=Paper=
{{Paper
|id=Vol-2485/paper26
|storemode=property
|title=Advantages of Interactive Visualization Tools in Planning Tasks
|pdfUrl=https://ceur-ws.org/Vol-2485/paper26.pdf
|volume=Vol-2485
|authors=Alena Zakharova,Evgenia Vekhter,Alexey Shklyar
}}
==Advantages of Interactive Visualization Tools in Planning Tasks==
https://ceur-ws.org/Vol-2485/paper26.pdf
Advantages of Interactive Visualization Tools in Planning Tasks
A.A. Zakharova1, E.V. Vekhter2, A.V. Shklyar2
zaa@tu-bryansk.ru |vehter@tpu.ru|shklyarav@tpu.ru
1
Bryansk State Technical University, Bryansk, Russian Federation;
2
Tomsk Polytechnic University, Tomsk, Russian Federation
The paper proposes the use of visualization tools as an independent or complementary tool designed to solve problems related to
the planning and audit of the results of various processes. The advantages arising as a result of transition to attraction of visual
perception for formation of the General idea of process and its results existing in the form of heterogeneous data are shown. The use of
visualization tools to find contradictions and errors made at the stage of process design is proposed. The proposed tool for visualizing
an educational environment is supplemented by an ability to save options for solving planning problems and for corresponding real
results. This creates conditions for a planning search for periods of varying lengths, during which the search and evaluation of factors
that have necessary effect on the achieved results of the educational program are carried out.
Keywords: visual analytics, visual model, data analysis, visual interpretation, visual perception.
1. High requirements for specialist’s qualifications involved in
1. Introduction the analysis, which increase overall resource intensity for
searching the necessary solution.
The basis of the “solving the planning problem” definition in
2. The need in interdisciplinary interaction between researchers
current article is an idea of the sequence of actions taken to
complicates the organization of analysis and increases the
achieve the predetermined goal. The goal is the state of the
risk of interpretation errors.
studied system, characterized by the set of values which are
3. While solving applied problems associated with the audit of
characteristics of the system. Thus, the solution to the planning
achieved results, there is a need to search and eliminate
problem is the description of the process, which ends with
internal contradictions in the studied data, which source is
obtaining the state of the {Pi} system with accuracy satisfying
errors made earlier at the planning stage. Subjective nature
the decision maker (DM).
of such contradictions complicates their detection by
While solving problems of the indicated type, or when it
traditional means of analysis based on previously formulated
becomes necessary to compare the achieved results and the
criteria.
resources expended, the task is to jointly analyze data included
in individual elements descriptions of the investigated (planned) 2. Formal statement of the problem
process. Data analysis aims to achieve two goals:
1. Identification of contradictions between disparate The analyzed process E is an ordered set of elements
information descriptions of processes included in the main O = {O1..On }, where Oi is an elementary process defined by a
process; set of unique properties. Properties of the element Oi are divided
2. Determination of possible ways and selection of the best into two subsets, based on their functional differences:
ones, in terms of resource intensity, to improve the achieved Oi = {Pi}= {Inx, Outy},
results. where the subset {Inx} is process requirements, incoming
The initial data in the analysis is the set of various data that connections of Oi element, and the subset {Outy} is results of the
characterize the studied process – heterogeneous data. process, outgoing connections of Oi element. Connections of the
Interpretation of such data is aimed at revealing implicit patterns Oi element determined in this way are its main characteristics,
in heterogeneous descriptions of processes and at obtaining new which ensure interaction with other elements and achievement of
knowledge. Systematization of efforts made by a researcher in the goal of the main process E.
carrying out the analysis of heterogeneous data, aimed at In accordance with these definitions, the goal of the main
formalizing procedures for setting and solving similar problems, process E can be represented as a composition of outgoing
it will make it possible to generalize the accumulated experience connections, characterized by some parameters, for example, the
and simplify the interpretation of the initial data. An element of number of outgoing connections (results) and some weighting
the educational program - curriculum for bachelors was taken as factors. Thus, the output of the set O={O1..On} is determined by
the initial data in this article (Fig. 1). the predetermined goal G={Out} and available resources.
