Visualization has been successfully applied to analyse time-dependent data for a long time now. Lately, a number of new approaches have been introduced, promising more effective graphs especially for large datasets and multiparameter data. In this paper, we give an overview on the visualization of time-series data and the available techniques. We provide a taxonomy and discuss general aspects of time-dependent data. AAer an overview on conventional techniques we discuss techniques for analysing time-dependent multivariate data sets in more detail. After this, we give an overview on dynamic presentation techniques and event-based visualization. The analysis of time-series data is one of the most widely appearing problems in science, engineering, and business. In the last years this problem gained increasing importance due to the fact that more sensitive sensors in science and engineering and the widespread use of computers in corporations have increased the amount of time-series data collected by many magnitudes. For long, visualization has proven to be an effective approach to analyze time-series data. The motivation behind this approach is to exploit the phenomenal abilities of the human eye to detect structures in images. A well designed visualization can aid in answering the following questions for unknown temporal data (MacEachren 1995 In what order do data elements appear? (Sequence) Do data elements exist together? (Synchronization) The visualition of timedependent data has a long history. Time-series plots appeared for the first time in the illustration of planetary orbits in a text kom a monastery school in the 10' or 11' century (Tu& 1983). In science, timeseries charts have been rediscovered not earlier than in the 18' century by Lambert. He applied line graphs to display periodic variation in soil temperature in relation to depth under the surface (Lambert 1779). Today, a wider repertoire of techniques to visualize timedependent data is available. In this paper, we give an overview of different tecbniques suitable to analyze time-series data. In section 2 we introduce a taxonomy for timedependence of data. In section 3 we give an overview on visualization techniques for this problem field Our classification of visualization techniques distinguishes between static and dynamic representations as well as between more conventional techniques and special methods for multi-pammeter data. Moreover, we discuss the common approach of event-based visualizations. We conclude in section 4 with general remark on using the described techniques and issues for linther research. Before discussing visualization methods for timedependent data we want to clarify some basic notation. We consider multi-parameter data as a set of multidimensional multivariate data. We adopt the convention from Bergeron (1993) and Wongetal. (1997) that the term multidimensional refers to the dimensionality of the independent variables, while the term multivariate refers to the dimensionality of the dependent variables of a data set. In our case the parameter time has an extraordinary importance, defining the independent dimension for which all other variables are given. That means, we consider multivariate data over time. Therefore, we can defme that time dependent data is 737
Various approaches have been developed for visualizing temporal data REF .
Visualization methods for time-dependent data - an overview
Proceedings of the 2003 Winter Simulation Conference, 2003.
Proceedings of the 2003 Winter Simulation Conference, 2003.
Computer Science