InfoMINDS: An Interdisciplinary Framework for Leveraging Data

Science upon Big Data in Surface Mining Industry

Vitor Afonso Pinto

1 a

and Fernando Silva Parreiras

2 b

Technology Department, Operational Technology for Base Metals South Atlantic, Vale, Carajas, Para, Brazil

Laboratory for Advanced Information Systems, FUMEC University, Rua do Cobre, Belo Horizonte, Brazil

Keywords:

Data Science, Big Data, Framework, Mining Industry.

Abstract:

Intending to be more and more data-driven, companies are leveraging data science upon big data initiatives.

However, to reach a better cost-beneﬁt, it is important for companies to understand all aspects involved in such

initiatives. The main goal of this paper is to provide a framework that allows professionals from the mining

industry to accurately describe data science upon big data. The following research question was addressed:

”Which essential components characterize an interdisciplinary framework for data science upon big data in

mining industry?”. To answer this question, we will extend OntoDIVE ontology to create a framework capable

of explaining aspects involved in such initiatives for the mining industry. As a result, this paper will present

InfoMINDS - A Framework for Data Science upon Big Data Relating People, Processes and Technologies

on Mining Industry. This paper will contribute to leveraging data science initiatives upon big data allowing

application of OntoDIVE on real-case scenarios in mining industry.

1 INTRODUCTION

Data Science can be deﬁned as an approach to ex-

tract worthy insights from low-value data. Big Data

can be deﬁned as an integrated ecosystem of tech-

nologies performing formal roles with the purpose to

create technical conditions for the delivery of value-

added applications based on data. Data science upon

big data is seen by organizations as a tool to improve

operational efﬁciency though it has strategic poten-

tial, drive new revenue streams and gain competitive

advantages (Sivarajah et al., 2017).

There is a considerable literature addressing major

concepts related to data science and big data. Some

studies proposed ways to characterize data science

upon big data using concepts of volume, velocity,

variety, validity, veracity, variability, visibility, ver-

dict and value (Sharma, 2017; Corea, 2016; Addo-

Tenkorang and Helo, 2016). Other studies proposed

ways to group big data technologies (Bari et al., 2014;

Ahlemeyer-Stubbe and Coleman, 2014; Murthy et al.,

2014). Other studies proposed ways to explain data

science processes (Corea, 2016; Maimon and Rokach,

2010; Abbott, 2014; Takurta et al., 2017).

https://orcid.org/0000-0002-2731-0952

https://orcid.org/0000-0002-9832-1501

Additionally, a range of literature exists suggest-

ing an interdisciplinary approach as a success factor

for data science initiatives (Corea, 2016; Forte, 2015;

Cady, 2017; Luis, 2017). Other studies presented

results generated by data science initiatives that in-

volved multiple knowledge areas (Xu et al., 2014;

Fisher et al., 2017; Roy et al., 2017; Capalbo et al.,

2017; Zhou et al., 2016; Arias and Bae, 2016; San-

toro et al., 2018; Hurwitz et al., 2015; Van Der Aalst,

2016; Zheng et al., 2015; Tal

on-Ballestero et al.,

2018; Balliu et al., 2016; Lei et al., 2016; Maciejew-

ski, 2017; Lu and Li, 2017; Stoet and Geary, 2018;

Seele, 2017; Lubchenco and Grorud-Colvert, 2015).

Even with all the progress that has been made,

mining industry is still grappling with how to cap-

ture insights that are not obvious. As an example,

mining personnel not always understand how data sci-

ence initiatives are conducted in other industries. An-

other aspect is that mining companies not always have

expertise to deﬁne and deploy big data technological

ecosystems. Although big data comprises technolo-

gies performing formal roles, technologies may vary

among organizations and each technology should be

minutely chosen to avoid loss of effectiveness. Thus,

there is a risk of receiving biased advisory from con-

sultants who try to push technologies based on their

own interests or limitations.

784

Pinto, V. and Parreiras, F.

InfoMINDS: An Interdisciplinary Framework for Leveraging Data Science upon Big Data in Surface Mining Industry.

