Dynamic Max Coun

This contains the ideas and notes for a Dynamic Max Count (Dynamic Max-in-time) aggregate operator

Concept

Instead of using Hyper-buckets that have discrete boundaries and densities which can not be updated reasonably using the MaxCountProgramNotes ideas, we propose a probabilistic method where by we put probability densities in space. Each probability density will need the following properties:

1. Parameters that define the distribution e.g.
   1. Center location
   2. Spatial size
   3. Standard deviation
   4. A measure of symmetry or skew
2. A multi-dimensional probability function preferably a function that uses
   • types functions as parameters e.g. \[$p(x_u(t),x_l(t),y_u(t),y_l(t)[,z_u(t),z_l(t)])$\]

3. A theory to update, delete or insert points and the distributions based on changes to points.

Based on this last item, we must maintain a database of 4-dimensional points that we index using 4-dimensional, probability buckets.