In our previous work we defined the Linked Data Visualization Model (LDVM) , an abstract visualization process customized for the specifics of Linked Data. LDVM allows users to create data visualization pipelines that consist of four stages: Source Data, Analytical Abstraction, Visualization Abstraction and View.
Source Data allows a user to define a custom transformation to prepare an arbitrary dataset for further stages, which require their input to be RDF. In this paper we only consider RDF data sources such as RDF files or SPARQL endpoints, e.g. DBPedia.
The Analytical Abstraction enables the user to specify analytical operators that extract data to be processed from a data source and then transform it to create the desired analysis. The transformation can also compute additional characteristics or even generate a new multi-dimensional dataset. For example, we can create a statistical dataset from DBPedia by querying for resources of type dbpedia-owl:City and using data from their properties such as dbpedia-owl:populationAsOf for a dimension and dbpedia-owl:populationTotal for a measure. Further analytical steps could be performed within this stage, e.g. filtering cities from a specific country.
In the Visualization Abstraction stage of LDVM we need to prepare the analytical data to be compatible with our Data Cube visualizer. In the case of the analytical data already being described by DCV, this stage can be skipped. Otherwise, we would have to use a LDVM transformer to convert non-DCV statistical data to DCV as it is the format required by our visualizer. This stage is what allows users to reuse statistical analyses with results in various formats without rewriting them simply by appending an appropriate transformer.
In View Stage, DCV-compliant data is passed to a visualizer which creates a user-friendly data cube visualization. Based on dimension links to SDMX and SKOS concepts, a visualizer can generate more sophisticated facets in order to let the user to slice and dice the data cube. A proper visualizer should contain the well-known data cube visualization techniques and in Payola, our LDVM implementation, we have such a visualizer.