DOI: 10.5220/0010484107840791

In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 2, pages 784-791

ISBN: 978-989-758-509-8

Considering the lack of concepts as a major con-

tributing factor for preventing the leverage of data sci-

ence initiatives upon big data, it is crucial to explain

all concepts related to this kind of initiative and the

interactions between and among them. The main goal

of this paper is to propose a conceptual framework

that allows professionals from the mining industry to

accurately describe data science upon big data.

Our approach differs from others as we intend to

create common ground so that data science initia-

tives upon big data can be fully understood by pro-

fessionals from any area of knowledge. In order

to contribute to the body of knowledge, this paper

is more interested in the general idea or conception

behind data science initiatives upon big data rather

than any individual instance of those initiatives. In

this context, the following research question was ad-

dressed: ”Which essential components characterize

an interdisciplinary framework for data science upon

big data in mining industry?”.

By addressing this research question, in this paper

we propose InfoMINDS which is a conceptual frame-

work that organizes practices commonly applied dur-

ing data science initiatives upon big data, consider-

ing a comprehensive and end-to-end perspective. This

framework may contribute either to clariﬁcation of

concepts or the explanation of interactions between

and among them. InfoMINDS can also be considered

as a foundation upon which mining companies can

build policies, standards, rules, procedures, method-

ologies or any other artifact in order to leverage data

science initiatives upon big data. This paper is struc-

tured as follows: Section 1 presents the context of this

work. Section 2 presents methods of research. Sec-

tion 3 presents results that are discussed in Section 4.

Section 5 concludes the paper.

2 METHODS

A conceptual framework may be deﬁned as an end

result of bringing together a number of related con-

cepts to explain or predict a given event or to give

a broader understanding of the phenomenon of in-

terest (Imenda, 2014). It is a visual presentation of

key variables, factors or concepts and their relation-

ship among each other which have been or have to

be studied (Miles and Huberman, 1994). The main

purpose of a conceptual framework is to bring focus

on the content and to act as a link between litera-

ture, methodology and results. We decided to build

a conceptual framework as it provides understanding,

rather than offering a theoretical explanation (Jaba-

reen, 2009).

In order to guarantee completeness and correct-

ness of InfoMINDS Framework, we decided to im-

plement it using OntoDIVE Ontology, presented in

(Pinto and Parreiras, 2020). In practical terms, we

created individuals on class Frameworks to repre-

sent either the InfoMINDS framework itself and each

one of its dimensions. We also created individuals on

class Processes to represent each of InfoMINDS pro-

cesses. As this study is focused on mining industry,

we created individuals on Class Frameworks to rep-

resent either the mining industry and each one of its

production phases (mining and mineral processing).

3 RESULTS

3.1 InfoMINDS Structure

InfoMINDS is structured in twenty-ﬁve processes that

are grouped in ﬁve dimensions. Processes derived

from existing multidisciplinary literature. Dimen-

sions derived from ICT business processes: Plan,

Build, Run, Enable and Manage. InfoMINDS is de-

signed to be ﬂexible as each initiative is unique. Fig-

ure 1 presents InfoMINDS Framework. Next subsec-

tions present details on dimensions and processes. We

made InfoMINDS available at GitHub

to encourage

its usage in future studies.

3.1.1 Dimension A: Plan

Dimension A (Plan). includes processes that aim at

understanding and deﬁning the goals of end users and

the environment in which data science initiative will

take place. Table 1 presents the ﬁve processes in-

cluded in this dimension along with their major goals.

3.1.2 Dimension B: Build

Dimension B (Build). includes processes to select-

ing, preprocessing, transforming data and also model-

ing and evaluating data applications. Table 2 presents

the ﬁve processes included in this dimension along

with their major goals.

3.1.3 Dimension C: Run

Dimension C (Run). includes processes to deploy,

manage and monitor data application and the out-

comes generated by them. Table 3 presents the three

processes included in this dimension along with their

major goals.

https://github.com/tecladista1/InfoMINDS

InfoMINDS: An Interdisciplinary Framework for Leveraging Data Science upon Big Data in Surface Mining Industry

785

Table 1: Processes from Dimension A (Plan).

Process Major Goals

Acquire

Interdisciplinary

Team

To provide an interdisciplinary

team capable of executing all

required activities to achieve

results proposed by data sci-

ence initiatives. This process

focus on explanation of some

functions that need to be per-

formed so that a data science

initiative can be successful.

