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 don 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 data focus
• Application: to apply techniques to real-world datasets