=Paper=
{{Paper
|id=Vol-1910/paper0109
|storemode=property
|title=Effective User Interactions for Visual Analytics Tools
|pdfUrl=https://ceur-ws.org/Vol-1910/paper0109.pdf
|volume=Vol-1910
|authors=Vladimir Guchev
|dblpUrl=https://dblp.org/rec/conf/chitaly/Guchev17
}}
==Effective User Interactions for Visual Analytics Tools==
https://ceur-ws.org/Vol-1910/paper0109.pdf
Effective User Interactions for
Visual Analytics Tools
Vladimir Guchev
guchev@di.unito.it
Computer Science Department, University of Turin
Tutor: prof. Cristina Gena
Abstract. In the last few decades, there has formed a layer of traditions
in the information processing from different fields of science and tech-
nology, which includes a variety of standards, techniques and approaches
for visual analysis of diverse types and structures of data. Every year,
the advanced technologies of human-computer interaction and computer
graphics are becoming closer to information-based areas of activity, pro-
viding flexible and adaptive solutions in the implementation of user in-
terfaces for information management and decision-making systems that
involving the human-analyst. Taking into account the fact that the re-
sulting quality of activity in various areas of high-tech depends on the
ability of studying and mastering of complexly structured storages of
multidimensional data, the issue of developing effective user interactions
for visual analytics tools is becoming increasingly important. This paper
pays attention to the problems and possible solutions of effective vi-
sual representation and management of static and dynamic graph-based
datasets. Particular attention given to ergonomic approaches of data vi-
sualization and concepts of its gesture-based handling and control.
Keywords: human-centered computing, user interface design, data vi-
sualization, node-link diagram, graph exploration, pen-centric and sketch-
based interaction
1 Introduction
Among the wide variety of books and publications, focused on the intersection
of CHI and Data Visualization, there is a bias in applied areas, while more
fundamental things as the design and ergonomics of the both of interaction
patterns and visual symbolic systems are leaving without due attention. Even
the developers of specialised software for data analytics, who should be well
acquainted with the capabilities of advanced input devices (e.g. touch screens or
surface stylus pens), still remain using interactions that are more typical for the
WIMP (windows-icons-mouse-pointer) concept with its limitations. So far, the
most of visual analytics tools are inside the button paradigm: that is the user
actions are typically launched by the pressing a real or virtual but still button,
sometimes even with a stylus or a touch surface.
For a human, well familiar with the culture of writing, the poking of points as
the base way of user interactions does not seem to be natural. Having advanced
input devices and computing systems, which are enough to enable the adaptive
and reactive gesture recognition, now is the good time for the reasonable ampli-
fication of low-level interactions in order to reduce the number of micro-gestures
(e.g. click sequences) at least for visual data analytics software, based on pen-
centric input systems. As a good practical example of effective application of
sketch-based interactions, it is worth considering the user interfaces for graph
exploration and editing. Due to the large number of demanded functions for ap-
plication to the graph elements, layouts and views, the implementation of user
strokes tracing and recognition in the context of an active interaction mode may
be an effective solution. The effectiveness of the mentioned above application ex-
ample may also depend on the readability of the graph. The topic of visual graph
or hyper-graph representation is quite complex and requires special study. How-
ever, among the approaches to partially ordered graph visualization, it is worth
noting the circular layout that has strengths in the both of the convenient form
for visual human perception and the flexibility in arrangement methods.
This paper is organized as follows. The “Related Work” section gives a brief
review of graph-based data visualization and manipulation techniques. The “To-
wards Reactive Interacting” section overviews the approaches to the building of
gestures design space and the realisation of reactive interactions. The “Towards
Flexible Visualizing” section is reviewing the visualization techniques by means
of node-link-group diagrams.
