Visual analytics for BigData variety and its behaviours

Abstract

BigData, defined as structured and unstructured data containing images, videos, texts, audio and other forms of data collected from multiple datasets, is too big, too complex and moves too fast to analyze using traditional methods. This has given rise to a few issues that must be addressed; 1) how to analyze BigData across multiple datasets, 2) how to classify the different data forms, 3) how to identify BigData patterns based on its behaviours, 4) how to visualize BigData attributes in order to gain a better understanding of data. It is therefore necessary to establish a new framework for BigData analysis and visualization. In this paper, we have extended our previous works for classifying the BigData attributes into the "5Ws" dimensions based on different data behaviours. Our approach not only classifies BigData attributes for different data forms across multiple datasets, but also establishes the "5Ws" densities to represent the characteristics of data flow patterns. We use additional non-dimensional parallel axes in parallel coordinates to display the ?5Ws? sending and receiving densities, which provide more analytic features for BigData analysis. The experiment shows that our approach with parallel coordinate visualization can be efficiently used for BigData analysis and visualization.

A Maturity Analysis of Big Data Technologies

“…The concept addresses large volumes of complex data, rapid growing data sets that may come from different autonomous sources. In recent approaches, Big Data is characterized by principles known as the 4V -Volume, Variety, Velocity and Veracity [3]. There are opinions about accepting other principles as Big Data characteristics, such as Value.…”

“…Solutions Big Data management systems receive data from various sources, gathering a huge volume of data often represented by heterogeneous and diverse dimensionalities [3]. Different information collectors prefer their own schemata or protocols for data recording, and the nature of different applications also result in diverse data representations.…”

2D approach measuring multidimensional data pattern in big data visualization

“…Assume the first dimension j=1 and last dimension j=m in equation (1), are set for comparing, the dataset can then be illustrated as 2D subsets D 1 and D m , with the data pattern subset D pat in equation (2).…”

“…Big Data, a structured and unstructured data that contains texts, images, audios, videos, click streams and other forms of data, collected from multiple datasets, are rapidly growing in size and complexity [1]. It comes from everywhere in our life, and so is too big, too complex and moves too fast.…”

A multicriteria optimization approach for the stock market feature selection

“…The LSTM network model is employed within this research in order to estimate if it is a good time to open a trade or it is better to idle. During the data transformation part, a huge amount of features are extracted from the limit order book and dealing with a large amount of various types of data has become an important challenge in computer science in recent years (see [47] and [27]). These extracted features describe market behavior.…”