=Paper=
{{Paper
|id=Vol-2608/paper57
|storemode=property
|title=Intelligent data analysis in hr process management
|pdfUrl=https://ceur-ws.org/Vol-2608/paper57.pdf
|volume=Vol-2608
|authors=Mykola Ivanov,Nataliia Maksyshko,Sergey Ivanov,Nataliia Terentieva
|dblpUrl=https://dblp.org/rec/conf/cmis/IvanovMIT20
}}
==Intelligent data analysis in hr process management==
https://ceur-ws.org/Vol-2608/paper57.pdf
Intelligent Data Analysis in HR Process Management
Mykola Ivanov 1[0000-0002-1908-0763], Nataliia Maksyshko 1[0000-0002-0473-7195],
Sergey Ivanov 1[0000-0003-1086-0701] and Nataliia Terentieva 1[0000-0001-6930-879X]
1
Zaporizhzhya National University, Zhukovsky str., 66, Zaporizhzhia, 69063, Ukraine
nn_iva@ukr.net, maxishko@ukr.net, flydaiver@gmail.com,
terenteva_nataliya@ukr.net
Abstract. This article discusses the problem of constructing a method of data
mining in HR process management and modeling the assessment of the degree
of personnel efficiency. A method of data mining in the management of HR
processes in the form of an algorithm with a fuzzy inference procedure imple-
mented in the MATLAB R2017a system is proposed, which made it possible to
assess the degree of personnel work efficiency.
This method and procedures of data mining allows you to simulate multidimen-
sional HR processes.
The application of the method and procedures of data mining in HR-process
management allows us to develop problem solving approaches.
Keywords: intelligent data analysis, HR management systems, fuzzy sets,
fuzzy inference procedure, fuzzy modeling, procedure
1 Introduction
Today, business is forced to solve a whole range of complex and unique tasks. So, to
solve the problems of increasing and stabilizing the management of economic facili-
ties in modern conditions, new approaches and solutions are required, which deter-
mined the emergence of a new concept of the German economist Klaus Schwab, Pres-
ident of the World Economic Forum in Davos [1]. According to this concept, it is
stated that we live in the era of the fourth industrial revolution, where the virtual
world is combined with the physical world using information technology. The fourth
industrial revolution is characterized by a change in economic relations and the wide-
spread use of intelligent technologies (Cloud technologies, Big Data, artificial neural
networks and fuzzy sets, data mining and others), which is the basis of the digital
economy.
It should be noted that there are various approaches to the determination and meas-
urement of the dynamics of the digital economy, and it is also difficult to assess its
volume. According to estimates [2], the share of the digital segment of the global
economy in 2020 could be 23% ($ 17 trillion). This leads to the expansion and rapid
development of the Internet market using Big Data, followed by modeling by fuzzy
logic approaches.
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
2 Formal problem statement
Therefore for successful management of economic facilities in the digital economy an
important role is given to HR management systems (Human resources) which are able
to offer a radically new mechanism that allows companies to remain competitive in
the market.
3 Literature review
The first direction is determined by the level of application of intelligent systems.
High information technologies embrace the world and replace the classical methods
of HR-process management. Robot programs are now being applied, offering em-
ployees of the enterprise to undergo an express interview or interview using yet expert
systems.
The next direction is the analysis and evaluation of young professionals who are
able to learn and solve modern problems. Now, new specialists up to 25 years of age
who have completely different knowledge, interests and fundamentally different ideas
about modern work are entering the labor market. Young specialists are able to quick-
ly make managerial decisions and push new projects forward, and thereby increase
the level of work in the enterprise.
In addition to these areas, it should be noted that for a long time HR management
was aimed at standardization and versatility. However, this approach is gradually
becoming obsolete today. This approach is being replaced by HR management meth-
ods that are focused on the maximum use of the intellectual capital of employees.
