Visual Analytics Towards Tool Interoperability

A Position Paper

Didem Gürdür, Fredrik Asplund, Jad El-khoury, Frederic Loiret and Martin Törngren

Department of Machine Design, KTH Royal Institute of Technology, Stockholm, Sweden

Keywords: Tool Chain Visualization, Interoperability, Visual Analytics, Data Visualization.

Abstract: Complex-engineering projects include artefacts from several engineering disciplines such as mechanical,

electrical, software components, processes and plans. While software tools can be powerful in each

individual discipline, it is difficult to build integrated tool chains. Moreover, it is challenging to evaluate and

update existing tool chains. At the same time, the field of visualization is getting mature and visual analytics

promises an opportunity to develop knowledge, methods, technologies and practice for exploiting and

combining the strengths of human and data. We consider this as a potential to evaluate current tool chains.

This position paper discusses the visualization and visual analytics practices to assess existing tool chains

performance.

1 INTRODUCTION

Development in complex engineering projects

requires tool support from different engineering

disciplines for different phases of the product

lifecycle. Furthermore, each engineering field uses

specific software tools that focus on explicit tasks

throughout the product development process.

Engineers therefore face problems with tool

interoperability through technological problems

related to data transmission or the interpretation of

the transferred data (Yan et al., 2010). Fortineau et

al., (2013) particularly highlight different

interpretations of data that is located in

heterogeneous environments as problematic. These

heterogeneities are based on the differences between

computing environments, languages, techniques,

tools and data sources (Paviot et al., 2011;

Giunchiglia et al., 2004; Spalazzese, 2009), in

different areas of expertise. The absence of

interoperability between tools results in high

development costs and reduced product quality

(Schürr and Dörr, 2005).

This position paper motivates the adaptation of

visualization analytics to interoperability research,

with the aim of facilitating tool interoperability in

heterogeneous engineering environments.

Section II provides a background to both

interoperability and visualization. Section III

describes opportunities related to utilizing visual

analytics approaches to enhance interoperability, and

also discusses the associated challenges. We discuss

technical aspects briefly on the Section IV and end

the paper by outlining the future research required to

overcome these challenges and make good on the

opportunities.

2 STATE-OF-THE-ART

At least 30 different definitions of interoperability

have been used in the literature during the last 30

years (Ford, 2007). Interoperability is a

multidimensional concept, which comprises several

perspectives and approaches from different

directions for different domains. Today altered

definitions of interoperability exist in the literature.

We will use IEEE definition in this paper that states

that the interoperability is: “The ability of two or

more systems or components to exchange and use

the exchanged information in a heterogeneous

network” (Geraci et al., 1991). One of the possible

interoperability problems occurs among tools. Tool

interoperability is a special case of interoperability,

which focuses on the interactions between software

tools in these systems.

A substantial amount of research effort has been

spent in this research field, but interoperability still

remains a broad and complex topic – and measuring

GÃijrdÃijr D., Asplund F., El-khoury J., Loiret F. and TÃ˝urngren M.

Visual Analytics Towards Tool Interoperabilty - A Position Paper.

DOI: 10.5220/0005751401390145

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (IVAPP 2016), pages 139-145

ISBN: 978-989-758-175-5

139

interoperability is especially difficult. Nevertheless,

assessing interoperability with well-chosen measures

is essential for identifying priorities in product

development. Many researchers have studied such

assessments and many approaches are proposed in

the literature (LaVean, 1980; Mensh et al., 1989;

Amanowicz and Gajewski, 1996; Clark and Jones,

1999; Hamilton et al., 2002). Wasserman introduced

5 widely accepted categories or dimensions of tool

integration as Control, Data, Platform, Presentation

and Process (Wasserman, 1990). Asplund and

Törngren identified a set of stakeholders (such as

application domain experts, project managers,

managers, support environment administrators,

customers and standardization organizations) and

six non-functional properties (flexibility, scalability,

cost, evolve ability, efficiency and the degree of

standardization) as especially important in the

subsequent discourse (2015). However, none of

these approaches aimed at developing an application

to measure interoperability. Furthermore, none of

them propose to use data visualization and

visualization analytics as a method to examine tool

interactions.

A path forward would be to leverage on Model

Based Engineering (MBE), which is gaining traction

based on its ability to address platform complexity:

MBE tools impose domain specific constraints to

perform model checking that can detect and prevent

errors in the early stages of the product lifecycle

(Schmidt, 2006). MBE relies on modelling the

product, and then implementing, testing, simulating

and analysing the product based on the models. An

extension to MBE could involve modelling and

automatically synthesizing the tool chains used

throughout the product development (Biehl, 2013).

