There was also an exhibition of Andy Worhal painting notable Jewish persons, one of the pictures is of Golda Meir, the only Israeli Woman prime-minister (time for the second one?)
The VLDB conference also uploaded pictures from the conference, so here are two pictures - one from my tutorial, and the second showing me in the first raw (it was not really the first raw, but it was the first captured by the camera) listening to the keynote talk:
While I have been away there were some Blog posts by Paul Vincent that worth focusing upon, I have already commented briefly to this one, but want to have longer reaction about the issue of event duration that was raised by Paul.
In most of the models events are considered as instantaneous, occurring within a single time point, the temporal database glossary from 1998 puts "instantaneous" as part of the definition of event, the rationale is of looking on event as transition between two states, and transition in most models takes zero time, A few years ago when we started the discussions about terms, I've pointed out the temporal glossary as a source for event definition, and David Luckham issued a strong objection to that definition, claiming that no event is really instantaneous, even simple events like the "aircraft is landing" takes more than zero time, while events that are composed of other events - "complex events" - like the 1929 crisis (now we can talk about the 2008 crisis) is compose of many events and occurred over an interval.
This is, of course, true, yet it is more convenient from computational point of view to deal with discrete time points than in intervals, furthermore, some systems have detection time semantics, looking at the time-stamp in which the event entered the system, rather than the time it occurred, this is the reason that we find time point semantics in most systems.
We can look at the following cases:
- The event really occurs within a time point, e.g. time series of sensor measurements, or stock quotes. There is indeed an interval among two successive events, but this relates to the state between the intervals and not the events themselves.
- The event occurs within an interval, but the granularity of our time computation is bigger than the interval, thus we can approximate the interval to a time point. Example: the granularity we are interested is an hour, thus even if an event occurs within several minutes, we can still approximate it to the closest hour.
- The event occurs within an interval, and it is important to process it with an interval semantics, since we would like to see it relationship to another time interval (e.g. temporal context).
- The event occurs in an unknown time-point that is bounded by an interval, there is some probability (e.g. uniform distribution) that it happened in any point of time within the interval. In VLDB there has been a paper by Yaneli Diao and her students entitled: Recognizing Patterns in Streams with Imprecise Timestamps Note that in this paper there are also some references to interval based semantics (of type 3).
- Derived events are another type of events whose temporal semantics may be tuned. For example: the derived event "frustrated customer" is being derived when a customer approaches a call center the third time about the same topic, the question is whether the customer is frustrated only when approaches the third time, or the customer is frustrated over all the time since the frustrating event occurred until it is fixed. Furthermore, derived event may also indicate an event that will happen in a future interval. I'll write more about this issue in the future.
Bottom line: the event processing systems of the next generation should support both time point and interval semantics along with uncertainties (Paul also had posting about "fuzzy patterns" on which I'll write in the future).