Show Me Your Face: Towards an Automated Method to Provide Timely Guidance in Visual Analytics.

Providing guidance during a Visual Analytics session can support analysts in pursuing their goals more efficiently. However, the effectiveness of guidance depends on many factors: Determining the right timing to provide it is one of them. Although in complex analysis scenarios choosing the right timing could make the difference between a dependable and a superfluous guidance, an analysis of the literature suggests that this problem did not receive enough attention. In this paper, we describe a methodology to determine moments in which guidance is needed. Our assumption is that the need of guidance would influence the user state-of-mind, as in distress situations during the analytical process, and we hypothesize that such moments could be identified by analyzing the user's facial expressions. We propose a framework composed by a facial recognition software and a machine learning model trained to detect when to provide guidance according to changes of the user facial expressions. We trained the model by interviewing eight analysts during their work and ranked multiple facial features based on their relative importance in determining the need of guidance. Finally, we show that by applying only minor modifications to its architecture, our prototype was able to detect a need of guidance on the fly and made our methodology well suited also for real-time analysis sessions. The results of our evaluations show that our methodology is indeed effective in determining when a need of guidance is present, which constitutes a prerequisite to providing timely and effective guidance in VA.

NEVA: Visual Analytics to Identify Fraudulent Networks.

Trust-ability, reputation, security and quality are the main concerns for public and private financial institutions. To detect fraudulent behaviour, several techniques are applied pursuing different goals. For well-defined problems, analytical methods are applicable to examine the history of customer transactions. However, fraudulent behaviour is constantly changing, which results in ill-defined problems. Furthermore, analysing the behaviour of individual customers is not sufficient to detect more complex structures such as networks of fraudulent actors. We propose NEVA (Network dEtection with Visual Analytics), a Visual Analytics exploration environment to support the analysis of customer networks in order to reduce false-negative and false-positive alarms of frauds. Multiple coordinated views allow for exploring complex relations and dependencies of the data. A guidance-enriched component for network pattern generation, detection and filtering support exploring and analysing the relationships of nodes on different levels of complexity. In six expert interviews, we illustrate the applicability and usability of NEVA.

Guide Me in Analysis: A Framework for Guidance Designers.

Guidance is an emerging topic in the field of visual analytics. Guidance can support users in pursuing their analytical goals more efficiently and help in making the analysis successful. However, it is not clear how guidance approaches should be designed and what specific factors should be considered for effective support. In this paper, we approach this problem from the perspective of guidance designers. We present a framework comprising requirements and a set of specific phases designers should go through when designing guidance for visual analytics. We relate this process with a set of quality criteria we aim to support with our framework, that are necessary for obtaining a suitable and effective guidance solution. To demonstrate the practical usability of our methodology, we apply our framework to the design of guidance in three analysis scenarios and a design walk-through session. Moreover, we list the emerging challenges and report how the framework can be used to design guidance solutions that mitigate these issues.

LightGuider: Guiding Interactive Lighting Design using Suggestions, Provenance, and Quality Visualization.

LightGuider is a novel guidance-based approach to interactive lighting design, which typically consists of interleaved 3D modeling operations and light transport simulations. Rather than having designers use a trial-and-error approach to match their illumination constraints and aesthetic goals, LightGuider supports the process by simulating potential next modeling steps that can deliver the most significant improvements. LightGuider takes predefined quality criteria and the current focus of the designer into account to visualize suggestions for lighting-design improvements via a specialized provenance tree. This provenance tree integrates snapshot visualizations of how well a design meets the given quality criteria weighted by the designer's preferences. This integration facilitates the analysis of quality improvements over the course of a modeling workflow as well as the comparison of alternative design solutions. We evaluate our approach with three lighting designers to illustrate its usefulness.

Capturing and Visualizing Provenance From Data Wrangling.

