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Appl. Sci. 2016, 6(4), 96; doi:10.3390/app6040096

Multi-Variable, Multi-Layer Graphical Knowledge Unit for Storing and Representing Density Clusters of Multi-Dimensional Big Data

1
Group Bio-Process Analysis Technology, Technische Universität München, Weihenstephaner Steig 20, Freising 85354, Germany
2
Bavarian State Research Center for Agriculture, Institute for Agricultural Engineering and Animal Husbandry, Vöttinger Straße 36, Freising 85354, Germany
3
Computer Unit, Faculty of Agriculture, University of Ruhuna, Mapalana, Kamburupitiy 81100, Sri Lanka
4
Lehrstuhl für Brau- und Getränketechnologie, Technische Universität München, Weihenstephaner Steig 20, Freising 85354, Germany
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Antonio Fernández-Caballero
Received: 25 October 2015 / Revised: 15 March 2016 / Accepted: 15 March 2016 / Published: 5 April 2016
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Abstract

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 AB ≠ ɸ, adding the color of AB gives higher color as (0, 0, 2k) 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. View Full-Text
Keywords: knowledge representation; continuous learning; cluster identification; big data knowledge representation; continuous learning; cluster identification; big data
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MDPI and ACS Style

Adikaram, K.K.L.B.; Hussein, M.A.; Effenberger, M.; Becker, T. Multi-Variable, Multi-Layer Graphical Knowledge Unit for Storing and Representing Density Clusters of Multi-Dimensional Big Data. Appl. Sci. 2016, 6, 96.

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