  In the paper, we consider the problem of discovering sequential patterns from
event-based spatio-temporal data. The problem is defined as follows: for a set
of event types $F$ and for a dataset of events instances $D$ (where each
instance in $D$ denotes an occurrence of a particular event type in considered
spatio-temporal space), discover all sequential patterns defining the following
relation between any event types participating in a particular pattern. The
following relation $\rightarrow$ between any two event types, denotes the fact
that instances of the first event type attract in their spatial proximity and
in considered temporal window afterwards occurrences of instances of the second
event type. In the article, the notion of sequential pattern in event-based
spatio-temporal data has been defined and the already proposed approach to
discovering sequential pattern has been reformulated. We show, it is possible
to efficiently and effectively discover sequential patterns in event-based
spatio-temporal data.
