Hermes:Guidance-enriched Visual Analytics for economic network exploration

The economy of a country can be modeled as a complex system in which several players buy and sell goods from each other.By analyzing the investment flows,it is possible to reconstruct the supply chain for the production of most goods,whose understanding is important to analysts and public officials interested in creating and evaluating strategies for informed and strategic decision making,for instance,adjusting tax policies.Those networks of players and investments,however,tend to be complex and very dense,which leads to over-plotted visualizations that obfuscate precious information such as the dependencies between productive sectors and regions.In this paper,we propose Hermes,a guidanceenriched Visual Analytics environment(named after the Greek God of Commerce)for the exploration of complex economic networks,to uncover supply chains,regions’productivity,and sector-to-sector relationships.With practical knowledge regarding guidance,we designed and implemented a visual sub-graph querying approach to extract patterns from such complex investment graphs obtained from real-world data.We present a three-fold evaluation of the system:we perform a qualitative evaluation of our approach with three domain experts,a separate assessment of the proposed guidance features with an expert researcher in this field,and a case study of Hermes using a bank account network dataset to demonstrate the generalizability of our approach.

Visual analytics for event detection: Focusing on fraud

The detection of anomalous events in huge amounts of data is sought in many domains.For instance,in the context of financial data,the detection of suspicious events is a prerequisite to identify and prevent attempts to defraud.Hence,various financial fraud detection approaches have started to exploit Visual Analytics techniques.However,there is no study available giving a systematic outline of the different approaches in this field to understand common strategies but also differences.Thus,we present a survey of existing approaches of visual fraud detection in order to classify different tasks and solutions,to identify and to propose further research opportunities.In this work,fraud detection solutions are explored through five main domains:banks,the stock market,telecommunication companies,insurance companies,and internal frauds.The selected domains explored in this survey were chosen for sharing similar time-oriented and multivariate data characteristics.In this survey,we(1)analyze the current state of the art in this field;(2)define a categorization scheme covering different application domains,visualization methods,interaction techniques,and analytical methods which are used in the context of fraud detection;(3)describe and discuss each approach according to the proposed scheme;and(4)identify challenges and future research topics.

Perspectives of visualization onboarding and guidance in VA

A typical problem in Visual Analytics(VA)is that users are highly trained experts in their application domains,but have mostly no experience in using VA systems.Thus,users often have difficulties interpreting and working with visual representations.To overcome these problems,user assistance can be incorporated into VA systems to guide experts through the analysis while closing their knowledge gaps.Different types of user assistance can be applied to extend the power of VA,enhance the user’s experience,and broaden the audience for VA.Although different approaches to visualization onboarding and guidance in VA already exist,there is a lack of research on how to design and integrate them in effective and efficient ways.Therefore,we aim at putting together the pieces of the mosaic to form a coherent whole.Based on the Knowledge-Assisted Visual Analytics model,we contribute a conceptual model of user assistance for VA by integrating the process of visualization onboarding and guidance as the two main approaches in this direction.As a result,we clarify and discuss the commonalities and differences between visualization onboarding and guidance,and discuss how they benefit from the integration of knowledge extraction and exploration.Finally,we discuss our descriptive model by applying it to VA tools integrating visualization onboarding and guidance,and showing how they should be utilized in different phases of the analysis in order to be effective and accepted by the user.

You get by with a little help:The effects of variable guidance degrees on performance and mental state

Since it can be challenging for users to effectively utilize interactive visualizations,guidance is usually provided to assist users in solving tasks.Guidance is mentioned as an effective mean to overcome stall situations occurring during the analysis.However,the effectiveness of a peculiar guidance solution usually varies for different analysis scenarios.The same guidance may have different effects on users with(1)different levels of expertise.The choice of the appropriate(2)degree of guidance and the type of(3)task under consideration also affect the positive or negative outcome of providing guidance.Considering these three factors,we conducted a user study to investigate the effectiveness of variable degrees of guidance with respect to the user’s previous knowledge in different analysis scenarios.Our results shed light on the appropriateness of certain degrees of guidance in relation to different tasks,and the overall influence of guidance on the analysis outcome in terms of user’s mental state and analysis performance.

TBSSvis:Visual analytics for Temporal Blind Source Separation

Temporal Blind Source Separation(TBSS)is used to obtain the true underlying processes from noisy temporal multivariate data,such as electrocardiograms.TBSS has similarities to Principal Component Analysis(PCA)as it separates the input data into univariate components and is applicable to suitable datasets from various domains,such as medicine,finance,or civil engineering.Despite TBSS’s broad applicability,the involved tasks are not well supported in current tools,which offer only text-based interactions and single static images.Analysts are limited in analyzing and comparing obtained results,which consist of diverse data such as matrices and sets of time series.Additionally,parameter settings have a big impact on separation performance,but as a consequence of improper tooling,analysts currently do not consider the whole parameter space.We propose to solve these problems by applying visual analytics(VA)principles.Our primary contribution is a design study for TBSS,which so far has not been explored by the visualization community.We developed a task abstraction and visualization design in a user-centered design process.Task-specific assembling of well-established visualization techniques and algorithms to gain insights in the TBSS processes is our secondary contribution.We present TBSSvis,an interactive web-based VA prototype,which we evaluated extensively in two interviews with five TBSS experts.Feedback and observations from these interviews show that TBSSvis supports the actual workflow and combination of interactive visualizations that facilitate the tasks involved in analyzing TBSS results.