Words of Estimative Correlation: Studying Verbalizations of Scatterplots

Natural language and visualization are being increasingly deployed together for supporting data analysis in different ways, from multimodal interaction to enriched data summaries and insights. Yet, researchers still lack systematic knowledge on how viewers verbalize their interpretations of visualizations, and how they interpret verbalizations of visualizations in such contexts. We describe two studies aimed at identifying characteristics of data and charts that are relevant in such tasks. The first study asks participants to verbalize what they see in scatterplots that depict various levels of correlations. The second study then asks participants to choose visualizations that match a given verbal description of correlation. We extract key concepts from responses, organize them in a taxonomy and analyze the categorized responses. We observe that participants use a wide range of vocabulary across all scatterplots, but particular concepts are preferred for higher levels of correlation. A comparison between the studies reveals the ambiguity of some of the concepts. We discuss how the results could inform the design of multimodal representations aligned with the data and analytical tasks, and present a research roadmap to deepen the understanding about visualizations and natural language

Towards Natural Language Empowered Interactive Data Analysis

The recent advances in natural language based interaction methodologies offer promising avenues to enhance the interactive processes within the human-machine dialogue of visual analytics. We envisage \textit{Multimodal Data Analytics} as a novel approach for conducting data analysis that builds on the strengths of visual analytics and natural language as an expressive interaction channel. We investigate the potential enhancements from such a multimodal approach and discusses the preliminary outline for a structured methodology to study the role of natural language in data analytics. Our approach builds on a simple model of human machine dialogue for interactive data analysis which we then propose to instantiate as visual analytics workflows -- representations to study and operationalise interactive data analysis routines empowered by natural language interaction

Towards Multimodal Data Analytics: Integrating Natural Language into Visual Analytics

The continuous interaction between users and the system in visual analytics can be considered a dialogue. We propose the use of multiple two-way channels facilitated by a multimodal interface as a central aspect of interactive visualization design, in particular, the use of natural language with interactive visualization. We discuss key related concepts, potential benefits, challenges and opportunities that emerge as a research agenda for multimodal data analysis

Characterizing Representation of Temporal Data Visualization

Designing visualizations for exploration of temporal data requires several choices based on aspects of time and visual representation. Previous taxonomies have described existing visualizations based on these aspects without relating the visual representations. We propose to characterize existing visualization techniques based on both semantic aspects of time and visual representations. Our design space helps to identify how these different visual representations relate and give the possibility to combine attributes of representation from different techniques. We compare two examples of visualizations from the literature based on our taxonomy

Summarising the structure of an organisation and reconstructing a chain of events - VAST 2014 Mini-Challenge 1 Submission Honourable Mention for Novelty in Visualization

The 2014 VAST Mini-challenge 1 asked participants to summarise the structure of two organisations with overlapping members, reconstruct the chain of events of a kidnapping and to provide two possible explanations. Our solution to the first part of the challenge - the "chalkboard" - received an Honourable Mention for Novelty in Visualization

Exploring Temporal Granularities with Visualization

Time can be expressed and aggregated into concepts called granularities. Granularities are defined in a structure with their rules of conversion that may take the form of trees or graphs, thus it’s possible to design tools that dynamically explore different granularities that might reveal patterns hidden in other levels. We described an intial investigation of the use of interactive visualization techniques for that purpose and define future work to be done

New global stability estimates for the Gel'fand-Calderon inverse problem

We prove new global stability estimates for the Gel'fand-Calderon inverse problem in 3D. For sufficiently regular potentials this result of the present work is a principal improvement of the result of [G. Alessandrini, Stable determination of conductivity by boundary measurements, Appl. Anal. 27 (1988), 153-172]

A framework for hierarchical time-oriented data visualisation

The paradigm of exploratory data analysis advocates the use of multiple perspectives to formulate hypotheses on the data. This thesis presents a framework to support it through the use of interactive hierarchical visualisations for the exploration of temporal data. The research that leads to the framework involves investigating what are the conventional interactive techniques for temporal data, how they can be combined with hierarchical methods and which are the conceptual transformations that enable navigating between multiple perspectives. The aim of the research is to facilitate the design of interactive visualisations based on the use of granularities or units of time, which hide or reveal processes at various scales and is a key aspect of temporal data. Characteristics of granularities are suitable for hierarchical visualisations as evidenced in the literature. However, current conceptual models and frameworks lack means to incorporate characteristics of granularities as an integral part of visualisation design. The research addresses this by combining features of hierarchical and time-oriented visualisations and enabling systematic re-configuration of visualisations. Current techniques for visualising temporal data are analysed and specified at previously unsupported levels by breaking down visual encodings into decomposed layers, which can be arranged and recombined through hierarchical composition methods. Afterwards, the transformations of the properties of temporal data are defined by drawing from the interactions found in the literature and formalising them as a set of conceptual operators. The complete framework is introduced by combining the different components that form it and enable specifying visual encodings, hierarchical compositions and the temporal transformations. A case study then demonstrates how the framework can be used and its benefits for evaluating analysis strategies in visual exploration

Changing a semantics: opportunism or courage?

The generalized models for higher-order logics introduced by Leon Henkin, and their multiple offspring over the years, have become a standard tool in many areas of logic. Even so, discussion has persisted about their technical status, and perhaps even their conceptual legitimacy. This paper gives a systematic view of generalized model techniques, discusses what they mean in mathematical and philosophical terms, and presents a few technical themes and results about their role in algebraic representation, calibrating provability, lowering complexity, understanding fixed-point logics, and achieving set-theoretic absoluteness. We also show how thinking about Henkin's approach to semantics of logical systems in this generality can yield new results, dispelling the impression of adhocness. This paper is dedicated to Leon Henkin, a deep logician who has changed the way we all work, while also being an always open, modest, and encouraging colleague and friend.Comment: 27 pages. To appear in: The life and work of Leon Henkin: Essays on his contributions (Studies in Universal Logic) eds: Manzano, M., Sain, I. and Alonso, E., 201