Visualizing Statistical Analysis of Multi Tabular Attributes with SQL

Data extraction and data management is playing a vital role in today’s world. Databases are widely used by all the organizations. Analysis of data is very crucial when comparisons are done between different subjects. There are many software’s developed for statistical analysis of data. Various visualization techniques are used for representation. In statistical analysis of tabular data in databases, data is either extracted as external sheets or the statistical software’s are connected to the servers to test data. In our approach, we introduce a web based interface where users can select any number of attributes and view the results with some simple visualizations. SQL queries are written for different methodologies to analyze data. Formulas and structure of all the queries are visualized and represented for the users to understand the query processing and the test methodologies. All the statistical tests are performed on multi tabular data. Ranking is performed on categorical data to replace these values with ranks. With the selected attributes, views are created in the database with the ranks replacing the categorical values in these views. The developed interface is tested with different users to evaluate the visualizations used and the understandability of the statistical tests.Computer Scienc

Contextualized Analysis of Movement Events

For understanding the circumstances, causes, and consequences of events that may happen during movement (e.g., harsh brake, sharp turn), it is necessary to analyze event context. The context includes dynamic attributes of the moving objects before and after the event and external context elements such as other moving objects, weather, terrain, etc. To explore events in context, we propose an analytical workflow including event contextualization, context pattern detection, and exploration of the spatio-temporal distribution of the detected patterns. The approach involves clustering of events based on the similarity of their contexts and interactive visual techniques for exploration of the distribution of the clusters in time, geographic space, and multidimensional attribute space. In close collaboration with domain experts, we apply our method to real-world vehicle trajectories with the purpose of identifying and investigating potentially dangerous driving behaviors

MyEvents: a personal visual analytics approach for mining key events and knowledge discovery in support of personal reminiscence

Reminiscence is an important aspect in our life. It preserves precious memories, allows us to form our own identities and encourages us to accept the past. Our work takes advantage of modern sensor technologies to support reminiscence, enabling self-monitoring of personal activities and individual movement in space and time on a daily basis. This paper presents MyEvents, a web-based personal visual analytics platform designed for non-computing experts, that allows for the collection of long-term location and movement data and the generation of event mementos. Our research is focused on two prominent goals in event reminiscence: 1) selection subjectivity and human involvement in the process of self knowledge discovery and memento creation; and 2) the enhancement of event familiarity by presenting target events and their related information for optimal memory recall and reminiscence. A novel multi-significance event ranking model is proposed to determine significant events in the personal history according to user preferences for event category, frequency and regularity. The evaluation results show that MyEvents effectively fulfils the reminiscence goals and tasks.