A Quantitative Analysis-based Algorithm for Optimal Data Signature Construction of Traffic Data Sets
Jasmine A. Malinao, Richelle Ann B. Juayong, Rona May U. Tadlas, Jhoirene Clemente, Erlo Robert F. Oquendo, John Boaz Lee, Ma. Sheilah Gaabucayan-Napalang, Jose Regin F. Regidor, Henry N. Adorna · Journal of Information Processing · 2012
In this paper, a new set of m-dimensional Power Spectrum-based data signatures is derived to obtain better Vector Fusion 2-dimensional visualizations of a time series and periodic n-dimensional traffic data set as compared with visualizations produced from using the entire set of n-dimensional Power Spectrum representations in literature, where m « n. We were able to ascertain that 4-dimensional data signatures provide empirically optimal representations with respect to the data set used. We have achieved ≈ 97.6% reduction in terms of data representation of the original nD data set with the signatures. We propose an algorithm that determines how good the selected set of m-dimensional signatures represents the n-dimensional data set in 2 dimensions in quantitative terms. We use the Vector Fusion visualization algorithm in transforming each signature from m dimensions into 2 dimensions. An improved set of qualitative criterion is drawn to measure the goodness of the 2-dimensional data signature-based visual representation of the original n-dimensional data set. Finally, we provide empirical testing, discuss the results, and conclude the contributions of the proposed methods.