P. Patricio and V. Velepucha,

Data Domain Servitization for Microservices Architecture,

Latin-American Journal of Computing (LAJC), vol. 12, no. 1, 2025.

Data Domain

Servitization for

Microservices

Architecture

ARTICLE HISTORY

Received 18 September 2024

Accepted 23 October 2024

Patricio Michael Paccha Angamarca

Salesian Polytechnic University

Cuenca, Ecuador

ppaccha@est.ups.edu.ec

ORCID: 0000-0001-6285-7390

Victor Vicente Velepucha Bonett

National Polytechnic School

Quito, Ecuador

victor.velepucha@epn.edu.ec

ORCID: 0000-0002-7335-2571

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14449867

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

Data Domain Servitization for Microservices

Architecture

Patricio Michael Paccha Angamarca

Salesian Polytechnic University

Cuenca, Ecuador

ppaccha@est.ups.edu.ec

ORCID: 0000-0001-6285-7390

Victor Vicente Velepucha Bonett

National Polytechnic School

Quito, Ecuador

victor.velepucha@epn.edu.ec

ORCID: 0000-0002-7335-2571

Abstract— Microservices have emerged as a software design

paradigm where small, autonomous services interact to meet

business requirements. However, transitioning from monolithic

systems to microservices presents challenges, especially when

multiple subdomains share transactional tables to maintain

referential integrity across separate databases. Ensuring each

microservice handles business data while adhering to ACID

properties—namely, atomicity, consistency, isolation, and

durability—is crucial. This requires unique, consistent, and low-

dependency data from a business domain perspective.

Systematic Literature Review serves as a secondary research

method aimed at evaluating the existing body of scientific literature.

It helps identify existing work, highlight research gaps, and propose

new research directions. In software engineering, SLRs offer a

comprehensive overview of studied research areas.

This article reports an empirical study based on a systematic

literature review aimed at identifying modeling techniques for

segmenting data structures during microservice design. The review

found limited methods to address the appropriate level of data

granularity per microservice. These findings highlight a need for

further research into processes and methodologies that can

effectively handle data segmentation and consistency within

microservice architectures.

Keywords— Servitization, Granularity, Data Segmentation,

Microservices, Data Architecture, Microservices Architecture

I. INTRODUCTION

Microservices architecture is a software architecture style

focused on using small, lightweight services designed to adapt

to dynamic business environments [1]. Each microservice

results from decomposing monolithic systems or is modeled

as an independent component from development to

deployment. This approach enables the scalability and

evolution of each service with autonomy over its data stores.

A microservices ecosystem manages its governance by

delegating business responsibilities to each service, with

boundaries defined by subdomains that handle specific parts

of the business process. However, defining the subdomain for

each microservice introduces several complexities when data

entities that are closely related are shared. This approach

necessitates maintaining independent data stores for each

microservice, leading to various challenges such as ensuring

the atomicity, consistency, isolation, and durability (ACID) of

data, managing transactions and their dependencies, and

defining mapping rules, among others [2].

Microservices are the result of breaking down an

application services into smaller components to enhance

composability, agility, deployment, and alignment with

business objectives [1]. Shahir et al. state that decentralized

data management is a crucial design characteristic of

microservices. They propose that each microservice should

manage its own database, as using a shared database violates

the principles of microservices architecture. Therefore, a key

element is to ensure that each microservice maintains its own

data storage [2].

There is a growing demand for data-driven web

applications, such as those used for recommendations,

predictions, and segmentation. These applications are often

transformed into complex conglomerates of services that

operate with challenges in coherence and management within

their architecture [3] Microservices can be a potential solution.

Unlike traditional services, microservices represent an

architectural style focused on decoupling and specialization.

The guiding principle is that each microservice should

perform a single task in the most efficient way and be easy to

understand. This task should have the smallest possible

function, but not necessarily the minimal one [1], [4], [5], [6],

this leaves software architects with the responsibility of

finding a middle ground and determining the appropriate level

of function and data granularity based on their experience and

judgment. According to authors like Nadareishvili et al.,

breaking a service into smaller parts requires a method that

establishes the criteria for a minimum viable level of

granularity to be managed by each microservice [7]. The

existence of a minimum viable size is not proposed. Instead,

they note that there is no definition in the literature regarding

how small or independent microservices should be [8],

ultimately leaving it to those designing microservices to

establish their own guidelines.

Data modeling initially relies on the abstraction of

business entities, evaluated as a unified set with attributes,

relationships, and interactions [9]. In microservices design,

this modeling must be segmented, with each microservice

specializing in a specific section. This involves defining an

individual storage strategy, consuming data from other

microservices, and mapping to the original data source.

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14449867

P. Patricio and V. Velepucha,

Data Domain Servitization for Microservices Architecture,

Latin-American Journal of Computing (LAJC), vol. 12, no. 1, 2025.