Understand

Business

Context

This process involves under-

standing the goals of the end-

user in terms of what is ex-

pected from data science ini-

tiative, considering all existing

constraints and requirements.

Business Process Modeling is

an approach that could be used

to facilitate this process.

Deﬁne

Appropriate

Paradigms

To choose appropriate

paradigms for conducting

a data science initiative con-

sidering the current scenario.

Depending on the expected re-

sults a different paradigm may

be chosen: agile, waterfall,

among others.

Deﬁne

Technological

Ecosystem

To deﬁne a technological

ecosystem to support a data

science initiative. Although

Big Data comprises technolo-

gies performing formal roles,

the technologies chosen to

perform each role may vary

among organizations or even

among initiatives.

Determine

Initiative

Readiness

To determine the maturity of

people, processes and tech-

nologies to conduct a data sci-

ence initiative upon big data.

This process intends to clar-

ify if all conditions are set to

starting a data science initia-

tive upon big data.

*Source: Authors.

3.1.4 Dimension D: Enable

Dimension D (Enable). includes processes to ad-

dress computing infrastructure, procurements, cy-

bersecurity and other enabling processes. Table 4

presents processes included in this dimension and

their goals.

Table 2: Processes from Dimension B (Build).

Process Major Goals

Perform Data

Selection

This process consists in iden-

tifying data sources, acquir-

ing, integrating and transfer-

ring data. Data need to be con-

sistently aggregated from dif-

ferent sources of information,

and integrated with other sys-

tems and platforms.

Perform Data

Preprocessing

Much of the raw data con-

tained in databases is un-

preprocessed, incomplete, and

noisy. The main goal of this

process is to treat outliers, in-

consistent values, missing val-

ues, redundant ﬁelds and obso-

lete ﬁelds.

Perform Data

Transformation

The main goal of this pro-

cess is to transform or consol-

idate data so that the resulting

data science processes may be

more efﬁcient. Data transfor-

mation comprehends transfor-

mation, dimension reduction

and discretization of data.

Perform Data

Modeling

The main goal of this pro-

cess is to create a model

based on initial hypothesis, ex-

ploratory data analysis, clas-

siﬁcation, clustering, among

others. Models can be equa-

tions linking quantities that we

can observe or measure. They

can also be a set of rules.

Perform Model

Evaluation

The main goal of this process

is to perform validation and

veriﬁcation tests of data appli-

cation. Model evaluation is the

process of assessing a prop-

erty or properties of a model

in terms of its structure and

data inputs so as to determine

whether or not the results can

be used in decision-making

*Source: Authors.

3.1.5 Dimension E: Manage

Dimension E (Manage)., includes processes to ad-

dress strategical processes, portfolio management,

risks, among others. Table 5 presents processes in-

cluded in this dimension and their goals.

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Table 3: Processes from Dimension C (Run).

Process Major Goals

Deploy Data

Application

The main goal of this process

is to deploy authorized version

of data application in a produc-

tion environment.

Manage Data

Application

The main goal of this process

is to manage data application,

including incidents, problems,

changes, among others.

Monitor Data

Application

Outcomes

The main goal of this process

is to capture outcomes pro-

vided by data application.

*Source: Authors.

Table 4: Processes from Dimension D (Enable).

Process Major Goals

Develop and

Manage Team

To manage and develop team

allocated to a particular data

science initiative.

Deploy and

Improve

Computing

Infrastructure

To deploy technologies for

data creation, acquisition,

transmission, ingestion, stor-

age, pre-processing, data

modeling, among others.

Manage and

Control

Procurements

To provide contracts with ex-

ternal vendors and partners.

Implement and

Monitor Data

Governance

To implement and monitor the

maturity level of data gover-

nance practices.

Implement and

Monitor

Information

Security

To implement and monitor

policies and routines to pre-

vent or mitigate risks related to

information security.

Manage and

Control Budget

To manage and control eco-

nomic and ﬁnancial budgets.

*Source: Authors.

