De strijd tegen witwassen wordt mede gevoerd vanuit een preventief ondernemingsperspectief. Een integere en beheerste bedrijfsvoering is gericht op het tegengaan van integriteits- en wetsovertredingen. Witwassen heeft zich ontwikkeld tot een centraal thema binnen het financieel toezichtrecht. Zowel de Wft als de Wwft stellen gedetailleerde eisen aan de interne controle en beheersingsinstrumenten, het bestuur en het klantbeleid. Een overkoepelende factor daarbij is het ‘ken uw klant’ beginsel (KYC) en het daarmee samenhangende Customer Due Dilligence (CDD). Deze bijdrage is gericht op de verschillen en overeenkomsten tussen de Wwft en Wft, in het licht van de Leidraad Wwft en Sw.
Event-based egocentric dynamic networks are an important class of networks widely seen in many domains. In this paper, we present a visual analytics approach for these networks by combining data-driven network simplifications with a novel visualization design - EgoNetCloud. In particular, an integrated data processing pipeline is proposed to prune, compress and filter the networks into smaller but salient abstractions. To accommodate the simplified network into the visual design, we introduce a constrained graph layout algorithm on the dynamic network. Through a real-life case study as well as conversations with the domain expert, we demonstrate the effectiveness of the EgoNetCloud design and system in completing analysis tasks on event-based dynamic networks. The user study comparing EgoNetCloud with a working system on academic search confirms the effectiveness and convenience of our visual analytics based approach.
Visualization helps users infer structures and relationships in the data by encoding information as visual features that can be processed by the human visual-perceptual system. However, users would typically need to expend significant effort to scan and analyze a large number of views before they can begin to recognize relationships in a visualization. We propose a technique to partially automate the process of analyzing visualizations. By deriving and analyzing image-space features from visualizations, we can detect perceptually-separable patterns in the information space. We summarize these patterns with a tree-based meta-visualization and present it to the user to aid exploration. We illustrate this technique with an example scenario involving the analysis of census data.
In this report, we propose ParkVis, a visual analytic system for tracking the unusual patterns of all paying park visitors. Using both communication and movement data, park officers can use our system to identify and gauge the extent of unusual activity occurring in the park.
We present a new visual analysis approach to support the comparative exploration of 2D vector-valued ensemble fields. Our approach enables the user to quickly identify the most similar groups of ensemble members, as well as the locations where the variation among the members is high. We further provide means to visualize the main features of the potentially multimodal directional distributions at user-selected locations. For this purpose, directional data is modelled using mixtures of probability density functions (pdfs), which allows us to characterize and classify complex distributions with relatively few parameters. The resulting mixture models are used to determine the degree of similarity between ensemble members, and to construct glyphs showing the direction, spread, and strength of the principal modes of the directional distributions. We also propose several similarity measures, based on which we compute pairwise member similarities in the spatial domain and form clusters of similar members. The hierarchical clustering is shown using dendrograms and similarity matrices, which can be used to select particular members and visualize their variations. A user interface providing multiple linked views enables the simultaneous visualization of aggregated global and detailed local variations, as well as the selection of members for a detailed comparison.
De novo design is a computational-chemistry method, where a computer program utilizes an optimization method, in our case an evolutionary algorithm, to design compounds with desired chemical properties. The optimization is performed with respect to a quantity called fitness, defined by the chemists. We present a tool that connects interactive visual analysis and evolutionary algorithm-based molecular design. We employ linked views to communicate different aspects of the data: the statistical distribution of molecule fitness, connections between individual molecules during the evolution and 3D molecular structure. The application is already used by chemists to explore and analyze the results of their evolution experiments and has proved to be highly useful.
How do computer architectures and physical architectures inform each other? This talk will explore the interconnection of data and visualization through an architectural and computational lens over the last 50 years, including the work of Steven Coons, Christopher Alexander, Richard Saul Wurman and others.
Visualization of all types of data is a highly effective tool used by researchers to gain insight into natural phenomena and to communicate their findings. It is also an increasingly popular means of presenting large scientific datasets to the general public in informal educational settings such as museums and planetaria. Visualization has appeared in many forms and in many cultures throughout digital history and contributes to the evolving visual language of science. Dr. Donna Cox and the Advanced Visualization Laboratory team at the National Center for Supercomputing Applications, University of Illinois, collaborate with science teams, writers, producers, educators, and media distribution professionals on content designed to engage a wide range of audiences. In the past 8 years alone, her collaborative educational and outreach projects have produced science narratives featuring data visualizations that have been viewed by more than 45 million people worldwide. Cox leads an NSF-funded project to create scientific visualizations and then test audiences' understanding of the phenomenon that is being presented. Large-scale computational data present unique visualization challenges for producers of high-resolution, production-quality 3D IMAX movies; feature films; and museum fulldomes. In this keynote, Cox will provide a visual feast of major projects, including new digital fulldome museum shows and award-winning IMAX films.