This is stimulated with the simultaneous satisfaction of individual needs, desires,
employee capabilities and their synchronization with the tasks of the enterprise. Mod-
ern HR specialists are beginning to more closely monitor the development of employ-
ees within the enterprise, which allows flexible management of career growth, which
can be adjusted taking into account the proposals of the employees themselves.
HR strategy is part of the overall strategy of enterprises and long-term planning of
their business activities. An important role in these plans is played by assessments of
the degree of personnel efficiency as a factor in updating and increasing production
efficiency in the overall economic strategy of an enterprise. The creation of modern
catalogs of employee data and their management requires the processing of a large
amount of information. This is due to a wide range of organizational, economic and
technical and technological tasks that are performed by personnel. Therefore, data
analysis in the management of HR processes is an urgent task.
The influence of information technology in the management of economic systems
was presented in the work of M. Ivanov [3]. However, the problems of rating man-
agement have not been resolved. The solution to the problem of rating management
was the work of Yu. Lysenko, V. Petrenko L. [4]. However, the level of staff devel-
opment and their assessment were not considered. This work was devoted to the solu-
tion of this problem, in which the theoretical aspects of personnel development are
studied, in particular, the concept, main tasks and directions of personnel develop-
ment at the enterprise. V. Helman [5] considers the development of enterprise person-
nel as a change in its qualitative characteristics, in which indicators in the form of
degree of activity were proposed.
Human resource management as a strategic human resource management today is
seen as going beyond the boundaries of management tasks, such as motivation, the
level of remuneration. Instead, managers need to consider HR management as a proc-
ess that contributes to the success of the enterprise. Therefore, the work of Brian E.
Becker [6] considers approaches where all managers should be involved in the man-
agement process, where the role of employees is important for the competitive advan-
tage of the enterprise. In addition, organizations that value their skilled employees are
more profitable than those that do not. The results of the works ща scientists Mark A.
Huselid [7], Jeffrey Pfeffer and John F. Veiga [8] show that successful enterprises
have several common characteristics: stable job security, the use of self-government
methods, perfect pay and access to information. These tasks were considered in the
work of Brian E. Becker, Mark A. Huselid [9], when an enterprise develops and mo-
tivates the development of human capital. The most successful enterprises manage
HR as a strategic asset, and evaluate the effectiveness of labor resources in terms of
its impact. Dennis R. Briscoe [10] in his work write about situation when each em-
ployee of the enterprise effectively fulfills his duties and creates a high-performance
work system in which the employee has the maximum involvement and responsibil-
ity.
In modern enterprises, an important task is to ensure a balance between the need
for coordination and synchronization of units located both in different cities and
around the world, which was reviewed by Randall S. Schuler, Pawan S. Budhwar, and
Gary W. Florkowski [11]. Achieving this balance is becoming increasingly difficult
as the level of functional diversity to which enterprises are exposed increases.
Today, new intelligent technologies are emerging to solve these problems. Such in-
telligent technologies include research and project management recommendations.
Such works include the works of S. Gottwald [12] and N.Vedula, P. Nicholson [13].
These projects on improvement of resource processes are the ones of the most im-
portant tasks of production in enterprise management. Currently, the number of works
in this direction is increasing by the authors W. Siler, J. Buckley [14], J. Waitelonis,
C. Exeler, H. Sack[15] and A. Abraham[16]. The obvious advantage of the proposed
methods is the use of mathematical tools, the theory of fuzzy sets, which allows im-
plementing the process of selecting conditions, the introduction of the method in the
managerial processes of the enterprise. It should be noted that this works did not solve
the problem of data mining in HR-process management with the possibility of model-
ing the degree of personnel efficiency, which determined the topic and relevance of
this article.
4 The purpose of the article
The article is devoted to development of a sequence of procedures for data mining in
human resource management using fuzzy logic approaches.
5 Data Mining method in HR process management
The task of data mining in the management of HR processes is the effective extraction
and analysis of the existing data array of employees with subsequent personnel man-
agement using cloud solutions. This will allow the rapid implementation of a new
personnel management system, obtaining a new level of accessibility and increasing
its mobility.
