9,593 research outputs found
From Data to Knowledge Graphs: A Multi-Layered Method to Model User's Visual Analytics Workflow for Analytical Purposes
The importance of knowledge generation drives much of Visual Analytics (VA).
User-tracking and behavior graphs have shown the value of understanding users'
knowledge generation while performing VA workflows. Works in theoretical
models, ontologies, and provenance analysis have greatly described means to
structure and understand the connection between knowledge generation and VA
workflows. Yet, two concepts are typically intermixed: the temporal aspect,
which indicates sequences of events, and the atemporal aspect, which indicates
the workflow state space. In works where these concepts are separated, they do
not discuss how to analyze the recorded user's knowledge gathering process when
compared to the VA workflow itself. This paper presents Visual Analytic
Knowledge Graph (VAKG), a conceptual framework that generalizes existing
knowledge models and ontologies by focusing on how humans relate to computer
processes temporally and how it relates to the workflow's state space. Our
proposal structures this relationship as a 4-way temporal knowledge graph with
specific emphasis on modeling the human and computer aspect of VA as separate
but interconnected graphs for, among others, analytical purposes. We compare
VAKG with relevant literature to show that VAKG's contribution allows VA
applications to use it as a provenance model and a state space graph, allowing
for analytics of domain-specific processes, usage patterns, and users'
knowledge gain performance. We also interviewed two domain experts to check, in
the wild, whether real practice and our contributions are aligned.Comment: 9 pgs, submitted to VIS 202
Beyond multimedia adaptation: Quality of experience-aware multi-sensorial media delivery
Multiple sensorial media (mulsemedia) combines multiple media elements which engage three or more of human senses, and as most other media content, requires support for delivery over the existing networks. This paper proposes an adaptive mulsemedia framework (ADAMS) for delivering scalable video and sensorial data to users. Unlike existing two-dimensional joint source-channel adaptation solutions for video streaming, the ADAMS framework includes three joint adaptation dimensions: video source, sensorial source, and network optimization. Using an MPEG-7 description scheme, ADAMS recommends the integration of multiple sensorial effects (i.e., haptic, olfaction, air motion, etc.) as metadata into multimedia streams. ADAMS design includes both coarse- and fine-grained adaptation modules on the server side: mulsemedia flow adaptation and packet priority scheduling. Feedback from subjective quality evaluation and network conditions is used to develop the two modules. Subjective evaluation investigated users' enjoyment levels when exposed to mulsemedia and multimedia sequences, respectively and to study users' preference levels of some sensorial effects in the context of mulsemedia sequences with video components at different quality levels. Results of the subjective study inform guidelines for an adaptive strategy that selects the optimal combination for video segments and sensorial data for a given bandwidth constraint and user requirement. User perceptual tests show how ADAMS outperforms existing multimedia delivery solutions in terms of both user perceived quality and user enjoyment during adaptive streaming of various mulsemedia content. In doing so, it highlights the case for tailored, adaptive mulsemedia delivery over traditional multimedia adaptive transport mechanisms
Focal Spot, Spring/Summer 1985
https://digitalcommons.wustl.edu/focal_spot_archives/1040/thumbnail.jp
VegaProf: Profiling Vega Visualizations
Vega is a popular domain-specific language (DSL) for visualization
specification. At runtime, Vega's DSL is first transformed into a dataflow
graph and then functions to render visualization primitives. While the Vega
abstraction of implementation details simplifies visualization creation, it
also makes Vega visualizations challenging to debug and profile without
adequate tools. Our formative interviews with three practitioners at Sigma
Computing showed that existing developer tools are not suited for visualization
profiling as they are disconnected from the semantics of the Vega DSL
specification and its resulting dataflow graph. We introduce VegaProf, the
first performance profiler for Vega visualizations. VegaProf effectively
instruments the Vega library by associating the declarative specification with
its compilation and execution. Using interactive visualizations, VegaProf
enables visualization engineers to interactively profile visualization
performance at three abstraction levels: function, dataflow graph, and
visualization specification. Our evaluation through two use cases and feedback
from five visualization engineers at Sigma Computing shows that VegaProf makes
visualization profiling tractable and actionable.Comment: Submitted to EuroVis'2
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