a better, faster, and more intuitive exploration of very large data resources. This will not only be valuable in an economic sense but will also stimulate and delight the user.