The resulting performance indicators of personnel at the enterprise can be repre-
sented in the form of multidimensional structures, where each measurement is repre-
sented by the corresponding indicators of the enterprise management system.
The following sequence diagram (the method) of data analysis in HR process man-
agement is proposed, which is presented in Fig. 1.
Fig. 1. Sequential data mining diagram in HR process management.
The proposed method includes four stages.
5.1 Stage 1 Procedures
The first stage solves the problem of choosing the analyzed indicators. For this, a lot
of ratings are determined:
P Pi (op1, op 2, ep1, ep 2, ep3), i 1, N , (1)
where op1 is a generalized indicator of job compliance, characterizing the degree of
conformity of qualifications and work experience of the post, level of responsibility,
as well as the quality of the performance of current work and duties,
op 2 - a generalized indicator of diligence, characterizing the effectiveness of the
tasks (complexity, quality, timeliness),
ep1 - ambitiousness, a single indicator of personality characteristics,
ep 2 - the quality of a leader, an indicator of personality characteristics,
ep3 - the level of attitude in the team, a single indicator of personality characteris-
tics.
5.2 Stage 2 Procedures
At the second stage, the initial information is determined, which is necessary for cal-
culating indicators based on expert assessments, analytical indicators (for example,
work experience, quality of work performed, and others).
To describe the formalized set of sets of source information, we introduce the
rules, namely, if the set P Pi (op1, op 2, op3) , is defined, then to use the value of
the component op 02 of unit level 0, we will use the notation op 02 (join operator).
At the second stage, procedures are applied that allow:
The first (I) procedure allows you to evaluate the regulatory or average value of the
performance of official duties by employees – P :
P Pi (op01, op02, op03) , (2)
where op 01 - normative or average value of the job performance of the i -th em-
ployee,
op 02 - normative or average value of the level of assessment of the performance of
tasks of the i -th employee.
The second (II) procedure is aimed at identifying many specialties (economist,
programmer, builder and others) – SP :
SP spr , r 1, LSP , (3)
where spr is the r - th specialty,
LSP - is the number of specialties.
The third procedure allows you to assess the level of education (secondary, bache-
lor, master and others) - UO :
UO uoV (name, ), r 1, LOV , (4)
where uoV - is the vector of characteristics of the v -th category,
name - category name,
- assessment of the level of education for the category in points,
LOV - is the number of categories.
The fourth (IV) procedure is aimed at assessing the level of enterprise management
(higher, middle and lower level) - UD :
UD udV (name, ), r 1, LUD , (5)
where udV - is the vector of characteristics of the V -th level,
name - level name,
- is an estimate of the level in points,
LUD - the number of levels, which is determined by the scale of the enterprise.
The fifth (V) procedure solves the problem of describing many positions in the en-
terprise - D :
D d j (name, ud , uo, kl , SPD ) : ud UD, uo UO , j 1, Ld , (6)
SPD spd r ( sp, ) : sp SP, 0 1, r 1, LSPD
j , (7)
where d j is the j -th position,
name - job title,
ud - position level in the organizational and staff structure of the enterprise,
uo - the level of education required for the j -th position,
kl - required work experience (minimum number of years) in a given position for
an optimal qualification level,
SPD - many specialties related to this position,
spd r - is the vector of the correspondence characteristics of the r -th specialty of
the j -th position ,
- is the correspondence coefficient of the specialty sp of the j -th position,
Ld - is the number of posts,
LSPD
j - number of specialties in the j -th position.
The sixth (VI) procedure solves the tasks of describing correspondence and job in-
terchangeability - SD :
SD sd f ( d1, d 2, ) : d1 D, d 2 D, 0 1, ( d1 d 2) 1 , f 1, LSD , (8)
LSD ( Ld ) 2 , (9)
where sd f - is the f -th vector of job matching characteristics d1 and d 2 ,
- is the compliance coefficient.