However, in the industry, tool chains that represent

large investments of time and money often already

exist. A large effort might have been spent on

acquiring suitable tools and training employees in

their use. This would act as a determent to the time

consuming modelling of tool and tool integration,

especially if models are not able to capture all the

required details. To fill this gap between existing

and envisaged tool chains a complementary

approach is needed.

Ways to represent complex relationships already

exist, e.g. bottom up visualization techniques.

Gershon (1992) defines visualization as follows:

“Visualization is more than a method of computing.

Visualization is the process of transforming

information into a visual form, enabling users to

observe the information. The resulting visual display

enables the scientist or engineer to perceive visually

features which are hidden in the data but

nevertheless are needed for data exploration and

analysis.” The visualization research field includes

studies of techniques for creating statistical graphics,

plots, tables, charts, etc. The primary goals of data

visualization are to communicate information clearly

and efficiently to users; to confirm analysis as a

goal-oriented examination of hypotheses; and to

explore data analysis as an interactive and usually

undirected search for structures and trends.

Effective

visualization helps users to analyse and reason about

data and evidence. It is worth underlining that visual

analytics is more than only visualization. According

to Keim et al. (2008)

“Visual analytics combines

automated analysis techniques with interactive

visualizations for an effective understanding,

reasoning and decision making on the basis of very

large and complex data sets.”. The fields of

visualization and visual analytics build upon

methods from scientific analytics, geospatial

analytics and information analytics (Wong and

Thomas, 2004). They profit from knowledge out of

the field of interaction as well as cognitive and

perceptual science. However, they are distinct from

each other, since visual analytics integrate

methodology from the statistical analytics,

knowledge discovery, data management and

knowledge representation research fields (Andrienko

et al., 2010).

Figure 1: Visual analytics process defined by Keim et al.,

(2008).

Visual analytic tools and techniques are useful

for synthesizing information and to derive insight

from massive, dynamic, ambiguous, and often-

conflicting data, to detect the expected and discover

the unexpected, to provide timely, defensible, and

understandable assessments and to communicate

assessment effectively for action. Keim et al. (2008)

introduced a framework for the visual analytics

process, which is shown in Figure 1. The process

starts by transforming the data by e.g. filtering and

sampling in order to extract meaningful units of data

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

140

for further processing. Next, a visual or automatic

analysis method needs to be applied, e.g. data

mining to estimate models for characterizing the

data. Finally visual data exploration is used, in

which users directly interact with the visual interface

to analyse and explore the data. The resulting

knowledge can then be fed back into further

iterations of the process.

Although visual analytics is not yet a best

practice in industrial product lifecycle processes the

use of databases and statistical techniques are not

new to manufacturing and engineering. Examples of

data and knowledge applications of artificial

intelligence could be found in manufacturing as

early as in the late 1980s (Ramamoorthy and Wah,

1989). The evolution in information technology,

data acquisition systems, and storage technology has

enticed researchers to study the use of knowledge

from databases. Today data from almost all

organizational processes is used in analyses,

including requirements, material planning and

control, product and process design, assembly,

scheduling, sales and maintenance. Moreover, this

data has a large potential both as a source of new

knowledge and a basis for operational predictions.

3 MOTIVATION

The overload of data is well-known phenomenon:

today data is produced at a rapid rate, and the ability

to collect and store data is increasing at a faster pace

than the ability to analyse it (Keim et al., 2008). In

many fields visualization methods and visual

analytics are therefore already commonplace. In

news, banking and management tools these methods

are extensively used to give users an overview of the

saved data. In fact, these tools frequently make

suggestions or otherwise simplify and facilitate

decision-making processes.

Data is raw, unorganized facts and statistics -

often simple, seemingly random and useless until

organized. On the other hand, information is the

processed, organized, structured data that is

presented in a given context so as to make it

valuable. In engineering, visualization and visual

analysis of tool chains could help tool chain

developers to quickly sort and analyse large,

disordered and inconsistent volumes of data and

extract comprehensible information out of it. In the

manufacturing, design, business, and medical

domains the identification of valuable patterns has

been an ambition for long time. This stems partly

from the need to deal with associated high-level

problems related to entire socio-technical systems:

for instance difficulties in adapting tool chains to

new domains, the unfeasibility in scaling tool chains

as organizations grow, tool “lock-in” due to business

models of tool vendors, technology hampering the

efficiency of organizations due to tool chains

mismatches, and non-standardized tool integration

that cannot be evolved to meet production needs

(Asplund and Törngren, 2015). Visual analytics

provides an opportunity to easily find patterns that

might help solve these problems. We believe it is

especially promising to extract patterns on tool

chains and tool interactions, such as which tools that

interact, how frequent these interactions are, what

data that is shared between tools, how many users of

the tools that exist, where the users are located, etc.

