Visual Analysis of Complex Event Sequences

Complex event sequences example: here, more than one hundred event sequences are visualized, each aligned along a joint temporal axis. A weekly pattern is quite dominant: seems like many events happen from Monday to Friday, whereas on weekend there seem to be less events happening. There are also some outliers and anomalies. Let's find out more about these findings!

We characterize an event sequence as a data type that is represented as a sequence of time stamps. Every time stamp indicates the occurrence of some measured phenomenon. In our case, event sequences do not have a type (such as therapy A, therapy B, etc.), it is just the signature of time stamps. Such event sequences are everywhere. Examples include:

•     Rising stock prices
•     Traffic accidents
•     Earthquake aftershocks
•     Tweets about some topic
•     Happening of the Olympic Games (always every four years, isn’t it?!)
•     Heartbeats
•     Sleeping phases
•     Etc. etc. etc.

• Event sequences may be long - spanning across long time intervals, large number of events
• Event sequence databases may be large - thousands of sequences with a rich set of interesting patterns
• Event sequences may be unknown - making decisions without knowledge?!

• Algorithmic support is needed - very good if data analysis problem can be solved automatically
• Human supervision is needed - very good if data analysis problem can NOT be solved automatically

• Metrics and features: to characterize event sequences
• Motif simplification: to substitute sequences by motifs
• Grouping: to assign similar sequences to clusters
• Ranking and filtering: to enable changing focus in the data and metadata
• Task abstraction: learn about domain expert's problems and derive analysis tasks, accordingly
• Application: to apply techniques to real-world datasets

See the open positions page