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| = Dynamic Max Count = | = Dynamic Max Coun = |
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| This contains the ideas and notes for a Dynamic Max Count or Max-in-time agregate operator | This contains the ideas and notes for a Dynamic Max Count (Dynamic Max-in-time) aggregate operator |
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| Instead of using Hyper-buckets that have descrete boundaries and densities which can not be updated resonably using the MaxCount ideas, we propose a probabalistic method where by we put probability densities in space. Each probability density will need the following properties: | 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: |
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| 1. A measure of symetry or skew 1. A multi-dimensional probability function preferably a function that specific 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)]) |
1. A measure of symmetry or skew 1. A multi-dimensional probability function preferably a function that uses types functions as parameters e.g. \[$p(x_u(t),x_l(t),y_u(t),y_l(t)[,z_u(t),z_l(t)])$\] 1. 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. |
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:
- Parameters that define the distribution e.g.
- Center location
- Spatial size
- Standard deviation
- A measure of symmetry or skew
- A multi-dimensional probability function preferably a function that uses
- types functions as parameters e.g. \[$p(x_u(t),x_l(t),y_u(t),y_l(t)[,z_u(t),z_l(t)])$\]
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.