Moreover, visual analytics could be a tool to

improve interoperability by leveraging on any

interaction patterns thus revealed.

Visualizations could also be useful in a

preliminary phase to model tool chains more

efficiently. One could extract the relationships that

exist in current tool chains through visualization

techniques, optimize the tool chain for better

interoperability according to well-defined metrics,

and model optimized tool chains through selected

MBE technologies.

Complexity could be classified as a property of a

scenario or as a relation. Kopetz (2013) defines

cognitive complexity as a “relation between a

scenario and an observer who tries to understand

the scenario”. To understand the scenario one needs

to link new concepts or dependencies with already

familiar concepts. We believe visualization and

visual analytics could create this link effectively

since visuals/images have been the foundation of

human understanding since the beginning of

recorded history. Another goal of visual analytics in

the engineering domain could therefore be the

evaluation of complexity. For instance, one could

discern tools that are not part of tool chains by

visualization and consider the need for integrating

them.

Visualization aims to visually represent the data

and visual analytics allow the user to directly

interact with the information, to quickly draw

conclusions and gain insights, and to eventually

make optimal decisions. Furthermore, visual

analytics could combine automated analysis

techniques with interactive visualizations for an

effective and efficient understanding, reasoning, and

decision making (Keim et al., 2008) of tool chain

developers/analysts. These analytics techniques

could include artificial intelligence and machine

learning methods where the visual analytics tool

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gives suggestions that can be used to achieve better

interoperability. As an example, analytics about the

sustainability of a tool chain could be introduced by

these methods if suitable metrics where in place.

Tool chains are commonly illustrated by block

diagrams as shown in Figure 2. However, real tool

interactions are more complex and multi-

dimensional. For instance, the location of databases,

the number of active users, and the frequency of tool

interactions in a tool chain are often not taken into

consideration; taking further action, such as adding

safety goals based on tool interactions, calculating

the cost of changing a tool chain, assessing

organizational aspects, etc. is not possible.

Figure 2: Block diagram of a sample tool chain.

Even though MBE has an important role in the

development of new tool chains, it is not meant to be

used for understanding existing tool chains, but to

create new ones. Modelling tool chains could be

more beneficial when we have the framework or

platform to extend the model for generating some

interfaces. However, this framework or platform

does not exist now and we are not able to use the

model of tool chain or its properties for further

applications. Moreover, we cannot make any

analysis according to these models of tool chain

since it is not really representing the current

situation. It is highly possible to overlook some

aspect due to oversimplification.

Visual analytics or visualisations constitute a

better chance of generating an overview of the

infrastructure in detail: node-link or network

diagrams have graphical advantages, such as being

able to illustrate each tool with different sized circles

(large circles for mostly used tools and small ones

for opposite), locate the real position of databases,

etc. This is illustrated in Figure 3 by a dashboard

that could give tool chain developers a chance to

illustrate different viewpoints according to different

stakeholders and dimensions. This could even help

optimize performance, automation and cooperation

of distributed development teams through the

lifecycle of the product from requirements to

technical support.

Figure 3: Dashboard of a visualized sample tool chain.

Visual analytics is still not a silver bullet for a

future increase in tool chain interoperability. The

main challenge that needs to be considered before

and after applying the approach is the scalability and

dimensionality of data sources. In many

applications, data streams come from multiple,

heterogeneous sources and need to be integrated and

processed together. In this situation, the methods

need to be able to scale with a range of different data

types, data sources, and levels of quality. The visual

representation algorithms need to be efficient

enough for implementation in interactive systems

(Keim et al., 2008).

Another important point to mention is the quality

of the collected data that will be used for analytics.

Collecting massive amount of data from

heterogeneous environments requires a structured

and well-planned approach. It is vital to control, re-

arrange and filter data, and then to choose the best

analysis algorithms to prevent users from being

misled by erroneous analysis results (Kopetz, 2013).

As noted by several authors in the tool integration

discourse, this will rely on providing a graphical

visualization tailored to the specific user rather than

hoping that all users will be able to completely and

consistently understand “generic” interfaces

(Asplund and Törngren, 2015). The level of detail in

visualizations should also be chosen with care: The

visual abstraction or the level of detail in

visualization could hide relevant data patterns. To

avoid this, visualization or visual analytics tool

should facilitate the use of several levels of detail.

Visual analytics tools for tool chain analysis

should be simple and easy to use to help tool chain

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developers to focus on real interoperability issues.

Complex or excessively technical user interfaces

could distract users (Keim et al., 2008). Another

challenge is the evaluation of tool chain

interoperability. It is very important to extract

dimensions, metrics and viewpoints, which play a

vital role for interoperability in tool chains, and then

integrate these with visual analytics. Such evaluation

metrics could be used to filter the visualization, but

are yet to become available.