2 Related Work
One of the most demanded concepts for visual analytics tools that has a rich
theoretical foundation, which allowing to study multidimensional datasets with
complex structures, is the interactive graph exploration. The significance and
ubiquity of interactive graph exploration are beyond any doubt: it gives wide
opportunities for extensive analysing of relationships and dependencies along
with patterns and exceptions in complex data such as biological, transport or
financial datasets, etc. Among the variety of combinations and hybrids of visual-
ization methods, node-link diagrams on the force-layout basis remain demanded
for flexible representation of network data like collaborative, social or commu-
nication networks. However, the readability and manageability of this type of
diagrams are an open problem: typical solutions (such as geometric compres-
sion, semantic abstraction, topologic simplification, etc.) provide to the analyst
albeit complete, but fixed and arduous result [1], [2]. The sensemaking of graph-
formatted data involves a wide range of tools for interactive and adaptive explo-
ration. Efficiency and clarity of such approaches for visual analysis depend on
the compliance between particular tasks and chosen modalities of interactivity
[3], [4], [5]. Fully automated algorithms for graph layout cannot provide a com-
plete solution for effective visualization. Several techniques have been proposed
for interacting with graphs, in particular, through customized layouts by adjust-
ing of nodes with interactive lenses and sticks, via magnet-based attraction and
radial menus [3], [6], [7]. By ordering the chaos of the initial layout, the analyst
gains insights into the instant graph changes [8].
Efforts on post-layout enhancement of graph visualization are usually associ-
ated with clusterization and multi-view representation [9], [10]. Few menu-based
techniques, providing some ways of interaction that typical for graphic editors
[6], may be considered as complicated to use. Studying of graph data through
free manipulation of nodes, specifically by partial geometrization of arrangement
while preserving the context of nodes in a graph topology, requires the ability
of quick direct control of elements: that is achievable through the sketch-based
techniques [11], [12].
Focusing on the visual part, it is worth noticing a large number of visualiza-
tion techniques in the literature that represent graphs using node-link diagrams
[13], [26], [29]. Among the rich variety of graph drawing techniques, it would be
worth to make a choice of the circular layout as one of the easiest for under-
standing and implementation. There are many examples of using the circular
and radial layouts for analysis of various datasets. Depending on the scope of
application, the circular and radial visualizations may be focused on different
formats and structures of exploring data, thus have great variety in approaches
to the graphical design. Burch et al. present the techniques and metaphors based
on a radial node-link approach for encoding of time-series relational data [14], as
well as for scalable dynamic graphical visualization [15]. Besides the discussion
of benefits and drawbacks of the radial visualization, the work also reveal the
static and dynamic aspects in node-link representations. The research of Velhow
et al. [16] proposes the approach to the exploration of time-varying relational
data, presented by large dynamic graphs, where the edges are defined by the
polar coordinates instead of Cartesian coordinates. The approach incorporates
several interaction techniques to explore dynamic patterns, such as trends and
counter-trends. The research of Holten [17] focuses on datasets, containing hier-
archical components, presents solutions for visualizing compound graphs based
on bundling of adjacency edges. The results of user studies, where hierarchi-
cal edge bundles applied for a few available layouts (such as balloon, rooted
tree, squarified treemap), show that the radial layout is the most preferable and
aesthetically pleasing.
3 Towards Reactive Interacting
Techniques and tools for visual analytics are often represented by a complex and
diverse set of using actions. Typically, each action performed by an elongate com-
bination of separate short-term manipulations with branching menus, multiple
sliders and numerous buttons. Although, in some cases, it is more convenient to
expand the set of manipulations through use of natural for humans stroking and
touching sequences. Within context-aware applications, the access to available
actions by sketching and drawing gestures could be obvious and convenient.
Interactive data visualization that supplemented with tracing and recognition
Fig. 1. Types of touch-based actions (A). Architecture of user interface design (B).
of pen-centric sketching, drawing and tapping, which are technically available
from any pointing device, opens new opportunities for layout control during ex-
ploratory data analysis. The current section presents an approach to gestures
processing, comprising a controller that classifies advanced pointer events and a
manager that allows handling events on data visualization elements.
