Big Data Visualization

Visualization helps in boosting human’s cognitive processes. It has been proved to offer a range of advantages to the process in which people acquire knowledge, which include (1) understanding of large data, (2) unexpectedly interesting ways of perceiving information, (3) quick recognition of errors and outliers in data set, (4) identification of patterns in data, and (5) ease at hypotheses formation out of the data.

With the mentioned benefits, information visualization has been widely deployed in many areas, from scientific study, crime analysis to business and education. Hence, a wide range of techniques and methods of enhancing visualization representation and interactivity have been studied and developed over the last decades. Some noteworthy studies include: visualization of unstructured temporal data with parallel rendering algorithm, taxonomies of interaction techniques, the focus+ context technique, treemaps for visualizing hierarchical data structure while making use of all of the available space, and artificial reality in visualization.

With the remarkable growth in data generation, visualization plays a more essential part than ever in facilitating the process in which people obtain insights into data. The total amount of data generated is expected to experience a significant growth, surpassing approximately 40 zeta bytes in 2020, and it will be increasing by 40% each year in the next decade. Nonetheless, the study showed that only a small portion of the collected data was tagged (around 3%) and the data that were analyzed was even less (about 0.5% of the world’s digital data). This necessitates approaches to representing data in a more intuitive way so that data is ready for users to easily perceive. Visualization is expected to help in solving the challenge.

The same situation occurs in education and health. Knowledge has had much shorter life than before. The application of data visualization in education, visualizing learning activities particularly can help improve learning analytics significantly. However, the process to choose what information is to be shown and the manner of displaying the information so that learners can find it fun and relevant have remained a challenge.

We propose an approach of visualizing learning activities in social networks using a 3D scene platform. We have developed a new model of visualizing learning activities using 3D data visualization framework. The method was evaluated for its effectiveness in making the learning process more fun and motivating and effective. Questionnaires and interviews has been used for the evaluation. Results show that our method of visualization of learning activities in social network made learning more fun and easier. Additionally, it also indicates that the model helps improve the students’ engagement and motivation on the subjects.