5 research outputs found
Multi-Perspective, Simultaneous Embedding
We describe MPSE: a Multi-Perspective Simultaneous Embedding method for
visualizing high-dimensional data, based on multiple pairwise distances between
the data points. Specifically, MPSE computes positions for the points in 3D and
provides different views into the data by means of 2D projections (planes) that
preserve each of the given distance matrices. We consider two versions of the
problem: fixed projections and variable projections. MPSE with fixed
projections takes as input a set of pairwise distance matrices defined on the
data points, along with the same number of projections and embeds the points in
3D so that the pairwise distances are preserved in the given projections. MPSE
with variable projections takes as input a set of pairwise distance matrices
and embeds the points in 3D while also computing the appropriate projections
that preserve the pairwise distances. The proposed approach can be useful in
multiple scenarios: from creating simultaneous embedding of multiple graphs on
the same set of vertices, to reconstructing a 3D object from multiple 2D
snapshots, to analyzing data from multiple points of view. We provide a
functional prototype of MPSE that is based on an adaptive and stochastic
generalization of multi-dimensional scaling to multiple distances and multiple
variable projections. We provide an extensive quantitative evaluation with
datasets of different sizes and using different number of projections, as well
as several examples that illustrate the quality of the resulting solutions
The State of the Art in Multilayer Network Visualization
Modelling relationships between entities in real-world systems with a simple
graph is a standard approach. However, reality is better embraced as several
interdependent subsystems (or layers). Recently the concept of a multilayer
network model has emerged from the field of complex systems. This model can be
applied to a wide range of real-world datasets. Examples of multilayer networks
can be found in the domains of life sciences, sociology, digital humanities and
more. Within the domain of graph visualization there are many systems which
visualize datasets having many characteristics of multilayer graphs. This
report provides a state of the art and a structured analysis of contemporary
multilayer network visualization, not only for researchers in visualization,
but also for those who aim to visualize multilayer networks in the domain of
complex systems, as well as those developing systems across application
domains. We have explored the visualization literature to survey visualization
techniques suitable for multilayer graph visualization, as well as tools,
tasks, and analytic techniques from within application domains. This report
also identifies the outstanding challenges for multilayer graph visualization
and suggests future research directions for addressing them
Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
Rapid growth in the generation of data from various sources has made data visualisation a valuable tool for analysing data. However, visual analysis can be a challenging task, not only due to intricate dashboards but also when dealing with complex and multidimensional data. In this context, advances in Natural Language Processing technologies have led to the development of Visualisation-oriented Natural Language Interfaces (V-NLIs). In this paper, we carry out a scoping review that analyses synergies between the fields of Data Visualisation and Natural Language Interaction. Specifically, we focus on chatbot-based V-NLI approaches and explore and discuss three research questions. The first two research questions focus on studying how chatbot-based V-NLIs contribute to interactions with the Data and Visual Spaces of the visualisation pipeline, while the third seeks to know how chatbot-based V-NLIs enhance users' interaction with visualisations. Our findings show that the works in the literature put a strong focus on exploring tabular data with basic visualisations, with visual mapping primarily reliant on fixed layouts. Moreover, V-NLIs provide users with restricted guidance strategies, and few of them support high-level and follow-up queries. We identify challenges and possible research opportunities for the V-NLI community such as supporting high-level queries with complex data, integrating V-NLIs with more advanced systems such as Augmented Reality (AR) or Virtual Reality (VR), particularly for advanced visualisations, expanding guidance strategies beyond current limitations, adopting intelligent visual mapping techniques, and incorporating more sophisticated interaction methods