3.1 Advanced Pen-centric Control of Data Visualization
In order to expand the range of a user commands set, recognizable by the sys-
tem, an extended approach to interpreting the input strokes is proposed. To
broaden the classification of pen-centric gestures perceived by a system, the
set of measurements as in Fig. 1A is proposed for usage. Creating and dynamic
updating of the gesture space is available with the potential of reactive program-
ming [18]. Various view-driven actions and data-driven elements can be bind to
the preferred tools and functions of interactive visualization: Fig. 1B shows the
architecture of user interface along with the related components. In general, the
gestures at the visual analytics tool are processed by two possible ways, as shown
in Fig. 2A. Depending on the complexity assessment of an input stroke, its’ at-
tributes are used for geometric calculations within the frame of related objects
positions or as the input vector for recognition by an artificial neural network.
For the most efficient distribution of available functionality between handlers,
some gestures may be reused for different actions in keeping with conditions of
global environment (as it is depicted for zooming levels in Fig. 3).
Fig. 2. Concept of pointer events handler (A). Examples of pen-centric actions (B).
Fig. 3. Reactive adjustment of the system response to a gesture, along with zooming.
3.2 Sketch-based Untangling of Force-directed Graphs
The exploration of moderately dense networks is used in various challenges of
visual data analysis. Frequently, the solutions lay in graph drawing, based on au-
tomatic force-directed layout, which results in a spontaneous and irreproducible
node-link diagram. Currently available approaches to improve its readability are
generally oriented to finite rendering without providing to the analyst handy
tools for post-layout manipulations. Enabling indirect manual control on visual-
izations through multi-step menus may appear difficult to learn and use. Thus,
the problem requires a more intuitive way of solving. This subsection presents
an original toolset for user-guided refinement of the force-directed graph lay-
out, with a bias on sketching techniques. Bearing in mind the ease-of-use and
acceptable accuracy of pen-centric manipulations, the sketching technique allow
transition of a graph geometry from irregular to regular. The toolset contains
simple and intuitive gesture-friendly user interface for view-driven selection, nav-
igation, manipulation, filtering and arrangement of nodes on a graph.
Depending on tasks and goals, performed during data exploration, pen-
centric interactions require different levels of input precision. If general draft
actions like a preselection (that can be corrected further) or a gesture-command
(having a good potential to be recognized) are feasible even with a rough stroke,
the manipulations with elements of visualization require greater accuracy. The
described user interface solves relatively complex view-driven tasks through easy-
performing pointing device gestures of two main types. The first type is the im-
Fig. 4. Triggered by a gesture post-layout manipulations of a selected group of nodes.
Fig. 5. Scheme of indirect drawing-based selection of a nodes group on a graph. This
scenario involves three steps with increasing requirements to the gestures precision.
precise drawing for shape recognition (which applies a corresponding action, in
particular the nodes alignment as in Fig. 4) or contextual tracing (for a rough
outline of constraints, as on the group selection tool in Fig. 5A). The second
type is the precise drag-and-drop manipulation that is a direct moving of graph
nodes or an accurate adjustment of the ruler sliders (as in Fig. 5C). The set
of pen-based single-stroke gestures for recognition is due to ease of drawing, re-
gardless whether it painted by a hardware stylus, or by a finger on touch-screen
device, or by a mouse. Sketching above the graph may be optionally visible: if
desired, the user interface allows to enable the indication of gesture path with
fading trace of a pointer, as well as to show a recognized pattern upon comple-
tion. In addition to the untangling of force-directed layout while stroking above
it, the toolset provides usual actions as searching, brushing, picking and direct
modification of individual or related nodes and groups.
4 Towards Flexible Visualizing
This section proposes guidelines to provide indications for an effective visual
relational analysis and chronology browsing of graph-based (as well as hyper-
graph-based) datasets. Such guidelines have been defined on the basis of relevant
papers and on the study about divergent possible visual representations of hyper-
graphs. As depicted in Fig. 6A, the semantics of data structure considers:
– Nodes, for instance, members of a collaboration network;
– Links, that are connections between vertices;
– Affiliations, grouping relationships of vertices;
– Relations, the dependencies among existing Groups.