Data quality management and assessment play a vital role for ensuring the trust in the data and its fitness-of-use for subsequent analysis. The transformation history of a data wrangling system is often insufficient for determining the usability of a dataset, lacking information how changes affected the dataset. Capturing workflow provenance along the wrangling process and combining it with descriptive information as data provenance can enable users to comprehend how these changes affected the dataset, and if they benefited data quality. We present DQProv Explorer, a system that captures and visualizes provenance from data wrangling operations. It features three visualization components: allowing the user to explore the provenance graph of operations and the data stream, the development of quality over time for a sequence of wrangling operations applied to the dataset, and the distribution of issues across the entirety of the dataset to determine error patterns.

EVA: Visual Analytics to Identify Fraudulent Events.

Financial institutions are interested in ensuring security and quality for their customers. Banks, for instance, need to identify and stop harmful transactions in a timely manner. In order to detect fraudulent operations, data mining techniques and customer profile analysis are commonly used. However, these approaches are not supported by Visual Analytics techniques yet. Visual Analytics techniques have potential to considerably enhance the knowledge discovery process and increase the detection and prediction accuracy of financial fraud detection systems. Thus, we propose EVA, a Visual Analytics approach for supporting fraud investigation, fine-tuning fraud detection algorithms, and thus, reducing false positive alarms.

Characterizing Guidance in Visual Analytics.

Visual analytics (VA) is typically applied in scenarios where complex data has to be analyzed. Unfortunately, there is a natural correlation between the complexity of the data and the complexity of the tools to study them. An adverse effect of complicated tools is that analytical goals are more difficult to reach. Therefore, it makes sense to consider methods that guide or assist users in the visual analysis process. Several such methods already exist in the literature, yet we are lacking a general model that facilitates in-depth reasoning about guidance. We establish such a model by extending van Wijk's model of visualization with the fundamental components of guidance. Guidance is defined as a process that gradually narrows the gap that hinders effective continuation of the data analysis. We describe diverse inputs based on which guidance can be generated and discuss different degrees of guidance and means to incorporate guidance into VA tools. We use existing guidance approaches from the literature to illustrate the various aspects of our model. As a conclusion, we identify research challenges and suggest directions for future studies. With our work we take a necessary step to pave the way to a systematic development of guidance techniques that effectively support users in the context of VA.

Information requisition is the core of guideline-based medical care: which information is needed for whom?

RATIONALE, AIMS AND OBJECTIVES: It is mandatory for the design of an efficient software product to know the different groups of users of a software tool, the tasks the users want to perform with it, and the information that is required for it. Our goal is to establish a comprehensive information source for the development of a consistent software environment supporting all tasks emerging from the creation to the execution of a computerized clinical practice guideline (CPG) for different user groups. METHODS: We conducted a comprehensive literature review to investigate the different user groups of a computerized CPG as well as their specific information needs. RESULTS: We provide a complete catalogue of every single aspect that may be related to information needs of any party concerned. In particular, we give detailed information on the tasks of guideline modellers on the one hand, and clinical information needs (i.e. information needs of physicians, nurses, nurse practitioners and patients) on the other hand. CONCLUSION: By providing categorized information from several studies and publications, we establish an exhaustive information basis for the design of a useful software tool facilitating the formalization and the execution of a CPG.

Easing semantically enriched information retrieval-An interactive semi-automatic annotation system for medical documents.

Mapping medical concepts from a terminology system to the concepts in the narrative text of a medical document is necessary to provide semantically accurate information for further processing steps. The MetaMap Transfer (MMTx) program is a semantic annotation system that generates a rough mapping of concepts from the Unified Medical Language System (UMLS) Metathesaurus to free medical text, but this mapping still contains erroneous and ambiguous bits of information. Since manually correcting the mapping is an extremely cumbersome and time-consuming task, we have developed the MapFace editor.The editor provides a convenient way of navigating the annotated information gained from the MMTx output, and enables users to correct this information on both a conceptual and a syntactical level, and thus it greatly facilitates the handling of the MMTx program. Additionally, the editor provides enhanced visualization features to support the correct interpretation of medical concepts within the text. We paid special attention to ensure that the MapFace editor is an intuitive and convenient tool to work with. Therefore, we recently conducted a usability study in order to create a well founded background serving as a starting point for further improvement of the editor's usability.