Democratic Replay: Enhancing TV Election Debates with Interactive Visualisations

This paper presents an online platform for enhancing televised election debates with interactive visualisations. Election debates are one of the highlights of election campaigns worldwide. They are also often criticised as appearing scripted, rehearsed, detached from much of the electorate, and at times too complex. Democratic Replay enhances videos of election debates with a collection of interactive tools aimed at providing a replay experience centred around citizens' needs. We present the system requirements, design and implementation, and report on an evaluation based on the ITV Leaders' Debate from the 2015 UK General Election campaign

Evaluation methodology for visual analytics software

O desafio do Visual Analytics (VA) é produzir visualizações que ajudem os utilizadores a concentrarem-se no aspecto mais relevante ou mais interessante dos dados apresentados. A sociedade actual enfrenta uma quantidade de dados que aumenta rapidamente. Assim, os utilizadores de informação em todos os domínios acabam por ter mais informação do que aquela com que podem lidar. O software VA deve suportar interacções intuitivas para que os analistas possam concentrar-se na informação que estão a manipular, e não na técnica de manipulação em si. Os ambientes de VA devem procurar minimizar a carga de trabalho cognitivo global dos seus utilizadores, porque se tivermos de pensar menos nas interacções em si, teremos mais tempo para pensar na análise propriamente dita. Tendo em conta os benefícios que as aplicações VA podem trazer e a confusão que ainda existe ao identificar tais aplicações no mercado, propomos neste trabalho uma nova metodologia de avaliação baseada em heurísticas. A nossa metodologia destina-se a avaliar aplicações através de testes de usabilidade considerando as funcionalidades e características desejáveis em sistemas de VA. No entanto, devido à sua natureza quatitativa, pode ser naturalmente utilizada para outros fins, tais como comparação para decisão entre aplicações de VA do mesmo contexto. Além disso, seus critérios poderão servir como fonte de informação para designers e programadores fazerem escolhas apropriadas durante a concepção e desenvolvimento de sistemas de VA

Using Machine Learning to Infer Reasoning Provenance from User Interaction Log Data

The reconstruction of analysts’ reasoning processes (reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems

Using machine learning to infer reasoning provenance from user interaction log data: based on the data/frame theory of sensemaking

The reconstruction of analysts’ reasoning processes (reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems

Collaborative Human-Computer Interaction with Big Wall Displays - BigWallHCI 2013 3rd JRC ECML Crisis Management Technology Workshop

The 3rd JRC ECML Crisis Management Technology Workshop on Human-Computer Interaction with Big Wall Displays in Situation Rooms and Monitoring Centres was co-organised by the European Commission Joint Research Centre and the University of Applied Sciences St. Pölten, Austria. It took place in the European Crisis Management Laboratory (ECML) of the JRC in Ispra, Italy, from 18 to 19 April 2013. 40 participants from stakeholders in the EC, civil protection bodies, academia, and industry attended the workshop. The hardware of large display areas is on the one hand mature since many years and on the other hand changing rapidly and improving constantly. This high pace developments promise amazing new setups with respect to e.g., pixel density or touch interaction. On the software side there are two components with room for improvement: 1. the software provided by the display manufacturers to operate their video walls (source selection, windowing system, layout control) and 2. dedicated ICT systems developed to the very needs of crisis management practitioners and monitoring centre operators. While industry starts to focus more on the collaborative aspects of their operating software already, the customized and tailored ICT applications needed are still missing, unsatisfactory, or very expensive since they have to be developed from scratch many times. Main challenges identified to enhance big wall display systems in crisis management and situation monitoring contexts include: 1. Interaction: Overcome static layouts and/or passive information consumption. 2. Participatory Design & Development: Software needs to meet users’ needs. 3. Development and/or application of Information Visualisation & Visual Analytics principle to support the transition from data to information to knowledge. 4. Information Overload: Proper methods for attention management, automatic interpretation, incident detection, and alarm triggering are needed to deal with the ever growing amount of data to be analysed.JRC.G.2-Global security and crisis managemen

Towards a Theory of Analytical Behaviour: A Model of Decision-Making in Visual Analytics

This paper introduces a descriptive model of the human-computer processes that lead to decision-making in visual analytics. A survey of nine models from the visual analytics and HCI literature are presented to account for different perspectives such as sense-making, reasoning, and low-level human-computer interactions. The survey examines the people and computers (entities) presented in the models, the divisions of labour between entities (both physical and role-based), the behaviour of both people and machines as constrained by their roles and agency, and finally the elements and processes which define the flow of data both within and between entities. The survey informs the identification of four observations that characterise analytical behaviour - defined as decision-making facilitated by visual analytics: bilateral discourse, divisions of labour, mixed-synchronicity information flows, and bounded behaviour. Based on these principles, a descriptive model is presented as a contribution towards a theory of analytical behaviour. The future intention is to apply prospect theory, a economic model of decision-making under uncertainty, to the study of analytical behaviour. It is our assertion that to apply prospect theory first requires a descriptive model of the processes that facilitate decision-making in visual analytics. We conclude it necessary to measure the perception of risk in future work in order to apply prospect theory to the study of analytical behaviour using our proposed model

Collaborative behavior, performance and engagement with visual analytics tasks using mobile devices

Interactive visualizations are external tools that can support users’ exploratory activities. Collaboration can bring benefits to the exploration of visual representations or visu‐ alizations. This research investigates the use of co‐located collaborative visualizations in mobile devices, how users working with two different modes of interaction and view (Shared or Non‐Shared) and how being placed at various position arrangements (Corner‐to‐Corner, Face‐to‐Face, and Side‐by‐Side) affect their knowledge acquisition, engagement level, and learning efficiency. A user study is conducted with 60 partici‐ pants divided into 6 groups (2 modes×3 positions) using a tool that we developed to support the exploration of 3D visual structures in a collaborative manner. Our results show that the shared control and view version in the Side‐by‐Side position is the most favorable and can improve task efficiency. In this paper, we present the results and a set of recommendations that are derived from them