The seventh (VII) procedure is aimed at assessing additional characteristics of em-
ployees - A:
A ai (ds, ST , OB) : ds D, uo UO, i 1, N , (10)
ST st j (d , kl ) : d D, d UO , j 1, LST
i , (11)
OB obw ( sp, uo, god ) : sp SP, uo UO, w 1, LOB
i , (12)
where ai - is the vector of characteristics of the i -th employee,
ds - the position held by the employee,
ST - many posts in which the employee previously worked and experience in
them,
st j - vector of characteristics of work experience in previous positions,
kl - length of service (number of years) in the position d ,
OB - value, reflects the education received by the i -th employee; sp - specialty;
uo - level of education,
god - year of receipt of the qualification document (certificate, certificate, diploma
and others),
LST
i - the number of posts previously held by the i -th employee,
LOB
i - the number of specialties in which the employee was educated by the i -th
employee.
The eight (VIII) procedure allows you to describe many additional tasks (deter-
mined by orders) and their characteristics in the enterprise:
Z zk (t 0, tk , tk , usz ), 0 1, k 1, M , (13)
where z k - is the vector of characteristics of the k -th task,
t 0 & tk - the value of the beginning and end of tasks, determines the term for
completing the task in units of measurement of working time (for example, working
days, hours and others),
tk ' - the value of time, determines the critical deadline for completing the task, af-
ter which the task is either canceled or transferred to another performer,
usz - task difficulty level,
M - is the number of tasks.
The set of completing additional IZ tasks by employees can be written as follows:
IZ iz k (a, z , uv p , uv) : a A, z Z , 0 uv p 200 , k 1, M , (14)
where iz k - is the characteristic of the k -th job,
a - an employee who performs additional tasks,
iz - the task
uv p - is the percentage of the task according to the plan at the current time t
( uv p 0 at time t0, uv p 200 at time k ),
uv - is the percentage of the task at the current time t .
In case of failure to perform additional tasks, the value of IZ 0 .
5.3 Stage 3 Procedures
At the third stage, the procedure for assessing the conformity of the specialty of the
position is performed. The function f returns the value of the correspondence of the
specialty xsp to the position xd :
d .spd i 0 . , j0 r0 : (d j 0 xd ) (d j 0 .spd r 0 .sp )
f ( xsp, xd ) j 0 . (15)
0, j0 r0 : (d j 0 xd ) (d j 0 .spd r 0 .sp )
The function f returns the value of the coefficient of correspondence and inter-
changeability of the post xsp post xd :
d .spd g . , g 0 : ( sd g 0 .d1) ( sd g 0 .d 2 xd )
f ( xsp, xd ) i . (16)
0, g 0 : ( sd g 0 .d1) ( sd g 0 .d 2 xd )
To determine job conformity is the level of education of the position held in con-
junction with work experience in similar or related positions:
pi .op1 (op11.op12).op13 , (17)
Lob
i ai .obw .god
op11 f (ai .obw sp. ai .ds ) , (18)
i 1 godT
Lsti ai .st j kl
op12 f (ai .ds. f . ai .st / d ) , (19)
i 1 ai .ds.kl
where godT - is the value of the current year,
op11 - qualification level of education received,
op12 - qualification level, which is determined by work experience,
op13 - quality of job performance, determined by an expert.
When solving the problem of data mining in the management of HR processes,
fuzzy logic methods are used to display the result on the interval [0; 1].
5.4 Stage 4 Procedures
Therefore, at the fourth stage, the procedure for constructing membership functions
based on the theory of fuzzy sets is performed.
The following “position”, “level”, “education” can be attributed to numerical lin-
guistic variables of employees, and “conflict”, “level of substitution” to linguistic
variables. Numerical linguistic variables and their meanings serve for a qualitative
description of a quantitative quantity. The values of linguistic variables are deter-
mined by experts.
It should be noted that a linguistic variable, like its original term set, is associated
with a specific dimensional scale on which all arithmetic operations are defined.