In this paper, we address the Mini Task I of Min-Challenge I, pluging in knowledge and some techniques of data mining and information visualization into our assignment. The background of Mini-Challenge I describes a virtual story of a famous gas company(GAStech), which had suffered employees missing during company celebration, asking for help to find out the suspicious criminals. As an analyst, our task is to use visual analytics to analyze the available data and develop responses to the questions. By using Matlab, Python and D3. js, we gave out a reasonable conclusion and proper answers to the questions.
Dodeca-Rings Map is the visual analytics system we designed to analyze geo-temporal traffic problems. The system is organized by three kinds of visualizations: dodecagons that show events on the map (Fig. 1 A & B), activity temporal charts (Fig. 1 C), and a social relationship matrix (Fig. 1D). We used it to solve the VAST 2014 Mini-Challenge 2 and then the Grand Challenge. The given data sets include two weeks of vehicle GPS tracking data, credit and loyalty card transaction data, as well as vehicle assignments data. The challenge requires us to describe common daily routines for the car drivers, identify unusual events, and address the uncertainties and conflicts inherent in this data.
The VAST Challenge 2014 MC3 featured a dataset of roughly 4000 microblog messages and 200 emergency callcenter reports produced in the fictitious city of Abila. The task was to identify relevant events and outliers in the data and highlight observations that are connected to an earlier kidnapping of employees from a company called GASTECH. In contrast to earlier challenges, the focus was on real-time data processing. The microblog messages were hosted on a web-server that simulated real-time streaming of the data during a 4.5 hour period and the challenge participants had to monitor and analyze them as they were transmitted. To tackle the challenge we developed ScatterScopes, a real-time enabled visual analytics system that fosters understanding of ongoing events by means of spatiotemporal overview and hierarchical drilldown. Trends and anomalies in space, time and content can be quickly identified with the system using interactive maps, sentiment timelines and textual search. Once interesting or suspicious subsets of elements are selected, their inherent topic structure can be further dissected based on a highly interactive treemap of message clusters. Subset selection and recombination is furthermore supported by a filter-and-flow mechanism that can also be used to formulate and test hypotheses based on Boolean logic. ScatterScopes has been used by our team to successfully identify and describe all events hidden in the MC3 data streams.
Advanced digital forensics technologies provide powerful basis in criminal investigation. Due to the complexity and diversity of data as well as the increasing quantity of the data, traditional digital forensics technologies have already can not adapt to the analysis requirements. This paper provides a visualization approach to analyze multiple types of data for digital forensics which provide users three interrelated tools: the RadViz tool, the PMViz tool and the SGGViz tool. Our solution focuses on the correlation analysis of trajectories and transactions, and it plays an important role in the process of analyzing the case in VAST 2014 Mini-challenge 2.
We present a new approach to visualizing the climate data of multi-dimensional, time-series, and geo-related characteristics. Our approach integrates three new highly interrelated visualization techniques, and uses the same input data types as in the traditional model-based analysis methods. As the main visualization view, Global Radial Map is used to identify the overall state of climate changes and provide users with a compact and intuitive view for analyzing spatial and temporal patterns at the same time. Other two visualization techniques, providing complementary views, are specialized in analysing time trend and detecting abnormal cases, which are two important analysis tasks in any climate change study. Case studies and expert reviews have been conducted, through which the effectiveness and scalability of the proposed approach has been confirmed.
As terrors grow wild around the world, visualization plays an important role in helping analyze security situation and solve terrorism and emergency cases. In this paper, we present a system of multiple interactive and coordinated views to help visualize and analyze several kinds of large-scale data and complex dataseis. This design assists to explore social network involved and analyze natural language used in large-scale dataseis, then to make an integrated analysis and presentation of all kinds of objects involved in cases in multiple perspectives and hierarchies. We implemented dataseis from VAST Challenge 2014 mini challenge 1 to verify the usability of our design.
We present VisIRR, an interactive visual information retrieval and recommendation system for large-scale document data. Starting with a query, VisIRR visualizes the retrieved documents in a scatter plot along with their topic summary. Next, based on interactive personalized preference feedback on the documents, VisIRR collects and visualizes potentially relevant documents out of the entire corpus so that an integrated analysis of both retrieved and recommended documents can be performed seamlessly.
