Interactive Visual Transformation for Symbolic Representation of Time-Oriented Data

Abstract

Data Mining on time-oriented data has many real-world applications, like optimizing shift plans for shops or hospitals, or analyzing traffic or climate. As those data are often very large and multi-variate, several methods for symbolic representation of time-series have been proposed. Some of them are statistically robust, have a lower-bound distance measure, and are easy to configure, but do not consider temporal structures and domain knowledge of users. Other approaches, proposed as basis for Apriori pattern finding and similar algorithms, are strongly configurable, but the parametrization is hard to perform, resulting in ad-hoc decisions. Our contribution combines the strengths of both approaches: an interactive visual interface that helps defining event classes by applying statistical computations and domain knowledge at the same time. We are not focused on a particular application domain, but intend to make our approach useful for any kind of time-oriented data.

Data Abstraction and Pattern Identification in Time-series Data

“…This thesis focuses specifically on data abstraction into symbolic representation that takes into account the temporal characteristics of the data. Transforming the raw data into a symbol sequence enables application of sequence analysis tools such as those found in text processing and bio-informatics communities [72] to analyze repeating patterns, anomalies, user query based pattern identification. Also, using symbolic approximation, the original data can also be transformed into smaller or less complex symbol components that are beneficial for storage or computation [112].…”