Multivariate data contain an abundance of information and many techniques have been proposed to allow humans to navigate this information in an ordered fashion. For this work, we focus on methods that seek to convey multivariate data as a collection of bivariate scatterplots or parallel coordinates plots. Presenting multivariate data in this way requires a regime that determines in what order the bivariate scatterplots are presented or in what order the parallel coordinate axes are arranged. We refer to this order as a visualization sequence. Common techniques utilize standard statistical metrics like correlation, similarity or consistency. We expand on the family of statistical metrics by incorporating the rigidity of causal relationships. To capture these relationships, we first derive a causal graph from the data and then allow users to select from several semantic traversal schemes to derive the respective chart sequence. We tested the sequences with a crowd-sourced user study and a user interview to confirm that the causality-informed visualization sequences help viewers to better grasp the causal relationships that exist in the data, as opposed to sequences derived from correlations or randomization alone.

We propose the concept of teaching (and learning) unfamiliar visualizations by analogy, that is, demonstrating an unfamiliar visualization method by linking it to another more familiar one, where the in-betweens are designed to bridge the gap of these two visualizations and explain the difference in a gradual manner. As opposed to a textual description, our morphing explains an unfamiliar visualization through purely visual means. We demonstrate our idea by ways of four visualization pair examples: data table and parallel coordinates, scatterplot matrix and hyperbox, linear chart and spiral chart, and hierarchical pie chart and treemap. The analogy is commutative i.e. any member of the pair can be the unfamiliar visualization. A series of studies showed that this new paradigm can be an effective teaching tool. The participants could understand the unfamiliar visualization methods in all of the four pairs either fully or at least significantly better after they observed or interacted with the transitions from the familiar counterpart. The four examples suggest how helpful visualization pairings be identified and they will hopefully inspire other visualization morphings and associated transition strategies to be identified.

High-dimensional data visualization has been attracting much attention. To fully test related software and algorithms, researchers require a diverse pool of data with known and desired features. Test data do not always provide this, or only partially. Here we propose the paradigm WYDIWYGS (What You Draw Is What You Get). Its embodiment, SketchPadND, is a tool that allows users to generate high-dimensional data in the same interface they also use for visualization. This provides for an immersive and direct data generation activity, and furthermore it also enables users to interactively edit and clean existing high-dimensional data from possible artifacts. SketchPadND offers two visualization paradigms, one based on parallel coordinates and the other based on a relatively new framework using an N-D polygon to navigate in high-dimensional space. The first interface allows users to draw arbitrary profiles of probability density functions along each dimension axis and sketch shapes for data density and connections between adjacent dimensions. The second interface embraces the idea of sculpting. Users can carve data at arbitrary orientations and refine them wherever necessary. This guarantees that the data generated is truly high-dimensional. We demonstrate our tool's usefulness in real data visualization scenarios.

This paper presents a requirement-oriented automated framework for formal verification of service workflows. It is based on our previous work describing the requirement-oriented service workflow specification language called SWSpec. This language has been developed to facilitate workflow composer as well as arbitrary services willing to participate in a workflow to formally and uniformly impose their own requirements. As such, SWSpec provides a formal way to regulate and control workflows. The key component of the to-be-proposed framework centers on verification algorithms that rely on propositional logic. We demonstrate that logic-based workflow verification can be applied to SWSpec which is capable of checking compliance and also detecting conflicts of the imposed requirements. By automating compliance checking process, this framework will support scalable services interoperation in the form of workflows in opened environments.