More on spatial event processing

This is a map of my home city, Haifa, which shows it northern part, I live in the south part outside this picture. I had some internal discussions this week about the role of location information in event processing, this is somewhat similar to the roles that time has in event processing, though the use of time is more pervasive, we see increasing number of applications that require spatial support. Some of the major utilizations of location information are:

1. Location as a context: The location is used to group events together as they serve as input for any event processing function such as: aggregation of pattern matching.
2. Location as enabler for spatial patterns: The locations of events is used in the function that determined whether a certain pattern is being matched.

Note that these two roles of locations are orthogonal.

An example for the first role: count all the police vehicles that are within 5 KM from a crime scene, where there are periodic events that identify the location of the police vehicles using GPS devise. In this case, location is done to find out which events are "in context", and the event processing function itself is a simple aggregation (count).

An example for the second role: A certain company experienced over one week, 20 events of breaking into employees houses, it an an attempt to figure out whether this is a work of a single person, the pattern that is matched is: the maximal distance between two breaking in events that occurred within a single night is less than 15KM. In this case the context is not spatial, since the events involved are determined by time ("a single night") and segmentation ("events related to houses of employees"), but the pattern that we are looking for is spatial, since it involves location.

As said, time can also play in the context role, in the pattern role, in both, or in none.

More on spatial event processing - later.

On spatial relations between events and entities

During the final pass of revisions for the EPIA book, we have re-visited the issue of spatial context, one of the options of spatial context is to group or partition events according to the relationship between event and a certain entity. However, location is not necessarily a point, there are three main type of location: point, line (or poly-line) and area (polygon). This is true for entity, but it is also true for an event, if the event is located using GPS, then a point coordinate is obtained, but if an event is located using the signal sent from a cellular phone, then the location of the event is a cell, i.e. it occurs within an area. We can also say that a vehicle is somewhere on road M1, which is a poly-line. Given these three data types, we can define several relations among the location of the event, and the location of the entity, as shown in the illustration above, such as: contains --- the entity contains the event, disjoint etc... The illustration above shows the various cases between events of various location types and entities of various location types. Some examples of use cases for the various relations will follow in one of the next postings, meanwhile -- you can think about applications yourself