3.2 InfoMINDS Application

In this section we present how we applied Info-

MINDS Framework to data science initiatives upon

big data on the mining industry. We used InfoMINDS

to organize activities of four data science initiatives:

two initiatives focused on operations and two fo-

cused on maintenance. Initiatives followed the se-

quence indicated by InfoMINDS Dimensions: Plan,

Build, Run, Enable and Manage. To ensure correct-

ness and completeness of this stage, we used On-

toDIVE ontology, presented in (Pinto and Parreiras,

2020) to represent all elements involved on initiatives.

Table 5: Processes from Dimension E (Manage).

Process Major Goals

Provide

Strategy

Alignment

To share business strategy

with all data science initiatives

upon big data.

Manage

Overall

Portfolio

To manage overall portfolio of

either projects or services..

Manage and

Control Risks

To identify, assess, manage

and control risks.

Manage and

Control

Resources

To manage and control non-

ﬁnancial resources available

for all initiatives.

Manage

Beneﬁts

Realization

To manage beneﬁts realization

for all the initiatives.

Knowledge and

Information

To share knowledge and infor-

mation about previous initia-

tives.

*Source: Authors.

3.2.1 Implementation of Dimension A: Plan

Following guidelines of Processes A.1 Acquire In-

terdisciplinary Team, A.2 Understand Business

Context, A.3 Deﬁne Appropriate Paradigm, A.4

Deﬁne Technological Ecosystem and A.5 Deter-

mine Initiative Readiness, interdisciplinary teams

were allocated to each initiative. Next, each squad

was assigned to a real business problem and teams

chose the most appropriate paradigm for the initiative

under their responsibility. Next, teams searched On-

toDIVE Ontology to ﬁnd existing technologies pre-

viously implemented by mining processes specialists.

At the end, teams decided to move all initiatives to the

next stage after considering their readiness.

3.2.2 Implementation of Dimension B: Build

Following guidelines of Processes B.1 Perform Data

Selection, B.2 Perform Data Preprocessing, B.3

Perform Data Transformation, B.4 Perform Data

Modeling and B.5 Perform Model Evaluation, all

teams performed activities to select, preprocess and

transform data from available data sources. Next,

data models were created and evaluated. Actions per-

formed in this stage were different as each initiative

had a different starting point. While some initiatives

were focused on rolling out existing applications to

a different location, other initiatives had to build pre-

dictive models and applications from the scratch.

InfoMINDS: An Interdisciplinary Framework for Leveraging Data Science upon Big Data in Surface Mining Industry

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3.2.3 Implementation of Dimension C: Run

Following guidelines of Processes C.1 Deploy Data

Application, C.2 Manage Data Application, C.3

Monitor Data Application Outcomes, all teams per-

formed activities to deploy and manage data appli-

cations besides monitoring outcomes of these data

applications. We created individuals on Class Out-

comes to represent data applications deployed in this

stage. These data applications were linked to roles in

the big data technological ecosystem through Object

Property haveRole.

3.2.4 Implementation of Dimension D: Enable

Following guidelines of Processes D.1 Develop and

Manage Team, D.2 Deploy and Improve Comput-

ing Infrastructure, D.3 Manage and Control Pro-

curements, D.4 Implement and Monitor Data Gov-

ernance, D.5 Implement and Monitor Informa-

tion Security and D.6 Manage and Control Budget,

team “TE00 – Squad Shared Services” oversaw peo-

ple development, management of computing infras-

tructure, management of budget and procurements

and monitoring of data governance and information

security. Initiatives started exchanging ﬁles and as

the time went on, improvements were made to enable

real-time acquisition of data.

3.2.5 Implementation of Dimension E: Manage

Following guidelines of Processes E.1 Provide Strat-

egy Alignment, E.2 Manage Overall Portfolio, E.3

Manage and Control Risks, E.4 Manage and Con-

trol Resources, E.5 Manage Beneﬁts Realization

and E.6 Share Knowledge and Information, this di-

mension comprehends processes to either start or ﬁn-

ish initiatives. To start the initiatives analyzed by this

study, ﬁrst they were authorized. Then, all ﬁnancial

and non-ﬁnancial resources were made available. We

used object property supportedBy to link initiatives

to people, processes and technologies.

3.3 InfoMINDS Examples

This section presents outcomes generated by initia-

tives analyzed in this paper. Examples of this sec-

tion were included to illustrate the potential of Info-

MINDS Framework.

