Ambidexterity is a good property for a boxer, he can decide when is better to attack with his right hand, and when to attack with the left hand (I am part of the left-handed minority, should write sometimes a post about being left-handed in the Right-hand people's world). Likewise, there are problems in the event processing space that can be solved by deterministic means (rules, queries, scripts, patterns --- chose your favorite religion), and problems that are solved by stochastic means -- using probabilistic networks, machine learning etc.. (AKA IEP - Intelligent Event Processing). When there is a pattern that need to be traced , to check compliance with regulations, and the pattern is well-defined - then a deterministic approach should be used; when there is a need to dynamically change the traffic lights policies to have minimal waiting time of vehicle, there is a need to predict the traffic in the next few minutes - this is a non deterministic problem and require some stochastic tool (BTW - my student, Elad Margalit, is looking at the traffic lights issue as his M.Sc. thesis). Event Processing Platforms should include various types of functionality - which brings to another discussion on the "actor/agent" architecture - which I'll refer to in one of the next posts. more -later
4 comments:
Interesting point, and one which is relevant to many other information systems problem.
I wonder if there is some general measure which allows us to automatically decide if a given problem will be best treated by stochastic measures or by deterministic ones.
Hello Eran -- I am not sure if there are such automatic procedures to decide, there may be some thumb rules.
cheers,
Opher
Well, there's one big rule of thumb, coming from the definitions: If there's uncertainly, then it's stochastic. If it's certain, then it's deterministic. :-)
Deterministic problems usually become stochastic when you're not exactly sure what an observation means. Taking a variation of traffic lights:
If you have a fine-grained sequence of exact locations for a car, an exact location for an intersection and precise knowledge of when the traffic light in the intersection is red and green... well then you can make a deterministic decision on whether the car was illegally crossing the intersection during a red light.
It becomes stochastic when you are not sure of the exact meaning of one or more observations. Maybe the sensor that determines the location of the car could be off by up to 20 feet in any direction. Maybe you only have a few observations of the car's location - none of them show the car exactly in the intersection during the red light but some show that it was suspiciously close. As soon as you are unsure of something, this is a candidate for stochastic modeling.
Thanks,
that was nicely put.
Eran.
Post a Comment