Information visualization for DNA microarray data analysis: A critical review

Graphical representation may provide effective means of making sense of the complexity and sheer volume of data produced by DNA microarray experiments that monitor the expression patterns of thousands of genes simultaneously. The ability to use ldquoabstractrdquo graphical representation to draw attention to areas of interest, and more in-depth visualizations to answer focused questions, would enable biologists to move from a large amount of data to particular records they are interested in, and therefore, gain deeper insights in understanding the microarray experiment results. This paper starts by providing some background knowledge of microarray experiments, and then, explains how graphical representation can be applied in general to this problem domain, followed by exploring the role of visualization in gene expression data analysis. Having set the problem scene, the paper then examines various multivariate data visualization techniques that have been applied to microarray data analysis. These techniques are critically reviewed so that the strengths and weaknesses of each technique can be tabulated. Finally, several key problem areas as well as possible solutions to them are discussed as being a source for future work

A characteristic-based visual analytics approach to detect subtle attacks from NetFlow records

Security is essentially important for any enterprise networks. Denial of service, port scanning, and data exfiltration are among of the most common network intrusions. It\u27s urgent for network administrators to detect such attacks effectively and efficiently from network traffic. Though there are many intrusion detection systems (IDSs) and approaches, Visual Analytics (VA) provides a human-friendly approach to detect network intrusions with situational awareness functionality. Overview visualization is the first and most important step in a VA approach. However, many VA systems cannot effectively identify subtle attacks from massive traffic data because of the incapability of overview visualizations. In this work, we developed two overviews and tried to identify subtle attacks directly from these two overviews. Moreover, zoomed-in visualizations were also provided for further investigation. The primary data source was NetFlow and we evaluated the VA system with datasets from Mini Challenge 3 of VAST challenge 2013. Evaluation results indicated that the VA system can detect all the labeled intrusions (denial of service, port scanning and data exfiltration) with very few false alerts

A Model-driven Visual Analytic Framework for Local Pattern Analysis

The ultimate goal of any visual analytic task is to make sense of the data and gain insights. Unfortunately, the process of discovering useful information is becoming more challenging nowadays due to the growing data scale. Particularly, the human cognitive capabilities remain constant whereas the scale and complexity of data are not. Meanwhile, visual analytics largely relies on human analytic in the loop which imposes challenge to traditional human-driven workflow. It is almost impossible to show every aspect of details to the user while diving into local region of the data to explain phenomenons hidden in the data. For example, while exploring the data subsets, it is always important to determine which partitions of data contain more important information. Also, determining the subset of features is vital before further doing other analysis. Furthermore, modeling on these subsets of data locally can yield great finding but also introduces bias. In this work, a model driven visual analytic framework is proposed to help identify interesting local patterns from the above three aspects. This dissertation work aims to tackle these subproblems in the following three topics: model-driven data exploration, model-driven feature analysis and local model diagnosis. First, the model-driven data exploration focus on the problem of modeling subset of data to identify the co-movement of time-series data within certain subset time partitions, which is an important application in a number of domains such as medical science, finance, business and engineering. Second, the model-driven feature analysis is to discover the important subset of interesting features while analyzing local feature similarities. Within the financial risk dataset collected by domain expert, we discover that the feature correlation among different data partitions (i.e., small and large companies) are very different. Third, local model diagnosis provides a tool to identify interesting local regression models at local regions of the data space which makes it possible for the analysts to model the whole data space with a set of local models while knowing the strength and weakness of them. The three tools provide an integrated solution for identifying interesting patterns within local subsets of data

A Pattern Approach to Examine the Design Space of Spatiotemporal Visualization

Pattern language has been widely used in the development of visualization systems. This dissertation applies a pattern language approach to explore the design space of spatiotemporal visualization. The study provides a framework for both designers and novices to communicate, develop, evaluate, and share spatiotemporal visualization design on an abstract level. The touchstone of the work is a pattern language consisting of fifteen design patterns and four categories. In order to validate the design patterns, the researcher created two visualization systems with this framework in mind. The first system displayed the daily routine of human beings via a polygon-based visualization. The second system showed the spatiotemporal patterns of co-occurring hashtags with a spiral map, sunburst diagram, and small multiples. The evaluation results demonstrated the effectiveness of the proposed design patterns to guide design thinking and create novel visualization practices