egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network

The development of the Internet has made social communication increasingly important for maintaining relationships between people. However, advertising and fraud are also growing incredibly fast and seriously affect our daily life, e.g., leading to money and time losses, trash information, and privacy problems. Therefore, it is very important to detect anomalies in social networks. However, existing anomaly detection methods cannot guarantee the correct rate. Besides, due to the lack of labeled data, we also cannot use the detection results directly. In other words, we still need human analysts in the loop to provide enough judgment for decision making. To help experts analyze and explore the results of anomaly detection in social networks more objectively and effectively, we propose a novel visualization system, egoDetect, which can detect the anomalies in social communication networks efficiently. Based on the unsupervised anomaly detection method, the system can detect the anomaly without training and get the overview quickly. Then we explore an ego’s topology and the relationship between egos and alters by designing a novel glyph based on the egocentric network. Besides, it also provides rich interactions for experts to quickly navigate to the interested users for further exploration. We use an actual call dataset provided by an operator to evaluate our system. The result proves that our proposed system is effective in the anomaly detection of social networks

Visual analysis of people's mobility pattern from mobile phone

It is now possible to record and collect a large amount of phone calls in a city. Each phone call record usually contains the caller and callee IDs, date and time, and the base station where the phone calls are made. As mobile phones are widely used in our daily life, many human behaviors can be revealed by analyzing mobile phone data. In this paper, we propose a comprehensive visual analysis system which can be used to analyze the population's mobility pattern from millions of phone call records. Our system consists of three major components: 1) visual analysis of user groups in a base station; 2) visual analysis of the mobility pattern of different user groups making phone calls in certain base stations; 3) visual analysis of handoff phone call records. Some well established visualization techniques such as parallel coordinates and pixel-based representations have been integrated into our system. We also develop some novel visualization schemes such as Voronoi-diagram-based visual encoding to reveal the unique features of mobile phone data. We have applied our system to real mobile phone data collected in a large city and some interesting findings regarding people's mobility pattern have been obtained. Keywords: Visual analysis, mobility pattern, mobile phone

Sci-Fin: Visual Mining Spatial and Temporal Behavior Features from Social Media

Check-in records are usually available in social services, which offer us the opportunity to capture and analyze users’ spatial and temporal behaviors. Mining such behavior features is essential to social analysis and business intelligence. However, the complexity and incompleteness of check-in records bring challenges to achieve such a task. Different from the previous work on social behavior analysis, in this paper, we present a visual analytics system, Social Check-in Fingerprinting (Sci-Fin), to facilitate the analysis and visualization of social check-in data. We focus on three major components of user check-in data: location, activity, and profile. Visual fingerprints for location, activity, and profile are designed to intuitively represent the high-dimensional attributes. To visually mine and demonstrate the behavior features, we integrate WorldMapper and Voronoi Treemap into our glyph-like designs. Such visual fingerprint designs offer us the opportunity to summarize the interesting features and patterns from different check-in locations, activities and users (groups). We demonstrate the effectiveness and usability of our system by conducting extensive case studies on real check-in data collected from a popular microblogging service. Interesting findings are reported and discussed at last

Semantic Point Cloud Segmentation Using Fast Deep Neural Network and DCRF

Accurate segmentation of entity categories is the critical step for 3D scene understanding. This paper presents a fast deep neural network model with Dense Conditional Random Field (DCRF) as a post-processing method, which can perform accurate semantic segmentation for 3D point cloud scene. On this basis, a compact but flexible framework is introduced for performing segmentation to the semantics of point clouds concurrently, contribute to more precise segmentation. Moreover, based on semantics labels, a novel DCRF model is elaborated to refine the result of segmentation. Besides, without any sacrifice to accuracy, we apply optimization to the original data of the point cloud, allowing the network to handle fewer data. In the experiment, our proposed method is conducted comprehensively through four evaluation indicators, proving the superiority of our method

T-Watcher: A New Visual Analytic System for Effective Traffic Surveillance

Nowadays, big cities are suffering from severe traffic congestion as a result of the continuing increase in vehicles. Taxis equipped with GPS can be viewed as sensors of the traffic situation in city. However, trajectory data generated by taxi's GPS traces are often high-dimensional and contain large spatial and temporal attributes, which pose challenges for analysts. In this paper, based on taxi trajectory data, we present an interactive visual analytics system, T-Watcher, for monitoring and analyzing complex traffic situations in big cities. Users are able to use a carefully designed interface to monitor and inspect data interactively from three levels (region, road and vehicle views). We develop a visualization method to monitor and analyze traffic patterns for abnormal behaviors detection. In the region view of our system, global temporal changes in spatial evolution will be presented to users and can be interactively explored. The road view shows temporal changes to the traffic situations of significant segments of roads. The vehicle view uses a novel visualization method to track individual vehicles. Furthermore, the three views integrate important statistical and historical information related to traffic, which illustrate temporal changes of the traffic. We find that this design can help users explore historical information while monitoring traffic. We test our system on a real-life vehicle dataset collected from thousands of taxis and obtained some interesting findings. The experimental results confirm the effectiveness and efficiency of the proposed visual detection method. The analysis of the results also shows that our system is capable of effectively monitoring traffic and detecting abnormal traffic patterns

VAIT: A Visual Analytics System for Metropolitan Transportation

With the increasing availability of metropolitan transportation data, such as those from vehicle Global Positioning Systems (GPSs) and road-side sensors, it has become viable for authorities, operators, and individuals to analyze the data for better understanding of the transportation system and, possibly, improved utilization and planning of the system. We report our experience in building the Visual Analytics for Intelligent Transportation (VAIT) system, which is the first system on real-life large-scale data sets for intelligent transportation. Our key observation is that metropolitan transportation data are inherently visual as they are spatio-temporal around road networks. Therefore, we visualize and manage traffic data, together with digital maps, and support analytical queries through this interactive visual interface. As a case study, we demonstrate VAIT on real-world taxi GPS and meter data sets from 15 000 taxis running for two months in a Chinese city of over 10 million people. We discuss the technical challenges in data calibration, storage, visualization, and query processing and offer first-hand lessons learned from developing the system. Based on our extensive empirical experiment results, VAIT beats state-of-the-art methods and systems in terms of scalability, efficiency, and effectiveness and offers us an easy-to-use, efficient, and scalable platform to shed more light on intelligent transportation research