Groups interrelated among themselves, representing a range, a tree, or a
sparse network, contain relatively densely linked nodes. Each node may be affil-
iated with a group or a set of groups. The hyper-edge specifies a set of nodes or
an ordered list of nodes. In the following, techniques for representation of nodes,
links and groups, along with their composition (see Fig. 6B), are reviewed in
order to explore and find proper visualization methods, related to circular, ra-
dial or spiral layout for the node-link diagram with groups. This study excludes
graphic effects, animation and obvious ways of coding, as colouring and condi-
tional marking [13], [19].
Fig. 6. Data structure considered in the study (A). The scheme of visible elements and
the combination of properties, overviewed in the guidelines (B).
4.1 Arrangement of Graph Layout
The displacement of an object on the screen is one of the problems to solve
when dealing with graph visualizations. Typically node-link diagrams are shown
on a Cartesian plane. There is a number of similar terms describing the items
distribution in a circular fashion: circular [20], radial [21] and spiral [22], each
referring to a polar coordinates system, where the starting point is the centre of
coordinates. The geometric structure of the polar grid may be characterized as
comprising two measurements:
– Circular, the position of element along the circular line. Visually, a circular
grid is built of concentric circles or a spiral as in Fig. 7B, which can be nested
or twisted with increasing radius in arithmetic or geometric progression. If
desired, the initially curved lines may be segmented in order to form a set
of straightened axes. Depending on the objectives, the curvature of guides
may be precluded (see Fig. 7B right) as it is done in the Radar Chart.
– Radial, the distance on a polar axis between element and the centre, i.e. the
pole. The main components of the radial grid are guiding lines, diverging
from the pole (see Fig 7A). The grid lines can be straight or bowed, with
tilt to the rotation axis, starting from centre or indented to periphery. The
guides that may contain loops [23] deliberately not considered here.
When the grid consists of concentric circles and centripetal lines and the
visualization is crowded, following curved lines can be difficult for a user. In
order to ease the reading of adjacent lines or sectors, there are several possible
implementations of segmentation of the diagram areas: the sectors, the circles,
the zebra stripes segments (Fig. 7D). Visualizations may support extending of
a selected sector angle and height stretching of a selected circle (Fig. 7C), or
adding of related details (Fig. 7E).
Fig. 7. Grid types in polar coordinates: radial (A), circular and spiral (B). Zoom im-
plementation (C). Basic approaches to grid segmentation (D). Most common solutions
for embedding of auxiliary elements (E).
4.2 Basic Composition Elements
The main components of the diagram are nodes, links and groups, which are
described below. According to Krzywinski [24], [25], the most common elements
for multilayer circular layout of nodes are the following (see Fig. 8A):
– Glyphs: symmetric symbols, miniatures or motifs, having mnemonic extent,
used to denote vertices on a graph. The glyph refers to a unique data unit
that may be paired with text.
– Patterns: a rectangular shape, transformed in the polar system that may
contain statistical graphics like scatter plots and charts.
– Silhouettes: a form of continuous line or area; they convey contextual time-
series or quantitative fluctuations. There is a wide variety of positioning for
different types of layers (see Fig. 8B).
The purpose of a link between a node pair is the binding of two elements of
the same type with a single line. This paragraph intentionally does not mention
the styling techniques that use colour, thickness, and other accessible attributes
of a line, which are widely observed in surveys and literature [26], [27]; it presents
the systematization of geometric variations of the link contours, along with the
approach to combining of a guide grid with a coordinate plane for the purpose to
indicate quantitative values. The analysis of complex multi-dimensional datasets
often requires handling data items of multiple types and also dealing with a rich
variety of connecting ways between such data (see Fig. 9C). The main design goal
is the achievement of visual distinctiveness between different types of linking. An
important aspect of the linking line forming in a graph visualized in Cartesian
plane (as in Fig. 9B) is the shaping of lines (as in Fig. 9A).