The formal goal of solving the planning problem formulated in
this way is to find the set O that provides given or best results
while observing all boundary requirements of B:
O={On : n = min, G=max} (1)
In the simplest case, when properties of elements Oi are
specified, the goal of the solution is to order the set O, which
ensures the fulfillment of the requirement (1). In the opposite
case, the solution to the problem is a new set O* = {In*, Out*}
which corresponds to requirements of B.
3. Data Features
Fig. 1 Example of educational planning data The amount of data and a number of additional information
have an influence on the interpretation process of the studied
Large amount of input data and limitation of available data. Some information is not directly related to the problem
resources (computational, time, human) often become a problem solving [3, 5]. At the stage of preliminary analysis of the initial
of practical analysis of heterogeneous data while designing and data or during the process of solving the problem the analyst gets
controlling results of various processes. Traditional means of a lot of additional information (intermediate solutions,
interpretation and analysis have several disadvantages [2, 4]: verification data, classification and clustering results, etc.). In
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
addition, the process of researching and interpreting of data, However, this is reasonable only for a number of problems, the
which requires significant time resources (hours, days), is formulation of which implies the possibility of setting a direct
associated with the need to study new data - replacing or question and receiving an answer as a necessary result [6]. For
complementing the old ones. many practical studies, analysis goal achievement is possible as
History of data acquisition, methods of their collection, the a result of the sequential solution of a number of problems,
presence of errors, partial lack of data, etc. could have a including when formulation of the next intermediate problem is
significant impact on the research objective achievement. possible only after obtaining the solution to the previous problem
Metadata - second-ordered data, play the role of an additional [10].
source of information for the researcher and are a part of An example of analysis tasks with similar properties is the
heterogeneous data. Thus, the peculiarity of studying interpretation of empirical data obtained during practical studies
heterogeneous data which are into the user's disposal while with a low level of formalization of the subject area [13]. In this
solving a planning problem is the need to compare many case, the purpose of the ongoing visual analysis is to obtain new
heterogeneous descriptions of individual elements of the process, knowledge based on detection of internal patterns in the studied
taking into account intermediate versions of solutions and the data and on the study of their properties. On this basis, similar
need to update interpreted data during analysis [1, 8]. As a tasks, which require consistent study, possibly with the use of
perspective way to overcome these difficulties, the use of data various tools of analysis, are classified as analytical.
visualization tools with the ability of organizing interactive The planning task is considered as a study of the set of
interaction between a user and the initial data is proposed. boundary requirements aimed at finding parameters of the set
O={O1..On} that ensure the best results. The ambiguity of the
4. User’s abilities target function becomes the reason for using analytical
visualization tools that allow to a decision maker to clarify the
The need to use perceptual capabilities, characterized by
problem statement while its solving. An additional circumstance,
empirical principles, sets to a developer a task of determination
which involves the choice of interactive visualization tools of the
of the set of user characteristics that are purposefully involved in
problem, is the need to coordinate preliminary awareness of a
the interpretation of the visual image of data. Selection can be
potential user (previous experience) and visual research features.
based on a preliminary assessment of available resources, which
include: 6. Visual representation function of the planning
1. Characteristics of a potential user, whose involvement in the
process of data interpretation does not create prerequisites for
task
increasing its duration. Basing on the formulated requirements for visualization
2. Computing resources, which allow to obtain visual images of tools, which are necessary to solve the planning problem, a
data, if they meet the requirement of interactivity with them. system for interactive presentation of data, which are included in
3. Temporary resources that determine speed of construction the description of an arbitrary educational program, has been
and interpretation of visual data images. developed (Fig. 2). Three-dimensional visual model is proposed
4. Additional requirements arising from the statement of the as a visualization tool designed to solve the problem, it forms a
research task, including: prior knowledge of a user, his visual image of information objects included in the source data
qualifications, probable features of perception, etc. [11]. An informative object is an element of an educational
The set of user’s characteristics involved in the cognitive program (an academic discipline, a course, program’s section).
