Visual Analytics for Understanding Draco's Knowledge Base

Draco has been developed as an automated visualization recommendation system formalizing design knowledge as logical constraints in ASP (Answer-Set Programming). With an increasing set of constraints and incorporated design knowledge, even visualization experts lose overview in Draco and struggle to retrace the automated recommendation decisions made by the system. Our paper proposes an Visual Analytics (VA) approach to visualize and analyze Draco's constraints. Our VA approach is supposed to enable visualization experts to accomplish identified tasks regarding the knowledge base and support them in better understanding Draco. We extend the existing data extraction strategy of Draco with a data processing architecture capable of extracting features of interest from the knowledge base. A revised version of the ASP grammar provides the basis for this data processing strategy. The resulting incorporated and shared features of the constraints are then visualized using a hypergraph structure inside the radial-arranged constraints of the elaborated visualization. The hierarchical categories of the constraints are indicated by arcs surrounding the constraints. Our approach is supposed to enable visualization experts to interactively explore the design rules' violations based on highlighting respective constraints or recommendations. A qualitative and quantitative evaluation of the prototype confirms the prototype's effectiveness and value in acquiring insights into Draco's recommendation process and design constraints.Comment: To be presented at VIS 202

Event Data Definition in LHCb

We present the approach used for defining the event object model for the LHCb experiment. This approach is based on a high level modelling language, which is independent of the programming language used in the current implementation of the event data processing software. The different possibilities of object modelling languages are evaluated, and the advantages of a dedicated model based on XML over other possible candidates are shown. After a description of the language itself, we explain the benefits obtained by applying this approach in the description of the event model of an experiment such as LHCb. Examples of these benefits are uniform and coherent mapping of the object model to the implementation language across the experiment software development teams, easy maintenance of the event model, conformance to experiment coding rules, etc. The description of the object model is parsed by means of a so called front-end which allows to feed several back-ends. We give an introduction to the model itself and to the currently implemented back-ends which produce information like programming language specific implementations of event objects or meta information about these objects. Meta information can be used for introspection of objects at run-time which is essential for functionalities like object persistency or interactive analysis. This object introspection package for C++ has been adopted by the LCG project as the starting point for the LCG object dictionary that is going to be developed in common for the LHC experiments. The current status of the event object modelling and its usage in LHCb are presented and the prospects of further developments are discussed.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 7 pages, LaTeX, 2 eps figures. PSN MOJT00

A Heuristic Approach for Dual Expert/End-User Evaluation of Guidance in Visual Analytics

Guidance can support users during the exploration and analysis of complex data. Previous research focused on characterizing the theoretical aspects of guidance in visual analytics and implementing guidance in different scenarios. However, the evaluation of guidance-enhanced visual analytics solutions remains an open research question. We tackle this question by introducing and validating a practical evaluation methodology for guidance in visual analytics. We identify eight quality criteria to be fulfilled and collect expert feedback on their validity. To facilitate actual evaluation studies, we derive two sets of heuristics. The first set targets heuristic evaluations conducted by expert evaluators. The second set facilitates end-user studies where participants actually use a guidance-enhanced system. By following such a dual approach, the different quality criteria of guidance can be examined from two different perspectives, enhancing the overall value of evaluation studies. To test the practical utility of our methodology, we employ it in two studies to gain insight into the quality of two guidance-enhanced visual analytics solutions, one being a work-in-progress research prototype, and the other being a publicly available visualization recommender system. Based on these two evaluations, we derive good practices for conducting evaluations of guidance in visual analytics and identify pitfalls to be avoided during such studies.Comment: Accepted to IEEE VIS 202

Perspectives of Visualization Onboarding and Guidance in VA

A typical problem in Visual Analytics 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.Comment: Elsevier Visual Informatics (revised version under review

Sabrina: Modeling and Visualization of Economy Data with Incremental Domain Knowledge

Investment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to understand the big picture. In this paper, we present Sabrina, a financial data analysis and visualization approach that incorporates a pipeline for the generation of firm-to-firm financial transaction networks. The pipeline is capable of fusing the ground truth on individual firms in a region with (incremental) domain knowledge on general macroscopic aspects of the economy. Sabrina unites these heterogeneous data sources within a uniform visual interface that enables the visual analysis process. In a user study with three domain experts, we illustrate the usefulness of Sabrina, which eases their analysis process

Type 1 Autoimmune Pancreatitis in Europe:Clinical Profile and Response to Treatment

Background &amp; Aims: Autoimmune pancreatitis (AIP) is an immune-mediated disease of the pancreas with distinct pathophysiology and manifestations. Our aims were to characterize type 1 AIP in a large pan-European cohort and study the effectiveness of current treatment regimens. Methods: We retrospectively analyzed adults diagnosed since 2005 with type 1 or not-otherwise-specified AIP in 42 European university hospitals. Type 1 AIP was uniformly diagnosed using specific diagnostic criteria. Patients with type 2 AIP and those who had undergone pancreatic surgery were excluded. The primary end point was complete remission, defined as the absence of clinical symptoms and resolution of the index radiologic pancreatic abnormalities attributed to AIP. Results: We included 735 individuals with AIP (69% male; median age, 57 years; 85% White). Steroid treatment was started in 634 patients, of whom 9 (1%) were lost to follow-up. The remaining 625 had a 79% (496/625) complete, 18% (111/625) partial, and 97% (607/625) cumulative remission rate, whereas 3% (18/625) did not achieve remission. No treatment was given in 95 patients, who had a 61% complete (58/95), 19% partial (18/95), and 80% cumulative (76/95) spontaneous remission rate. Higher (≥0.4 mg/kg/day) corticosteroid doses were no more effective than lower (&lt;0.4 mg/kg/day) doses (odds ratio, 0.428; 95% confidence interval, 0.054–3.387) and neither was a starting dose duration &gt;2 weeks (odds ratio, 0.908; 95% confidence interval, 0.818–1.009). Elevated IgG4 levels were independently associated with a decreased chance of complete remission (odds ratio, 0.639; 95% confidence interval, 0.427–0.955). Relapse occurred in 30% of patients. Relapses within 6 months of remission induction were independent of the steroid-tapering duration, induction treatment duration, and total cumulative dose. Conclusions: Patients with type 1 AIP and elevated IgG4 level may need closer monitoring. For remission induction, a starting dose of 0.4 mg/kg/day for 2 weeks followed by a short taper period seems effective. This study provides no evidence to support more aggressive regimens.</p