To assess the characteristics of employees in Table 1, linguistic variables and their
dimensions are proposed.
Table 1. Linguistic variables of employee characteristics
1 1 The term
Term set The metric and type of exposure X min X max
designation
Performance of duties - 0 1 Not per-
stimulation formed
T11 Partially
completed
Performed
Job Interchangeability , Average
discouragement High
Level of education , 1 3,0 Secondary
stimz,ulation education
T31 Bachelor
Master
Conflict ,stimulation 0 3,0 Low
T41 Average
High
The importance level of the specialty 0 0,5 Low
T51 < SpecialtyLevel >, Average
discouragement High
The use of the concept of stimulation and destimulation is applied taking into ac-
count the influence on the degree of personnel efficiency, namely, stimulation - the
effect on the increase and destimulation - on the reduction of the factor.
Therefore, the term set Ti n Ti n ' is associated with the set Ti n ' , where
Ti n ' x, T n ' ( x) x xmin , xmax - is a fuzzy number, i 1, m , m - is the number of
i
term sets, n - is the number of employees.
To eliminate the influence of changes in the input variables of the metrics and, as a
consequence, the correction of term sets, a transition to a normalized function is pro-
posed. Let the previously defined term set Ti be the original one.
The normalized linguistic variable is a mapping on the interval [0; 1]:
Din Din ' , Din ' z , D ' ( z ) z 0,1 ,
i
(20)
where z is a fuzzy number corresponding to the term set Di' on the interval [0; 1], n -
is the number of employees.
These functions allow you to display heterogeneous input variables in a single
normalized interval [0;1], which allows you to reduce errors associated with different
quantities and their dimensions. This provides a convenient representation of the val-
ues, as well as their interpretation.
6 The method of constructing a model of data mining in HR
process management
The structural model of data mining in HR process management is presented in Fig. 2.
Fig. 2. The structural model of the model of data mining in the management of HR-process.
In the structural model, T Ti is a term set, where i 1, n , n - is the number of
sets, each of which is represented by a fuzzy variable with a domain of definition x .
The process of modeling fuzzy values is based on a fuzzy inference system, which
allows you to convert expert estimates into fuzzy values.
In the fuzzy inference system, the procedure for finding a clear value for each of
the input linguistic variables based on defuzzification is applied. Defuzzification in a
fuzzy inference system is the process of finding a value for each of the output linguis-
tic variables of the set W x1 , x2 , .., xn . The task of defuzzification is to use the
results of accumulation of all output linguistic variables. It is necessary to obtain a
quantitative value of each of the output variables. Output variables can be used in a
fuzzy inference system relative to the input linguistic variable.
Accumulation of fuzzy inference is the process of finding the membership function
for each of the output linguistic variables of the set.
The transformation of a fuzzy set into list of values of variables is named as de-
fuzzification.
The defuzzification procedure is performed by a sequence that considers each of
the output linguistic variables and the fuzzy set Ti Ti j related to it. The result
of defuzzification for the output linguistic variable is defined as a quantitative value.
The defuzzification process is considered complete when quantitative values are
determined for each of the output linguistic variables. For the fuzzy inference system,
the Mamdani algorithm was applied [17].
The Mamdani algorithm includes the following steps [18, 19]:
the formation of a rule base for fuzzy inference systems;
fuzzification of input variables;
aggregation of conditions in fuzzy rules to find the degree of truth of the conditions
of each of the rules of fuzzy logic;
accumulation of conclusions of fuzzy production rules;
defuzzification of output variables based on the center of gravity method.
An example of one of 234 rules has the following form:
If (Position is Npfd) and (InterchangeabilityLevel is
low) and (Education is secondary) and (Conflict is low)
and (SpecialtyLevel is low) then (StaffEfficiency is
NotHardworking).
Depending on the nature of the domain X, numerical linguistic variables can be de-
fined.