3.3.1 Data Science for Mining Operations

This initiative was created to improve energy efﬁ-

ciency in mining operations, by reducing fuel con-

sumption. In practical terms, this initiative intended

to address the following business problem: In what

extension the fuel consumption on coal mining opera-

tions is affected by other variables?. Table 6 presents

outcomes of this initiative in order to illustrate Info-

MINDS capabilities.

3.3.2 Data Science for Mineral Processing

Mineral processing includes size reduction and en-

richment of minerals. This initiative was created

to improve coal marketability, by ensuring values of

yield above 30% and ash below 11.2%. In practical

terms, this initiative intended to address the following

business problem: In what extension ash and yield

are affected by other variables of coal mineral pro-

cessing?. Table 7 presents outcomes of this initiative

in order to illustrate InfoMINDS capabilities.

3.3.3 Data Science for Mining Maintenance

Industrial maintenance intends to guarantee availabil-

ity and reliability for facilities and equipment. This

initiative was created to improve asset management,

by extending lifetime of mining trucks. In practical

terms, this initiative intended to address the follow-

ing question: In what extension the lifetime of mining

trucks are affected by other process variables?. Ta-

ble 8 presents outcomes of this initiative in order to

illustrate InfoMINDS capabilities.

3.3.4 Data Science for Plant Maintenance

Plant maintenance seeks for optimum availability,

optimum operating conditions, maximum utiliza-

tion of maintenance resources, optimum equipment

life, minimum spares inventory and ability to react

quickly. This initiative was created to improve asset

management, by reducing tearing on conveyor belts.

In practical terms, this initiative intended to address

the following question: In what extension tearing of

conveyor belts are related to other process variables?.

Table 9 presents outcomes of this initiative in order to

illustrate InfoMINDS capabilities.

4 DISCUSSION

InfoMINDS is a conceptual framework that orga-

nizes practices commonly applied to design, build

and maintain data applications considering a compre-

hensive and end-to-end perspective. InfoMINDS cre-

ates a common vocabulary allowing processes for de-

velopment and maintenance to be reused and shared

amongst industries from different segments.

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E - Manage

D - Enable

Develop and Manage Team

D.1

Implement and Monitor

Data Governance

D.4

Deploy and Improve

Computing Infrastructure

D.2

Implement and Monitor

Information Security

D.5

Manage and Control Procurements

D.3

Manage and Control Budget

D.6

Provide Strategy Alignment

E.1

Manage and Control Resources

E.4

Manage Overall Portfolio

E.2

Manage Benefits Realization

E.5

Manage and Control Risks

E.3

Share Knowledge and Information

E.6

A - Plan

Acquire Interdisciplinary Team

A.1

Define Technological Ecosystem

A.4

Understand Business Context

A.2

Define Appropriate Paradigms

A.3

B - Build

Perform Data Selection

B.1

Perform Data Modeling

B.4

Perform Data Preprocessing

B.2

Perform Data Transformation

B.3

C - Run

Deploy Data Application

C.1

Manage Data Application

C.2

Monitor Data Application Outcomes

C.3

Perform Model Evaluation

B.5

Determine Initiative Readiness

A.5

Figure 1: InfoMINDS: Framework Overview.

*Source: Authors.

Table 6: Examples of Data Science for Mining Operations.

Predictive

Application

A predictive application was built to identify processes variables that could inﬂuence fuel

consumption based on routes, conditions of roads and inclination angles of equipments.

Prescriptive

Application

A prescriptive application was built and deployed into the ﬂeet management system which

is used by operators to receive information from dispatch controllers.

Descriptive

Application

A descriptive application was built and deployed to facilitate ﬁnding deviations to recom-

mendations of predictive application.

*Source: Authors.

InfoMINDS may contribute either to clariﬁcation of

concepts or to the explanation of interactions between

and among them. It can also be considered as a foun-

dation upon which mining companies can build poli-

cies, standards, rules, procedures, methodologies or

any other artifact in order to leverage data science ini-

tiatives upon big data.

InfoMINDS allows professionals from any knowl-

edge area to understand major processes and activi-

ties related to data science initiatives upon big data

as well as the processes and activities related to the

management, maintenance and support of data appli-

cations. All initiatives analyzed in this study were led

by personnel with no previous knowledge about big

data technologies or data science management.