interpretation of the visual image of the studied data can be Each such object is an array of data (name, course, duration,
determined by generalization existing schemes of the capacity, incoming requirements, planned results), including
visualization process [9]. Based on the obtained set, three groups variables of different types.
of visualization tools are distinguished, which differ in functional There has been developed a software that allows to get an
purpose and in methods of practical implementation: interpreted visual image with the use of visualizers of Autodesk
1. Observation. Obtaining visual information (perception of 3ds Max package. The algorithm for constructing a visual image
color, space, movement, allocation of groups, recognition of is implemented using Maxscript language, therefore it is portable
forms, signs). and can be easily adapted to new visualization technical
2. Search. That means to identify relevant objects and processes capabilities. Visualization interface only partially uses Autodesk
(spatial thinking, prior awareness, motivation) in the initial 3ds Max environment and can be adapted to the needs of a
visual information. particular user.
3. Formulation. Formulation of the answer’s hypothesis to the Reasonable interactive management system creates
research question (experience of using visual analytics, an conditions for setting new research questions and receiving
ability to study and apply new language systems), answers quickly, accelerating achievement of the analysis goal.
formalization of new information. Consequently, interactive features of the visualization tool
Development of computer visualization technologies and determine sequence and logic of a researcher's reasoning.
their continuous complication create difficulties in interaction To reduce the training period required to become familiar
between a user and developed visualization tools [7]. This with the new interpretation tool, it is proposed to use a
circumstance becomes critical in a situation where visualization representation metaphor based on traditional methods of
tools provide cooperative participation of a group of researchers visualizing tabular data (charts, graphs). In 3D space of the
or are a way of exchanging information between specialists with visualization tool, a cylindrical coordinate system is defined, that
different levels of training or area of specialization. There is a allows each point in space to be matched with three values:
need to choose between users ability to use new visualization training time, load, result. The scale of measurement units along
tools or involving existing visual communication skills to time and result axes can be arbitrary, the load is measured as a
interpret data. percentage accordingly to the maximum possible. The ability to
compare objects is realized through the use of color coding,
5. Analytical visualization tools which rules can be changed in accordance with perceptional
characteristics of a particular user [12].
A significant part of tasks, where visualization tools are used,
implicitly takes into account the assumption that the research
question’s answer can be obtained as a result of a single
interpretation of visualized information by a researcher.
Fig. 2. Visualization of educational process description
Fig. 3 Visualization of accumulated results
The radial direction of the time axis allows to visualize
educational programs data of any duration (bachelor's degree, 7. Benefits of visualization tools
specialist’s program and master's degree). Program steps, which Practical usage of the developed visualization tool made it
correspond to the given time intervals (years, semesters) are possible to get an assessment of the proposed approach benefits
divided by concentric circular elements used to represent the comparing to traditional methods of interpreting and verifying
accumulated results. Each concentric element is a reference scale heterogeneous data contained in documents, which regulate
(0-100%) with a common starting point. The proposed structure educational programs.
provides a presentation of an increasing number of learning The proposed tool for visualizing an educational
results without worsening general perception of data. environment is supplemented by an ability to save options for
The information object provides to an observer an solving planning problems and for corresponding real results.
opportunity to interpret visual attributes as values of the This creates conditions for a planning search for periods of
corresponding parameters: color is an identification attribute, varying lengths, during which the search and evaluation of
dimensions are load ones, position corresponds to the training factors that have necessary effect on the achieved results of the
period. Information about incoming requirements and planned educational program are carried out.
