I've done some research on the topic and, so far, it was possible to identify that the following patterns have been thoroughly explored:
1) Sequence Mining. Example: "A-B-C-D happened in 10% of the database". This can also include a time constraint between events.
2) Temporal Association Rules: "A,B,C->D (30%, 20%) between 7am and 10am". In this case, the temporal information is used to "slice" the database in n-parts, which are then used to extract traditional association rules.
My first impression is that many recent researchers are focused on efficiently performing either 1 or 2, or porting them to data stream environments. I am wondering if there are any other patterns that can be obtained from timestamped data, for instance, "A,B->C (20%, 30%)", such that A and B happens in any order in a time window of at least 10 hours and at maximum 20 hours before C.
If nothing similar exists, is it worth the effort to develop a new data mining method to extract patterns similar to this?
Also, if you know about any open datasets with the following characteristics, please let me know. I've tried UCI repository, but no success so far.
Events with their respectives timestamps for many individuals or sensors. Example:
1 A (2012-02-20) B (2012-03-23) C (2013-01-20)
2 B (2003-04-30) D (2004-03-20)
3 B (2010-09-10) A (2010-10-01) C (2010-10-02)