Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. This work-in-progress paper focuses on misinformation in graphical representations utilizing a case-based learning approach. The misleading data visualization examples are surveyed and projected onto fundamental units of visual communication, such as size, value, shape, size, and position. This work aims at helping viewers understand the root causes of the misuse, as well as provide basic principles for making more effective visualizations.

The explosion of social media has paved a way for big data in which entrepreneurs use this data to find out potential customers, market demands, individual behavior, thereby to improve existing products, to create new products according to users' need, or to analyze and evaluate financial risks. The challenges of the heterogeneity and fragmentation of data make it difficult for analysts to fully exploit the benefit of deluge information. Available statistical software lacks customization and address unknown research questions. This paper proposes FinanViz, a visual analytics tool for analyzing financial news on social media. The principal aim of FinanViz is to observe the dynamic behavior of terms/words over time along with their proximity to other terms/words. The tool provides an intuitive, interactive exploration of the financial topics and what events are emerging in which we would argue that it will give hints for financial marketers in the decision making process.

Visualizing and analyzing geospatial and temporal observations are common tasks for many application domains. In this paper, we introduce UFO Tracker, a visual analytic tool for analyzing unidentified flying object sightings from the National UFO Reporting Center. The goal here is to give the user a higher level view of where different types of sightings occur, to investigate whether sightings are increasing or decreasing over time, to discover the connections between different events which might happen at different geographic areas, and to quickly identify typical incidents at a given period of time without reading the whole sightings through topic modelling. Multiple visualization and data mining techniques are combined to make sense the increasingly large UFO reports which get updated hourly. The usefulness of the application is evaluated through a case study where anon-expert in ufology can find some typical interesting sightings. Our application can also be able to detect some misleading events such as missile launch or fireworks on a specific day through keywords and topic extraction. One limitation of our application is the data which is not up-todate when new sightings are posted since the application pulled and processed data locally. Our initial application targets UFO sighting reports. However, we believe our approach has wider applications in other research domains, such as analyzing text corpus obtained from social media.

The "Internet of Things" is changing the way companies operate and consumers behave. Therefore, it is essential to capture trends in "Internet of Things". This paper proposes IoTViz, a visual analytics tool for analyzing "Internet of Things" news on social media. The principal aim of IoTViz is to observe the dynamic behavior of topics along with their proximity to other dimensions such as user comments and ratings in multiple coordinated views. IoTViz provides an interactive exploration of the IoT topics and supports of a range of interactive features, such as linking and filtering, allowing users to narrow down events of interest quickly. It is interesting to filter and visualize IoT news regarding the individual organization, e.g., user opinions/ratings regarding a company or its products.

This demonstration paper introduces MalViz, a visual analytic tool for analyzing malware behavioral patterns through process monitoring events. The goals of this tool are: 1) to investigate the relationship and dependencies among processes interacted with a running malware over a certain period of time, 2) to support professional security experts in detecting and recognizing unusual signature-based patterns exhibited by a running malware, and 3) to help users identify infected system and users' libraries that the malware has reached and possibly tampered. A case study is conducted in a virtual machine environment with a sample of four malware programs. The result of the case study shows that the visualization tool offers a great support for experts in software and system analysis and digital forensics to profile and observe malicious behavior and further identify the traces of affected software artifacts.