KB4VA: A Knowledge Base of Visualization Designs for Visual Analytics

Visual analytics (VA) systems have been widely used to facilitate decision-making and analytical reasoning in various application domains. VA involves visual designs, interaction designs, and data mining, which is a systematic and complex paradigm. In this work, we focus on the design of effective visualizations for complex data and analytical tasks, which is a critical step in designing a VA system. This step is challenging because it requires extensive knowledge about domain problems and visualization to design effective encodings. Existing visualization designs published in top venues are valuable resources to inspire designs for problems with similar data structures and tasks. However, those designs are hard to understand, parse, and retrieve due to the lack of specifications. To address this problem, we build KB4VA, a knowledge base of visualization designs in VA systems with comprehensive labels about their analytical tasks and visual encodings. Our labeling scheme is inspired by a workshop study with 12 VA researchers to learn user requirements in understanding and retrieving professional visualization designs in VA systems. The theme extends Vega-Lite specifications for describing advanced and composited visualization designs in a declarative manner, thus facilitating human understanding and automatic indexing. To demonstrate the usefulness of our knowledge base, we present a user study about design inspirations for VA tasks. In summary, our work opens new perspectives for enhancing the accessibility and reusability of professional visualization designs

Scientists in the MIST: Simplifying Interface Design for End Users

We are building a Malleable Interactive Software Toolkit (MIST), a tool set and infrastructure to simplify the design and construction of dynamically-reconfigurable (malleable) interactive software. Malleable software offers the end-user powerful tools to reshape their interactive environment on the fly. We aim to make the construction of such software straightforward, and to make reconfiguration of the resulting systems approachable and manageable to an educated, but non-specialist, user. To do so, we draw on a diverse body of existing research on alternative approaches to user interface (UI) and interactive software construction, including declarative UI languages, constraint-based programming and UI management, reflection and data-driven programming, and visual programming techniques

Emerging from the MIST: A Connector Tool for Supporting Programming by Non-programmers

Software development is an iterative process. As user re-quirements emerge software applications must be extended to support the new requirements. Typically, a programmer will add new code to an existing code base of an application to provide a new functionality. Previous research has shown that such extensions are easier when application logic is clearly separated from the user interface logic. Assuming that a programmer is already familiar with the existing code base, the task of writing the new code can be considered to be split into two sub-tasks: writing code for the application logic; that is, the actual functionality of the application; and writing code for the user interface that will expose the functionality to the end user. The goal of this research is to reduce the effort required to create a user interface once the application logic has been created, toward supporting scientists with minimal pro-gramming knowledge to be able to create and modify pro-grams. Using a Model View Controller based architecture, various model components which contain the application logic can be built and extended. The process of creating and extending the views (user interfaces) on these model components is simplified through the use of our Malleable Interactive Software Toolkit (MIST), a tool set an infrastructure intended to simplify the design and extension of dynamically reconfigurable interfaces. This paper focuses on one tool in the MIST suite, a connec-tor tool that enables the programmer to evolve the user interface as the application logic evolves by connecting related pieces of code together; either through simple drag-and-drop interactions or through the authoring of Python code. The connector tool exemplifies the types of tools in the MIST suite, which we expect will encourage collabora-tive development of applications by allowing users to inte-grate various components and minimizing the cost of de-veloping new user interfaces for the combined compo-nents

A dataflow platform for applications based on Linked Data

Modern software applications increasingly benefit from accessing the multifarious and heterogeneous Web of Data, thanks to the use of web APIs and Linked Data principles. In previous work, the authors proposed a platform to develop applications consuming Linked Data in a declarative and modular way. This paper describes in detail the functional language the platform gives access to, which is based on SPARQL (the standard query language for Linked Data) and on the dataflow paradigm. The language features interactive and meta-programming capabilities so that complex modules/applications can be developed. By adopting a declarative style, it favours the development of modules that can be reused in various specific execution context

Evaluating Hank, a cognitive modelling language for psychologists

Hank is a visual programming language devised specically for the use of cognitive psychologists rather than computer programmers. This paper introduces an eighteen month evaluation project on the use of Hank. This project began on November the 1st 1998, the initial findings and planned evaluation programme will be presented at the workshop in January

Tea: A High-level Language and Runtime System for Automating Statistical Analysis

Though statistical analyses are centered on research questions and hypotheses, current statistical analysis tools are not. Users must first translate their hypotheses into specific statistical tests and then perform API calls with functions and parameters. To do so accurately requires that users have statistical expertise. To lower this barrier to valid, replicable statistical analysis, we introduce Tea, a high-level declarative language and runtime system. In Tea, users express their study design, any parametric assumptions, and their hypotheses. Tea compiles these high-level specifications into a constraint satisfaction problem that determines the set of valid statistical tests, and then executes them to test the hypothesis. We evaluate Tea using a suite of statistical analyses drawn from popular tutorials. We show that Tea generally matches the choices of experts while automatically switching to non-parametric tests when parametric assumptions are not met. We simulate the effect of mistakes made by non-expert users and show that Tea automatically avoids both false negatives and false positives that could be produced by the application of incorrect statistical tests.Comment: 11 page

Integrating web services into data intensive web sites

Designing web sites is a complex task. Ad-hoc rapid prototyping easily leads to unsatisfactory results, e.g. poor maintainability and extensibility. However, existing web design frameworks focus exclusively on data presentation: the development of specific functionalities is still achieved through low-level programming. In this paper we address this issue by describing our work on the integration of (semantic) web services into a web design framework, OntoWeaver. The resulting architecture, OntoWeaver-S, supports rapid prototyping of service centred data-intensive web sites, which allow access to remote web services. In particular, OntoWeaver-S is integrated with a comprehensive web service platform, IRS-II, for the specification, discovery, and execution of web services. Moreover, it employs a set of comprehensive site ontologies to model and represent all aspects of service-centred data-intensive web sites, and thus is able to offer high level support for the design and development process