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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 define a probability density function in each hyper-bucket. In a sense we are not trying to minimize skew in the bucket creation process, but recognizing and modeling skew in each bucket. The added advantage to this concept is that a region equivalent to a hyper-bucket containing no points may be excluded from the index. Consequently the algorithm searches a smaller space. For example if it becomes necessary to shrink the size of a hyper-bucket to [[latex2($10~unit^{6}$)]] size in a [[latex2($10000 unit^6$)]] space we will have [[latex2($1 \times 10^18$)]] buckets. and points concentrated in specific inEach 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 define a probability density function in each hyper-bucket. In a sense we are not trying to minimize skew in the bucket creation process, but recognizing and modeling skew in each bucket. The added advantage to this concept is that a region equivalent to a hyper-bucket containing no points may be excluded from the index. Consequently the algorithm searches a smaller space. For example if it becomes necessary to shrink the size of a hyper-bucket to [[latex2($10~unit^{6}$)]] size in a [[latex2($10000~unit^6$)]] space we will have [[latex2($1 \times 10^18$)]] buckets. and points concentrated in specific inEach 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 define a probability density function in each hyper-bucket. In a sense we are not trying to minimize skew in the bucket creation process, but recognizing and modeling skew in each bucket. The added advantage to this concept is that a region equivalent to a hyper-bucket containing no points may be excluded from the index. Consequently the algorithm searches a smaller space. For example if it becomes necessary to shrink the size of a hyper-bucket to latex2($10~unit^{6}$) size in a latex2($10000~unit^6$) space we will have latex2($1 \times 10^18$) buckets. and points concentrated in specific inEach 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)