A multi-variable visualization technique on a 2D bitmap for big data is introduced. If A and B are two data points that are represented using two similar shapes with
m pixels, where each shape is colored with RGB color of (0, 0,
k
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A multi-variable visualization technique on a 2D bitmap for big data is introduced. If A and B are two data points that are represented using two similar shapes with
m pixels, where each shape is colored with RGB color of (0, 0,
k), when
A ∩
B ≠ ɸ, adding the color of
A ∩
B gives higher color as (0, 0, 2
k) and the highlight as a high density cluster, where RGB stands for Red, Green, Blue and
k is the blue color. This is the hypothesis behind the single variable graphical knowledge unit (GKU), which uses the entire bit range of a pixel for a single variable. Instead, the available bit range of a pixel is split, and a pixel can be used for representing multiple variables (multi-variables). However, this will limit the bit block for single variables and limit the amount of overlapping. Using the same size
k (>1) bitmaps (multi-layers) will increase the number of bits per variable (BPV), where each
(x,
y) of an individual layer represents the same data point. Then, one pixel in a four-layer GKU is capable of showing more than four billion overlapping ones when BPV = 8 bits (2
(BPV × number of layers)) Then, the 32-bit pixel format allows the representation of a maximum of up to four dependent variables against one independent variable. Then, a four-layer GKU of
w width and
h height has the capacity of representing a maximum of (2
(BPV × number of layers)) ×
m ×
w ×
h overlapping occurrences.
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