9,240 research outputs found
Evaluation of two interaction techniques for visualization of dynamic graphs
Several techniques for visualization of dynamic graphs are based on different
spatial arrangements of a temporal sequence of node-link diagrams. Many studies
in the literature have investigated the importance of maintaining the user's
mental map across this temporal sequence, but usually each layout is considered
as a static graph drawing and the effect of user interaction is disregarded. We
conducted a task-based controlled experiment to assess the effectiveness of two
basic interaction techniques: the adjustment of the layout stability and the
highlighting of adjacent nodes and edges. We found that generally both
interaction techniques increase accuracy, sometimes at the cost of longer
completion times, and that the highlighting outclasses the stability adjustment
for many tasks except the most complex ones.Comment: Appears in the Proceedings of the 24th International Symposium on
Graph Drawing and Network Visualization (GD 2016
Fast filtering and animation of large dynamic networks
Detecting and visualizing what are the most relevant changes in an evolving
network is an open challenge in several domains. We present a fast algorithm
that filters subsets of the strongest nodes and edges representing an evolving
weighted graph and visualize it by either creating a movie, or by streaming it
to an interactive network visualization tool. The algorithm is an approximation
of exponential sliding time-window that scales linearly with the number of
interactions. We compare the algorithm against rectangular and exponential
sliding time-window methods. Our network filtering algorithm: i) captures
persistent trends in the structure of dynamic weighted networks, ii) smoothens
transitions between the snapshots of dynamic network, and iii) uses limited
memory and processor time. The algorithm is publicly available as open-source
software.Comment: 6 figures, 2 table
NiMo syntax: part 1
Many formalisms for the specification for concurrent and distributed systems have emerged. In particular considering boxes and strings approaches. Examples are action calculi, rewriting logic and graph rewriting, bigraphs. The boxes and string metaphor is addressed with different levels of granularity. One of the approaches is to consider a process network as an hypergraph. Based in this general framework, we encode NiMo nets as a class of Annotated hypergraphs. This class is defined by giving the alphabet and the operations used to construct such programs. Therefore we treat only editing operations on labelled hypergraphs and afterwards how this editing operation affects the graph. Graph transformation (execution rules) is not covered here.Postprint (published version
Mapping Tasks to Interactions for Graph Exploration and Graph Editing on Interactive Surfaces
Graph exploration and editing are still mostly considered independently and
systems to work with are not designed for todays interactive surfaces like
smartphones, tablets or tabletops. When developing a system for those modern
devices that supports both graph exploration and graph editing, it is necessary
to 1) identify what basic tasks need to be supported, 2) what interactions can
be used, and 3) how to map these tasks and interactions. This technical report
provides a list of basic interaction tasks for graph exploration and editing as
a result of an extensive system review. Moreover, different interaction
modalities of interactive surfaces are reviewed according to their interaction
vocabulary and further degrees of freedom that can be used to make interactions
distinguishable are discussed. Beyond the scope of graph exploration and
editing, we provide an approach for finding and evaluating a mapping from tasks
to interactions, that is generally applicable. Thus, this work acts as a
guideline for developing a system for graph exploration and editing that is
specifically designed for interactive surfaces.Comment: 21 pages, minor corrections (typos etc.
Visualising the structure of document search results: A comparison of graph theoretic approaches
This is the post-print of the article - Copyright @ 2010 Sage PublicationsPrevious work has shown that distance-similarity visualisation or âspatialisationâ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or âcluster growingâ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally optimal mapping. In this paper the clustering performance of a recently proposed algorithm called isometric feature mapping (Isomap), which deals with non-linearity by transforming dissimilarities into geodesic distances, is compared to that of non-metric multidimensional scaling (MDS). Various graph pruning methods, for geodesic distance estimation, are also compared. Results show that Isomap is significantly better at preserving local structural detail than MDS, suggesting it is better suited to cluster growing and other semantic navigation tasks. Moreover, it is shown that applying a minimum-cost graph pruning criterion can provide a parameter-free alternative to the traditional K-neighbour method, resulting in spatial clustering that is equivalent to or better than that achieved using an optimal-K criterion
Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases
A critical challenge in constructing a natural language interface to database
(NLIDB) is bridging the semantic gap between a natural language query (NLQ) and
the underlying data. Two specific ways this challenge exhibits itself is
through keyword mapping and join path inference. Keyword mapping is the task of
mapping individual keywords in the original NLQ to database elements (such as
relations, attributes or values). It is challenging due to the ambiguity in
mapping the user's mental model and diction to the schema definition and
contents of the underlying database. Join path inference is the process of
selecting the relations and join conditions in the FROM clause of the final SQL
query, and is difficult because NLIDB users lack the knowledge of the database
schema or SQL and therefore cannot explicitly specify the intermediate tables
and joins needed to construct a final SQL query. In this paper, we propose
leveraging information from the SQL query log of a database to enhance the
performance of existing NLIDBs with respect to these challenges. We present a
system Templar that can be used to augment existing NLIDBs. Our extensive
experimental evaluation demonstrates the effectiveness of our approach, leading
up to 138% improvement in top-1 accuracy in existing NLIDBs by leveraging SQL
query log information.Comment: Accepted to IEEE International Conference on Data Engineering (ICDE)
201
Tac-tiles: multimodal pie charts for visually impaired users
Tac-tiles is an accessible interface that allows visually impaired users to browse graphical information using tactile and audio feedback. The system uses a graphics tablet which is augmented with a tangible overlay tile to guide user exploration. Dynamic feedback is provided by a tactile pin-array at the fingertips, and through speech/non-speech audio cues. In designing the system, we seek to preserve the affordances and metaphors of traditional, low-tech teaching media for the blind, and combine this with the benefits of a digital representation. Traditional tangible media allow rapid, non-sequential access to data, promote easy and unambiguous access to resources such as axes and gridlines, allow the use of external memory, and preserve visual conventions, thus promoting collaboration with sighted colleagues. A prototype system was evaluated with visually impaired users, and recommendations for multimodal design were derived
Can animation support the visualisation of dynamic graphs?
Animation and small multiples are methods for visualizing dynamically evolving graphs. Animations present an interactive movie of the data where positions of nodes are smoothly interpolated as the graph evolves. Nodes fade in/out as they are added/removed from the data set. Small multiples presents the data like a comic book with the graph at various states in separate windows. The user scans these windows to see how the data evolves. In a recent experiment, drawing stability (known more widely as the âmental mapâ) was shown to help users follow specific nodes or long paths in dynamically evolving data. However, no significant difference between animation and small multiples presentations was found. In this paper, we look at data where the nodes in the graph have low drawing stability and analyze it with new error metrics: measuring how close the given answer is from the correct answer on a continuous scale. We find evidence that when the stability of the drawing is low and important nodes in the task cannot be highlighted throughout the time series, animation can improve task performance when compared to the use of small multiples
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