Multi-Variable, multi-layer graphical knowledge unit for storing and representing density clusters of multi-dimensional big data

Abstract

A multi-variable visualization technique on a 2D bitmap for big data is introduced. If A andB are two data points that are represented using two similar shapes with m pixels, where each shapeis colored with RGB color of (0, 0, k), when A ∩ B ≠ Φ, adding the color of A ∩ B gives higher color as(0, 0, 2k) and the highlight as a high density cluster, where RGB stands for Red, Green, Blue and k isthe 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 apixel 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 samesize 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 capableof 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 variablesagainst one independent variable. Then, a four-layer GKU of w width and h height has the capacityof representing a maximum of (2(BPV × number of layers)) × m × w × h overlapping occurrences.