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seeReg - Regular Expression for Time-varying Multi-variate Visualization

 



Extracting and visualizing temporal patterns in large scientific data is an open problem in visualization research. First, there are few proven methods to flexibly and concisely define general temporal patterns for visualization. Second, with ever-growing large timedependent data sets, scalable and general solutions for handling the data are still not widely available. In this work, we have developed a textual pattern matching approach for specifying and identifying general temporal patterns. Besides defining the formalism of the language, we also provide a working implementation with sufficient efficiency and scalability to handle large data sets. Using recent large-scale simulation data from multiple application domains, we demonstrate that our visualization approach is one of the first to empower a concept driven exploration of large-scale time-varying multivariate data. Our system increases the utility of range query systems. By providing a high-level language for temporal data analysis, we allow scientists to consider their data at a higher level, describing multivariate features of interest in a language that allows the specification of uncertainty. The results show that significant scientific understanding can be achieved across application domains when the richness of data description is increased. Our system has the potential to solve many large-scale data understanding problems in scientific simulation. In applications where scientists can only describe results in a "fuzzy" manner, systems that directly measure and express uncertainty have great promise to provide heretofore undiscovered scientific insight.

Above figure displays a query to a large scale climate data set. This query aims to identify the point in time when the first large snowfall between May and December occurs in various years. The first large snow cover first appears in Northern Canada, Siberia, and the Himalayas, and then progresses into the warmer regions to the South and, in the case of the Eurasian landmass, to the West. The Rocky Mountains can be identified as an area of early snowfall.

'Visualizing Temporal Patterns in Large Multivariate Data using Textual Pattern Matching', Markus Glatter, Jian Huang, Sean Ahern, Jamison Daniel and Aidong Lu, IEEE Transactions on Visualization and Computer Graphics, 14(6), pp. 1467-1474, 2008. (special issue for IEEE Visualization’08).