learning results is presented as links between information Usage of visual analytics in this direction makes it possible
objects. Attributes of such connections are their direction and to form a detailed idea about patterns of an educational
quantity, which correspond to the source data. In accordance with environment functioning. The combination of visual design tools
characteristics of the subject area, incoming (requirements) and of an educational process and accumulation of knowledge about
outgoing (results) communications have the same type, that achieved results creates a unique information environment,
means it can be interpreted as created or developing access to which can be useful to both the "organizers" and the
competencies. "participants" of this research process. Due to features of
For a visual presentation of accumulated learning results visualization tools, an interdisciplinary analytical approach for
(Fig. 3), the simultaneous use of two expressive means is solving problems of designing complex multi-factor processes is
proposed: a rating scale of effectiveness and visual scaling of formed. The potential benefits of this approach might be the
links. In the first case, it becomes possible to efficiently use the following features:
three-dimensional space of the visual model, in the second one - • Reduction of time required to analyze large amount of
the assessment of training results in the educational program can heterogeneous data.
occur while interpreting two-dimensional visualization. • Usage of highly specialized experience of specialists in the
The method for visualizing intermediate and general results given subject area without the need of analysts participation
(Fig. 2) in the form of profiles for summing results was who have skills in data analysis field.
developed. The profile has the form of a histogram representing • Possibility of organizing a collective solution to the planning
accumulated results as a percentage of planned values. To problem, taking into account a significant amount of
simplify the image of the studied data, elements of the results additional boundary conditions that are formed during the
presentation can be temporarily excluded from the visual model. solution process.
Visual objects, which present to a user data of the achieved • Formalization of knowledge related to the solution of the
learning results, perform one of the main functions, which problem of planning the studied process, which provides
consists of searching for options of individual elements effectiveness increase of solving similar problems in the
characteristics, which satisfy the condition (1). Thus, the use of future.
visual presentation elements as a system of interactive control of • Reduction of time required to a decision maker to complete
the state of the visualization tool is proposed. The interface of the design process, based on the possibility of obtaining a
interaction with the visualization tool created in this case complete picture of the studied data, as well as based on
becomes the basis for a cognitive interpretation of the source data persuasiveness of visualization tools.
images, taking into account individual characteristics of a user's
thinking.
8. Usage prospects • Duplication of results. Resources usage for repeatedly
obtaining similar results. The visual image of such a
Perspective options for using user’s interaction with the contradiction is diverging lines of cumulative results.
developed visualization tool can be considered as follows:
• Analysis of the set of profiles of intermediate results 9. Conclusion
simultaneously with the general idea of the studied process,
formed using the developed visualization tool, allows to The use of visualization tools as a tool of operational data
make dynamic correction of the planned results. research in tasks planning is an example of visual analysis of
• Identification and elimination of contradictions in the source heterogeneous data. The obtained advantages are high speed of
data. Lack of input data: incoming requirements of the visual perception, which is necessary for simultaneous
information object are not provided with results. The comparison of large amount of disparate facts, as well as the
requested data are missing. It is detected as an open input of ability to interpret data not only in numerical form.
an information object (Fig. 4). • The proposed visualization tool allows to solve problems of
an analytical type, to carry out sequential operations of
obtaining, interpreting information, forming hypotheses,
making decisions about changing goals or decision
conditions.
• The technique of using visualization tools allows to achieve
the research goal without interpreting numerical values.
Thus, an approach to solving problems of this type, based
solely on visualization tools, is proposed.
• A version of the source data visual presentation has been
developed, which ensures a reduction of resource intensity of
the user’s training phase.
• The visualization tool has low dependence on the specifics
of the task’s subject area. Thus, experience of using the
proposed means of interpretation can be involved in solving
other problems.
• Low requirements for preliminary preparedness of users
provide an ability to use visualization tools in case of absence
of special training in solving problems of data interpretation,
which have an interdisciplinary nature.
