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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:		 Instead of using Hyper-buckets that have constant 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:

Dynamic Max Count

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 constant 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. latex2(p(xu(t),xl(t),yu(t),yl(t)[,zu(t),zl(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.

DynamicMaxCount (last edited 2020-01-23 22:27:01 by 68)