Interactive Exploration of the Network Behavior of Personal Machines

Personal machines are often the weakest points within a large network. Although they run an ever-increasing number of network services, these machines are often controlled by users who are unaware of security threats. Thus, a well-informed attacker can, with modest effort, identify and gain control over personal machines. However, system administrators need to know the tools and techniques used for attacks while simultaneously needing to invest huge analytical efforts to detect malicious behavior in the vast volumes of network traffic. In our research project we investigate the idea that an understanding of the regular behavior of personal machines can improve the chance of detecting the point in time when a machine shows malicious behavior. We propose a visual exploration system based on a data abstraction layer and temporal visual representations of the network traffic. The data abstraction layer enables an interactive change in the level of detail of the network traffic while temporal visualizations help system administrators to detect unexpected network traffic. In the next phase of this project, we will conduct experiments to get a good feel about the limits of our system in detecting malicious behavior in real-world scenarios

PyRQA -- Conducting Recurrence Quantification Analysis on Very Long Time Series Efficiently

PyRQA is a software package that efficiently conducts recurrence quantification analysis (RQA) on time series consisting of more than one million data points. RQA is a method from non-linear time series analysis that quantifies the recurrent behaviour of systems. Existing implementations to RQA are not capable of analysing such very long time series at all or require large amounts of time to calculate the quantitative measures. PyRQA overcomes their limitations by conducting the RQA computations in a highly parallel manner. Building on the OpenCL framework, PyRQA leverages the computing capabilities of a variety of parallel hardware architectures, such as GPUs. The underlying computing approach partitions the RQA computations and enables to employ multiple compute devices at the same time. The goal of this publication is to demonstrate the features and the runtime efficiency of PyRQA. For this purpose we employ a real-world example, comparing the dynamics of two climatological time series, and a synthetic example, reducing the runtime regarding the analysis of a series consisting of over one million data points from almost eight hours using state-of-the-art RQA software to roughly 69 seconds using PyRQA.Comment: 15 pages, 3 figure

Deep learning, remote sensing and visual analytics to support automatic flood detection

Floods can have devastating consequences on people, infrastructure, and the ecosystem. Satellite imagery has proven to be an efficient instrument in supporting disaster management authorities during flood events. In contrast to optical remote sensing technology, Synthetic Aperture Radar (SAR) can penetrate clouds, and authorities can use SAR images even during cloudy circumstances. A challenge with SAR is the accurate classification and segmentation of flooded areas from SAR imagery. Recent advancements in deep learning algorithms have demonstrated the potential of deep learning for image segmentation demonstrated. Our research adopted deep learning algorithms to classify and segment flooded areas in SAR imagery. We used UNet and Feature Pyramid Network (FPN), both based on EfficientNet-B7 implementation, to detect flooded areas in SAR imaginary of Nebraska, North Alabama, Bangladesh, Red River North, and Florence. We evaluated both deep learning methods' predictive accuracy and will present the evaluation results at the conference. In the next step of our research, we develop an XAI toolbox to support the interpretation of detected flooded areas and algorithmic decisions of the deep learning methods through interactive visualizations

Information at your finger tips: Exploring the US Census Data

U.S. National Level plot there are high income clusters on the East Side of Central Park, and in suburbs of Chicago but not its downtown neighborhood. In the San Francisco area we can identify Silicon Valley; the income in this small area is significantly greater than average (Data=Block Level; Global Shape=Cartogram based on Household Distribution).

Circle View - A New Approach for Visualizing Time related Multidimensional Data Sets

This paper introduces a new approach for visualizing multidimensional time-referenced data sets, called Circle View. The Circle View technique is a combination of hierarchical visualization techniques, such as treemaps [6], and circular layout techniques such as Pie Charts and Circle Segments [2]. The main goal is to compare continuous data changing their characteristics over time in order to identify patterns, exceptions and similarities in the data. To achieve this goal Circle View is a intuitive and easy to understand visualization interface to enable the user very fast to acquire the information needed. This is an important feature for fast changing visualization caused by time related data streams. Circle View supports the visualization of the changing characteristics over time, to allow the user the observation of changes in the data. Additionally it provides user interaction and drill down mechanism depending on user demands for a effective exploratory data analysis. There is also the capability of exploring correlations and exceptions in the data by using similarity and ordering algorithms

FP-Viz: Visual Frequent Pattern Mining

Frequent pattern mining plays an essential role in many data analysis tasks including association-, correlation-, and causality analysis and has broad applications. Examples are market basket analysis and web click stream analysis. Although a number of efficient methods for mining frequent patterns where proposed in the past, there exist only a small number of visual exploration tools for discovering frequent patterns. In this paper we present a novel visualization technique for exploring frequent itemsets interactivly, based on a radial visual layout approach

Scalable Pixel based Visual Data Exploration

Pixel based visualization techniques have proven to be of high value in visual data exploration. Mapping data points to pixels not only allows the analysis and visualization of large data sets, but also provides an intuitive way to convert raw data into a graphical form. The graphical representation fosters new insights and encourages the formation and validation of new hypotheses to the end of better problem solving and gaining deeper domain knowledge. However, the ever increasing amount of information leads to new challenges for pixel-based techniques and concepts, especially if the number of data points significantly exceeds the available screen resolution. The paper focuses on ways to improve the scalability of pixel based techniques by proposing a multiresolution pixel-oriented visual exploration approach for large datasets. This approach combines clustering techniques with pixel-oriented mappings to preserve local clusters while providing space filling relevancedriven representations of the whole data set or portions of the data. The paper presents different application scenarios from the fields of financial analysis, geo-visualization, and network data analysis that clearly show the practical benefit of the multi-resolution approach for tackling the problem of scalability