Fig. 8. Basic types of layers for circular layout (A): silhouette, pattern, glyph. Com-
bining variations of layers (B): direct review for the related item (blue), overview for a
group of neighbours (violet), report for a pair of distant nodes (dark grey).
Considering that the polar coordinates grid has two basic elements (the cir-
cle and the intersection of straight lines) among the variations of the line shape
it is worth highlighting the following. Orthogon, which, in principle, may rep-
resent an alternate polygon fitted for the polar grid properties. Applicable for
symmetrically spaced distant nodes. Round, which follows the shape of a circu-
lar diagram and looks neat at close radial distances. Conoid, in contrast to the
previous types, it is fully overlaid on the grid. It combines the ability to vary in
two dimensions at once, through a rotation angle and a height above/below the
node placement guide. In addition to joining a pair of nodes, it may denote the
quantitative or chronological properties of a link by the sequence of bends.
Many graphical solutions were proposed and applied to indicate containment
contexts of node sets: i.e. belonging of data units to certain classes for both
hyper-links in graphs [28] and groups in node-link diagrams [29]. In comparison
with probabilistic layout techniques (e.g. springs-embedded [30]), the precise
geometric layouts give more possibilities for visualizing containment.
Fig. 9. Regular geometric transformations of linking (A, top) and multi-linking (A,
bottom) lines. Modulation of direct linking lines by styling (B). Variations of link
styles, depending on a distance between nodes on a circular guide (C).
Fig. 10. Grouping through distortion of alignment patterns (A). Visual solutions for
neighboring (B, left) and distant (B, right) nodes. Five containment contexts of a violet
node via presented techniques (C). Steps of compression of nodes into a group (D).
Visual grouping of nodes can be done using the alignment by a grid (in order
to achieve uniformity or proximity of adjacent units) and express containment
through graphic connectors. In the case of alignment (Fig. 10A), the visual effect
of grouping is achievable through gaps and spaces on the circular visualization, as
well as using layers. To express containment, neighbour nodes can be combined
in groups of geometric outlines or underlines (Fig. 10B, left), distant nodes can
be gathered in groups with compound geometric or free-form contours and paths
(Fig. 10B, right), or via visual compression (as in Fig. 10D).
Depending on the task and the level of visual differentiation for node-link-
group diagram items, the circular layout may contain combinations of completely
different approaches to design or synthesis. Having a rich and wide overview of
the variety of representing modes for each element, it is important to know how
to combine them effectively and properly for practical applications.
5 Conclusions and Future Work
Definitely, the visual analytics technologies have great potential for increasing
the effectiveness of interactions during data exploration, as well as for improving
the readability and aesthetic qualities of data visualization.
Among the advantages of drawing gestures is the distribution of application
functions between different physical user actions. Implementation of the pre-
sented interaction style for enough complex data visualization tools, intended
for use by qualified users, can speed up the analytical work by replacing multi-
step actions (like selections in cascade menus) with pen-centric shortcuts. During
filtering and querying, when it is required just to “outline the boundaries” of
interesting values, this interaction style allows to significantly reduce a number
of short-term user manipulations with control elements by accomplishing the
task in one gesture.
The future work towards reactive interacting will expand the capacity of
sketch-based gestures, applicable in visual analysis of various by type and struc-
ture datasets, through the usage of multiple-touch and multiple-stroke actions,
and their combinations. Particular attention will be paid to studying of the users
learnability and preferences in a choice of gesturing methods and techniques for
specific view-driven applications.
Developing of the guidelines and framework for graph-based data visualizing
using circular techniques, which mainly focuses on the different possibilities of
visual representation of graphs and hyper-graphs, has a purpose to help prac-
titioners to have an easy reference in choosing the right technique according
to specific needs. This work will continue by assembling existing techniques in
the literature and developing the software framework that provides a systematic
proposal of a set of techniques, which can be useful for designing of static or
dynamic graphs and hyper-graphs.
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