4 TECHNICAL DISCUSSION

This position paper is the first attempt of complete

research about the applicability of the visualization

and visual analytics of tool chains. In this paper, we

aim to point out the importance of visualization and

visual analytics to improve tool interoperability.

However, it is vital to relate this new topic with

existing visualization and visual analytics

approaches. In this section, we will investigate

relevant solutions from overlapping research areas

such as visual software analytics and workflow

management.

Visual software analytics investigates visual

analytics approaches of the visualization of artefacts

related to software systems and their development

process (Keim et al., 2008). These software systems

are also complex systems and include time

dependency, heterogeneous data, and influenced by

different stakeholders like development tool chains.

Visualization of software evolution classically uses

information about the modifications of the source

code (Voinea and Telea, 2005; Voinea and Telea,

2008), interactions of developers with code (Ma,

2008), or the development on software metrics.

Diehl (2007) provides a comprehensive survey of

software visualization methods and in his study

divides the concern of visual software analytics to

three as; structure, behaviour and evolution. We can

apply these concerns in tool interoperability context

easily. As we already mentioned, like software,

products also evolve in time with contribution of

stakeholders.

The significance of visual representations to

increase the understanding of computer programs is

not new concept. Goldstein and von Neumann

(1963) presented a system of describing processes

using operation, assertion, and alternative boxes

which then called flowcharts and their the

usefulness, whereas Haibt (1959) developed a

system that could draw them automatically.

Afterwards software visualization techniques

continue to enhance and still developing.

Nevertheless, there are fundamental similarities

between software development and product

development lifecycles. In addition there are proven

useful visualization methods in software engineering

field for the development process that can be

migrated to product development context (Price et

al., 1992; Bohner, 1996; Storey et al., de Souza et

al., 2007). One down side is the immature data

mining state of tool chain information in product

development environment when compared with the

visual mining of software repositories. Visual

mining of data repositories in software development

are still more homogenous than the tool interactions

information and there are very few research done on

especially product lifecycle management data.

Ameri and Dutta (2005) states that even though the

product lifecycle management solutions are aiming

to streamline the flow of information about product

data, few organizations are benefiting from it truly.

Moreover, we need to explore deeper to understand

how we can reach more specific information about

tools during the development process.

There are already existing visualization

applications and frameworks such as AVS (Upson et

al., 1989), VTK (Schroeder, 2004), InfoVis Toolkit

(Fekete, 2004) or VisTrails (Callahan et al., 2006).

For instance VisTrails have been used by Hlawatsch

et al. (2015) to visualize and analyse the evolution of

module workflows. Also there are many researches

done for scientific workflow management, which

used Kepler (Altintas et al., 2004) system. There is

also web based open source Data Driven Documents

(D3.js) (Bostock, 2012) framework for creating

interactive visualizations. However we need to

investigate these applications further to understand

how far we can employ the approaches from the tool

interoperability perspective and examination of their

feasibility is not in the scope of this paper.

5 CONCLUSION AND FUTURE

WORK

In this paper we discussed the interoperability issues

in tool chains and explained how important

visualization and visual analytics are to improve the

interoperability of tools. Even though these

techniques are compared with MBE, we do not

intend to replace modelling practices. On the

contrary we believe visualization has a significant

value in aiding tool chain developers, engineers,

analysts, decision makers, and other stakeholders to

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143

promptly gain insights from the high volumes of

data. When combined with analytics, data

visualization promises opportunities in exploring

data quickly and serves as an interaction medium to

augment requirements analyst’s knowledge

discovery with advanced computational capabilities.

This could affect the whole tool chain

interoperability positively and thereby improve

productivity.

In many cases, the information would have to be

collected from heterogeneous data sources and by of

knowledge that currently only exists in the mind of

experts. It is possible to apply analytical reasoning

hypotheses on the data and reach a better

understanding of the data, which supports the user in

his task to gain insight. Visualization and visual

analytics are an opportunity to apply these

hypotheses/methods, to extract patterns of tool

chains and tool interactions, to evaluate complexity

of tool chains, to create overview of the

infrastructure with different view points, to optimize

performance, automation and cooperation of

distributed development teams and over all to

improve interoperability.

One should not forget that real interoperability

issues in industry often consist of a series of

difficulties. Solving one might be accomplishable;

but doesn’t necessarily solve the overall problem.

The main goal of the proposed research is to bring

the power of visualizations and visual analytic tools

to product development to improve interoperability

between tools. In the future, we will perform a

survey in order to extract interoperability metrics,

which will support the filtering mechanism,

evaluation and analysis of tool chains. We will

collect data streams about tool interactions and

evaluate a visual analytics approach on one use case

to elaborate on the resulting opportunities.

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