The values of linguistic variables are determined on an ordinal scale. It should be
noted that a linguistic variable, like its original term set, is associated with a specific
scale on which all arithmetic operations are defined. Therefore, the term set Ti Ti j
is associated with the set Ti , where Ti x, T j ( x) x xmin , xmax ; i 1, n ; j 1, m ;
j j
i
n - is the number of term sets, m is the number of terms.
A model that satisfies these fuzzy sets is their union:
Ti sup T j ( x) , Ti Ti j .
i
(21)
We construct membership functions for the linguistic variable characteristics of em-
ployees, presented in Table 1.
The process of converting experts' qualitative assessments into fuzzy quantities
consists in mapping the elements of the original term set in the form of constructing
membership functions of fuzzy quantities Ti j Ti .
The description of linguistic variables is as follows:
position, {not fulfilled, partially fulfilled, fulfilled}, [0; 1],
level of interchangeability, {low, medium, high}, [1; 3],
education, {secondary, bachelor, master}, [1; 3],
conflict, {low, medium, high}, [1; 3].
Moreover, the values of the sets are in the range [0; 1] & [1; 3].
7 Experiments and results
The use of trapezoidal membership functions is due to the fact that the calculation
uses the definition of fuzzy numbers. This form of analytical approximation use (LR)
- functions, which include the trapezoid functions. From this it follows that fuzzy
numbers are determined to perform operations with similar procedures.
The constructed membership functions of the input linguistic variables are pre-
sented in Fig. 3.
a) b)
Fig. 3. The membership function of the input linguistic variables: a) “Fulfillment of duties”, b)
“Interchangeability of posts”.
To assess the degree of personnel efficiency, developed knowledge bases are applied,
which are presented in the form of a rule base.
In the fuzzy inference procedure for managing HR processes, it is necessary to
consider the work of employees at all levels of work. The fuzzy inference procedure
is implemented in the MATLAB R2017a system, which allowed obtaining the follow-
ing results of assessing the degree of personnel efficiency.
To perform the procedure, we built the diligence function of the output linguistic
variable “assessment of the degree of personnel efficiency”, which is presented in
Fig. 4.
Fig. 4. The membership function of the output linguistic variable "assessment of the degree of
personnel efficiency".
The simulation results of assessing the degree of personnel efficiency, which is pre-
sented in Fig. 5.
a)
b)
Fig. 5. Modeling the assessment of the degree of personnel efficiency: a) level of importance of
the specialty(SpecialtyLevel) and education, b) performance of duties (Position) and education.
The results of modeling the potential effectiveness of the staff showed that five sets:
responsibilities, job experience, duration of continuous work per day, job inter-
changeability, conflict, the level of importance of the specialty and the level of educa-
tion have an intersection ( 5i 1 Ti1 Ø ). The value of the potential efficiency of the
employee’s staff is equal to the intersection of sets and it is 0.65, which corresponds
to a high score (Fig. 5).
The result can be used by business entities to improve the efficiency of staff and in
the selection of personnel.
8 Conclusion
The authors of the article propose a solution for constructing a sequence of data min-
ing of human resources and modeling the degree of personnel efficiency based on
fuzzy sets.
The proposed data analysis method involves four steps. At the first stage, the prob-
lem of choosing the analyzed indicators is solved. At the second stage, eight proce-
dures are performed to solve the following problems: to assess the normative or aver-
age values of labor productivity, to determine many specialties, to assess the level of
education, to evaluate the levels of enterprise management, to describe a lot of posts,
to describe correspondence and job interchangeability, to evaluate additional charac-
teristics of employees, for descriptions of many additional tasks and their characteris-
tics. At the third stage, the procedure for assessing the conformity of the specialty of
the post is carried out. At the fourth stage, the procedure for constructing membership
functions based on the theory of fuzzy sets is performed. The fuzzy inference proce-
dure is implemented in the MATLAB R2017a system, which makes it possible to
assess the degree of personnel efficiency.
Prospects for the application of the proposed method and procedures for data min-
ing of human resources lie in the possibility of using large data warehouses and cloud
computing.
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