Fig. 4. Lack of input data
10. Acknowledgements
• Chronological discrepancy between incoming requirements
of the information object and the results achieved. This work was supported by Russian Science Foundation,
Contradiction arises if the input of the information object project 18-11-00215.
requires data that will be received later. It is visualized as a
line of accumulated results going in the opposite direction
(Fig. 5). 11. References
[1] Batch A., Elmqvist N. The Interactive Visualization Gap in
Initial Exploratory Data Analysis. // IEEE Transactions on
Visualization and Computer Graphics, 2018, Vol. 24, Issue
1, pp. 278-287. doi: 10.1109/TVCG.2017.2743990
[2] Blascheck T. [et al.]. VA2: A Visual Analytics Approach
for Evaluating Visual Analytics Applications // IEEE
Transactions on Visualization and Computer Graphics,
2016, Vol. 22, Issue 1, pp. 61-70. doi:
10.1109/TVCG.2015.2467871
[3] Cano S. [et al.]. A Visualization and Human-Computer
Interaction Proposal in the Context of Pronunciation
Information // IEEE Revista Iberoamericana de Tecnologias
del Aprendizaje, 2016, Vol. 11, Issue 1, pp. 12-17. doi:
10.1109/RITA.2016.2518440
[4] Chen C. Top 10 Unsolved Information Visualization
Problems // IEEE Computer Graphics and Applications,
2005, Vol. 25 Issue 4, pp. 12–16. doi:
10.1109/MCG.2005.91
[5] Chen M., Golan A. What May Visualization Processes
Fig. 5 Chronological contradiction Optimize? // IEEE Transactions on Visualization and
Computer Graphics. 2016, Vol. 22, Issue 12, pp. 2619–
• Unreasonable usage of existing resources. The result of the 2632. doi: 10.1109/TVCG.2015.2513410
interpretation of result lines that are not presented at the [6] Crouser R.J. [et al.]. Toward Theoretical Techniques for
output of the overall process. Measuring the Use of Human Effort in Visual Analytic
• Degradation of results. The proposed visualization tool takes Systems // IEEE Transactions on Visualization and
into account possibility of reducing the level of the achieved Computer Graphics, 2017, Vol. 23, Issue 1, pp. 121-130.
result according to time. A contradiction is the results doi: 10.1109/TVCG.2016.2598460
presence in the final profile that do not take into account the [7] Dasgupta A. [et al.]. Familiarity Vs Trust: A Comparative
level of their degradation. Study of Domain Scientists’ Trust in Visual Analytics and
Conventional Analysis Methods. // IEEE transactions on
visualization and computer graphics, 2017, Vol. 23, Issue 1,
pp. 271-280. doi: 10.1109/TVCG.2016.2598544
[8] Matsushita M., Kato T. Interactive Visualization Method
for Exploratory Data Analysis // Proceedings Fifth
International Conference on Information Visualisation,
2001. doi: 10.1109/IV.2001.942128
[9] Pirolli P.L. Information Foraging Theory: Adaptive
Interaction with Information. Oxford University Press,
2007.
[10] Podvesovskii A.G., Isaev R.A. Visualization Metaphors for
Fuzzy Cognitive Maps // Scientific Visualization, 2018,
Vol. 10, No. 4, pp. 13-29. doi: 10.26583/sv.10.4.02
[11] Zakharova A.A. [et al.]. Visual modeling in an analysis of
multidimensional data // IOP Conf. Series: Journal of
Physics: Conf. Series, 2018, Vol. 944, 012127. doi:
10.1088/1742-6596/944/1/012127
[12] Zakharova A.A., Vekhter E.V., Shklyar A.V. The
Applicability of Visualization Tools in the Meta-Design of
an Educational Environment // European Journal of
Contemporary Education, 2019. Vol. 8, No. 1, pp. 43–51.
doi: 10.13187/ejced.2019.1.43
[13] Vieira C., Parsons P., Byrd V. Visual learning analytics of
educational data: A systematic literature review and
research agenda // Computers and Education, 2018, Vol.
122, pp. 119–135. doi: 10.1016/j.compedu.2018.03.018