InfoMINDS contributes either to clariﬁcation of

concepts or to the explanation of interactions between

and among them. Mining industry can beneﬁt from

InfoMINDS as transaction technologies, previously

implemented by mining processes specialists, gen-

erate large volumes of scattered data and integrated

analyses of those data may be used as a tool for im-

proving operational efﬁciency of the industry. Info-

MINDS takes all existing technologies into consid-

eration as they have the potential to be used as data

source for data science initiatives. Besides that, Info-

MINDS helps mining industry to share best practices

between different sites. Data applications showed in

this paper could be rolled out to similar operations,

leveraging data science upon big data.

5 CONCLUSION

InfoMINDS framework is a conceptual framework

designed to create common ground so that data sci-

ence initiatives upon big data can be fully understood

by professionals from any area of knowledge. It has

twenty-ﬁve processes grouped into ﬁve dimensions

and contributes to leveraging data science initiatives

InfoMINDS: An Interdisciplinary Framework for Leveraging Data Science upon Big Data in Surface Mining Industry

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Table 7: Examples of Data Science for Mineral Processing.

Predictive

Application

A predictive application was built and deployed to identify processes variables that could

inﬂuence results of ash and yield. This application was designed to identify the physical

type of coal being processed and automatically suggest parameters so that results of ash and

yield could be inside an expected range.

Prescriptive

Application

A prescriptive application was built and deployed into the system used by operators to de-

termine setups for processes variables. This prescriptive application brought tangible results

as operators were able to see the distance between their setups and the optimal ranges.

Descriptive

Application

A descriptive application was built and deployed to facilitate ﬁnding deviations to recom-

mendations of predictive application. This application was used to monitor adherence by

operator, by shift, and so on.

*Source: Authors.

Table 8: Examples of Data Science for Mining Maintenance.

Predictive

Application

A predictive application was built and deployed to identify variables that could inﬂuence

lifetime of mining trucks. This application analyzes fueling data, haul truck telemetry,

engineering parameters and laboratory results to recommend the sequence of scheduled

maintenance.

Prescriptive

Application

A prescriptive application was built and deployed into the maintenance workshop. This

application is focused on presenting trucks with lifetime lower than expected and suggests

the components to be replaced to extend the lifetime of equipment.

Descriptive

Application

A descriptive application was built and deployed in order to facilitate ﬁnding deviations to

recommendations of predictive application. This application is used to monitor components

suggested to be replaced.

*Source: Authors.

Table 9: Examples of Data Science for Plant Maintenance.

Predictive

Application

A predictive application was built and deployed to identify variables that could quickly

detect tearing of conveyor belts. After analyzing different process variables, a single process

variable was identiﬁed as capable of indicating the start of a tearing event.

Prescriptive

Application

A prescriptive application was built and deployed into the plant controller to automatically

stop conveyor belts whenever requested by predictive application.

*Source: Authors.

upon big data in mining industry. It can be considered

as a foundation upon which mining companies can

build policies, standards, rules, procedures, method-

ologies or any other artifact, in order to leverage data

science. It also may help mining industry profession-

als to draw parallels between data science results for

a different domain to their own domain.

InfoMINDS conﬁrmed its capability of explaining

interactions between people, processes and technolo-

gies in the context of data science upon big data on

mining industry. The framework conﬁrmed its con-

tribution to the clariﬁcation of concepts and termi-

nologies related to either data science or big data in

mining industry. In this study, all initiatives were led

by personnel with no previous knowledge about data

science. Still, consistent data science results were

achieved. InfoMINDS showed it can enlarge possi-

bilities of data science applications.

This study has several limitations. Firstly, In-

foMINDS is based on OntoDIVE ontology and in-

herits its limitations. Besides that, InfoMINDS was

conceived as a conceptual framework. Assuming

that different researchers may approach a single phe-

nomenon using different perspectives, it is possi-

ble that they might end up with different conceptual

frameworks as ﬁnal result. Another limitation is the

fact InfoMINDS was applied in data science initia-

tives of a single mining company. Future works could

apply InfoMINDS on more real-case scenarios to col-

lect insights and thoughts of more people. Future

works could also implement a system based on OWL

ﬁle generated by Proteg

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