arXiv subjects

Peter W. S. Butcher

Publications and source records attributed to Peter W. S. Butcher.

5 recordsLinked to original sources

arXiv2026

Somewhere Over the Desktop: A Research Agenda for Ubiquitous Analytics

Spatial computing, generative AI, and open web standards are converging. Three spatial operating systems -- Android XR, Meta Horizon OS, and Apple visionOS -- now ship with platform-level scene understanding. Wearable displays span the range from full headsets to slim smartglasses. Agentic AI operates on the same spatial substrates as the human user. This convergence enables new opportunities for \textit{ubiquitous analytics} (UA): the use of many, physically distributed, networked devices to support data sensemaking anytime and anywhere. But proprietary platforms are settling design conventions that will calcify without evidence-based alternatives. UA has now matured to the point where its intellectual history can be read as a structured genealogy of foundations, contributions, and lineages. We trace this genealogy and organize it into clusters spanning cognition, context, interaction, platforms, visualization, collaboration, and evaluation. Finally, we cross these clusters with each other, yielding a total of 42 future research challenges.

cs.HC↗

arXiv2026

Channels and Substrates: Distributed Cognition as an Interaction Model for Ubiquitous Analytics

Traditional HCI interaction models assume a single monolithic interface and a stable sensorimotor loop. These models fit poorly with cross-device (XVA) and ubiquitous analytics (UA), where interactive data sensemaking unfolds across multiple devices, artifacts, and people in disparate settings from the office to the factory floor. In this paper, we show how interaction in ubiquitous analytics can be modeled using distributed cognition as propagation of representational state across substrates -- minds, speech, bodies, artifacts, and devices -- rather than as traffic through a single interface. On this basis we introduce input and output channels as generalizations of the visual channels from data visualization: just as visual channels carry data through properties of the visual substrate, input and output channels carry representational state through substrates whose availability, suitability, and preferability depend on context. We demonstrate the channels and substrates framework by reanalyzing several ubiquitous, immersive, and situated analytics systems.

cs.HC↗

arXiv2024

Attention-Aware Visualization: Tracking and Responding to User Perception Over Time

We propose the notion of Attention-Aware Visualizations (AAVs) that track the user's perception of a visual representation over time and feed this information back to the visualization. Such context awareness is particularly useful for ubiquitous and immersive analytics where knowing which embedded visualizations the user is looking at can be used to make visualizations react appropriately to the user's attention: for example, by highlighting data the user has not yet seen. We can separate the approach into three components: (1) measuring the user's gaze on a visualization and its parts; (2) tracking the user's attention over time; and (3) reactively modifying the visual representation based on the current attention metric. In this paper, we present two separate implementations of AAV: a 2D data-agnostic method for web-based visualizations that can use an embodied eyetracker to capture the user's gaze, and a 3D data-aware one that uses the stencil buffer to track the visibility of each individual mark in a visualization. Both methods provide similar mechanisms for accumulating attention over time and changing the appearance of marks in response. We also present results from a qualitative evaluation studying visual feedback and triggering mechanisms for capturing and revisualizing attention.

cs.HC↗

arXiv2024

Path-based Design Model for Constructing and Exploring Alternative Visualisations

We present a path-based design model and system for designing and creating visualisations. Our model represents a systematic approach to constructing visual representations of data or concepts following a predefined sequence of steps. The initial step involves outlining the overall appearance of the visualisation by creating a skeleton structure, referred to as a flowpath. Subsequently, we specify objects, visual marks, properties, and appearance, storing them in a gene. Lastly, we map data onto the flowpath, ensuring suitable morphisms. Alternative designs are created by exchanging values in the gene. For example, designs that share similar traits, are created by making small incremental changes to the gene. Our design methodology fosters the generation of diverse creative concepts, space-filling visualisations, and traditional formats like bar charts, circular plots and pie charts. Through our implementation we showcase the model in action. As an example application, we integrate the output visualisations onto a smartwatch and visualisation dashboards. In this article we (1) introduce, define and explain the path model and discuss possibilities for its use, (2) present our implementation, results, and evaluation, and (3) demonstrate and evaluate an application of its use on a mobile watch.

cs.HC↗

arXiv2023

The Reality of the Situation: A Survey of Situated Analytics

The advent of low cost, accessible, and high performance augmented reality (AR) has shed light on a situated form of analytics where in-situ visualizations embedded in the real world can facilitate sensemaking based on the user's physical location. In this work, we identify prior literature in this emerging field with a focus on situated analytics. After collecting 47 relevant situated analytics systems, we classify them using a taxonomy of three dimensions: situating triggers, view situatedness, and data depiction. We then identify four archetypical patterns in our classification using an ensemble cluster analysis. We also assess the level which these systems support the sensemaking process. Finally, we discuss insights and design guidelines that we learned from our analysis.

cs.HC↗