No abstract

In spite of advances in technologies for working with data, analysts still spend an inordinate amount of time diagnosing data quality issues and manipulating data into a usable form. This process of ‘data wrangling’ often constitutes the most tedious and time-consuming aspect of analysis. Though data cleaning and integration arelongstanding issues in the database community, relatively little research has explored how interactive visualization can advance the state of the art. In this article, we review the challenges and opportunities associated with addressing data quality issues. We argue that analysts might more effectively wrangle data through new interactive systems that integrate data verification, transformation, and visualization. We identify a number of outstanding research questions, including how appropriate visual encodings can facilitate apprehension of missing data, discrepant values, and uncertainty; how interactive visualizations might facilitate data transform specification; and how recorded provenance and social interaction might enable wider reuse, verification, and modification of data transformations

By extending and instantiating an existing formal task framework, we define a task taxonomy and task design space for temporal graph visualisation. We discuss the process involved in their generation, and describe how the design space can be 'sliced and diced' into multiple overlapping task categories, requiring distinct visual techniques for their support. The approach addresses deficiencies in the task literature, offering domain independence, greater task coverage, and unambiguous task specification. The taxonomy and design space capture tasks for temporal graphs, and also static graphs, multivariate graphs, and graph comparison, and will be of value in the design and evaluation of temporal graph visualisation systems.

This paper summarises the state-of-the-art in multiple tree visualisations. It discusses the spectrum of current representation techniques used on single trees, pairs of trees and finally multiple trees, in order to identify which representations are best suited to particular tasks and to find gaps in the representation space where opportunities for future multiple tree visualisation research may exist. The application areas from where multiple tree data are derived are enumerated, and the distinct structures that multiple trees make in combination with each other and the effect on subsequent approaches to their visualisation are discussed, along with the basic high-level goals of existing multiple tree visualisations.

Abstract.Biologists use scientific names to label the organisms described in their data; however, these names are not unique identifiers for taxonomic entities. Alternative taxonomic classifications may apply the same name, associated with alternative definition or circumscription. Consequently, labelling data with scientific names alone does not unambiguously distinguish between taxon concepts. Accurate integration and comparison of biological data is required on taxon concepts, as defined in alternative taxonomic classifications. We have derived an abstract, inclusive model for the diverse representations of taxonomic concepts used by taxonomists and in taxonomic databases. This model has been implemented as a proposed standard XML schema for the exchange and comparison of taxonomic concepts between data providers and users. The representation and exchange of taxon definitions conformant with this schema will facilitate the development of taxonomic name/concept resolution services, allowing the meaningful integration and comparison of biological datasets, with greater accuracy than on the basis of name alone.