Using treemaps for variable selection in spatio-temporal visualisation

We demonstrate and reflect upon the use of enhanced treemaps that incorporate spatial and temporal ordering for exploring a large multivariate spatio-temporal data set. The resulting data-dense views summarise and simultaneously present hundreds of space-, time-, and variable-constrained subsets of a large multivariate data set in a structure that facilitates their meaningful comparison and supports visual analysis. Interactive techniques allow localised patterns to be explored and subsets of interest selected and compared with the spatial aggregate. Spatial variation is considered through interactive raster maps and high-resolution local road maps. The techniques are developed in the context of 42.2 million records of vehicular activity in a 98 km(2) area of central London and informally evaluated through a design used in the exploratory visualisation of this data set. The main advantages of our technique are the means to simultaneously display hundreds of summaries of the data and to interactively browse hundreds of variable combinations with ordering and symbolism that are consistent and appropriate for space- and time- based variables. These capabilities are difficult to achieve in the case of spatio-temporal data with categorical attributes using existing geovisualisation methods. We acknowledge limitations in the treemap representation but enhance the cognitive plausibility of this popular layout through our two-dimensional ordering algorithm and interactions. Patterns that are expected (e.g. more traffic in central London), interesting (e.g. the spatial and temporal distribution of particular vehicle types) and anomalous (e.g. low speeds on particular road sections) are detected at various scales and locations using the approach. In many cases, anomalies identify biases that may have implications for future use of the data set for analyses and applications. Ordered treemaps appear to have potential as interactive interfaces for variable selection in spatio-temporal visualisation. Information Visualization (2008) 7, 210-224. doi: 10.1057/palgrave.ivs.950018

Using pivots to explore heterogeneous collections: A case study in musicology

In order to provide a better e-research environment for musicologists, the musicSpace project has partnered with musicology’s leading data publishers, aggregated and enriched their data, and developed a richly featured exploratory search interface to access the combined dataset. There have been several significant challenges to developing this service, and intensive collaboration between musicologists (the domain experts) and computer scientists (who developed the enabling technologies) was required. One challenge was the actual aggregation of the data itself, as this was supplied adhering to a wide variety of different schemas and vocabularies. Although the domain experts expended much time and effort in analysing commonalities in the data, as data sources of increasing complexity were added earlier decisions regarding the design of the aggregated schema, particularly decisions made with reference to simpler data sources, were often revisited to take account of unanticipated metadata types. Additionally, in many domains a single source may be considered to be definitive for certain types of information. In musicology, this is essentially the case with the “works lists” of composers’ musical compositions given in Grove Music Online (http://www.oxfordmusiconline.com/public/book/omo_gmo), and so for musicSpace, we have mapped all sources to the works lists from Grove for the purposes of exploration, specifically to exploit the accuracy of its metadata in respect to dates of publication, catalogue numbers, and so on. Therefore, rather than mapping all fields from Grove to a central model, it would be far quicker (in terms of development time) to create a system to “pull-in” data from other sources that are mapped directly to the Grove works lists

On-line analytical processing

On-line analytical processing (OLAP) describes an approach to decision support, which aims to extract knowledge from a data warehouse, or more specifically, from data marts. Its main idea is providing navigation through data to non-expert users, so that they are able to interactively generate ad hoc queries without the intervention of IT professionals. This name was introduced in contrast to on-line transactional processing (OLTP), so that it reflected the different requirements and characteristics between these classes of uses. The concept falls in the area of business intelligence.Peer ReviewedPostprint (author's final draft