The authors highlight the need for existing or new

methods in the literature to guide the segmentation of data

structures, ensuring viable microservices implementation.

Without such methods, designers may rely on intuition and

trial and error, potentially breaking design principles,

consuming excessive resources, and blurring distinctions from

traditional SOA services.

In a preliminary literature review, the authors did not

find a proposed method for segmenting a business data model

or determining the appropriate granularity for each

microservice. Therefore, a more comprehensive study through

a Systematic Literature Mapping is necessary to identify the

presence or absence of such methods in the scientific

literature. Identifying these methods would help highlight and

disseminate them among software architects and researchers,

promoting microservices architecture as a viable option.

Conversely, if no such methods are found, it would open a new

research area focused on this specific topic.

The study aims to conduct an exhaustive literature

search to determine if methods exist for segmenting data

structures in microservice design. The article explores the

challenge of aligning microservice principles with a method

to determine the appropriate data granularity (fine or coarse)

for each service without physically dividing the business

domain while ensuring better efficiency, performance, and

balance of the microservice.

This article is structured as follows: Section II describes

the Systematic Review procedure, including the method,

research questions, inclusion and exclusion criteria, search

strategy, selection process, and data extraction. Section III

details the techniques found for data modeling in

microservices. Section IV discusses the results obtained.

Finally, Section V presents the conclusions of the study.

II. S

YSTEMATIC REVIEW

A. Research Framework

According to Kitchenham [11] in several of his

collaborations, has established the main approaches to carry

out literature reviews rigorously in the field of software

engineering such as Systematic Literature Review, SLR;

Systematic Mapping Study, SMS [10] and Mapping Review

Combined with a Systematic Review. A Systematic Mapping

Study (SMS) is defined as “a broad review of primary studies

in a specific topic to identify the available evidence in that

area”. In this context, primary and secondary studies are

distinguished. A primary study is “an empirical study

investigating a specific research question”, while a secondary

study is “a study that reviews all primary studies related to a

specific research question with the aim of

integrating/synthesizing the evidence related to that research

question”.

A Mapping Review Combined with a Systematic Review

provides a structured reporting framework for research, often

presenting results through categorization, which frequently

offers a visual summary of its findings (the map). The analysis

of results is conducted by extracting relevant information and

categorizing it to determine the contributions of primary

studies within the research area.

In addition to SMS, there are other types of secondary

studies, such as Systematic Reviews. According to

Kitchenham and Charters [10], a Systematic Literature

Review is “a type of secondary study that uses a well-defined

methodology to identify, analyze, and interpret all evidence

related to a specific research question in an objective and

repeatable manner (to some degree)”.

Given the issues outlined in the introduction and the

objectives set for this research work, an SMS was chosen as

the proposed Research Framework.

B. Methodology

To minimize potential threats to the validity of the research

and to provide appropriate answers to the research questions,

the authors have chosen to use a Systematic Mapping Study

(SMS) following the process below [10], [11]:

1) Research Question

a) Problem Statement

b) Research Questions

2) Inclusion and Exclusion Criteria

a) Selection Criteria

3) Search Strategy

a) Control Group

b) Search String

c) Candidate Studies

4) Selection Process

a) Candidate Studies

b) Study Selection

c) Primary Studies

5) Data Extraction

a) Feature Extraction

b) Model Extraction

Figure 1 presents the sequence of steps performed as part

of the Systematic Mapping Study applied to this research

project.

C. Research Question

1) Problem Statement

Unlike traditional SOA services, a distinctive

characteristic of microservices is that, by design, each one

should have its own specialized and independent data model

and storage. This approach avoids direct references between

services, making them more decoupled and modular, enabling

the composition of more complex deployments [2].

During business data modeling, analysts typically abstract

all entities, attributes, relationships, and interactions to create

a unified representation model [9]. However, in the design of

microservices, it is necessary to segment the data to identify,

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14449867

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

for each microservice, the relevant entities, attributes,

relationships, and interactions specific to its limited scope.

Fig. 1. Systematic Mapping Study Process

This segmentation process is often based on the intuition

and judgment of the developer or software architect, lacking a

guideline or method grounded in theory that defines the

optimal level of granularity and the appropriate boundaries for

subdivision.

Thus, there is a need to explore the scientific literature for

proposed methods for data structure segmentation. If such

methods are found, they could strengthen the theoretical and

practical aspects that would encourage broader adoption of

microservices architecture according to its theoretical

conception. Without a defined method, each practitioner may

select the desired level of granularity based on intuition and

trial and error. This could lead to violations of certain design

principles, excessive resource consumption, and neglect of the

necessary differentiation from traditional SOA services,

rendering microservices almost unnecessary.

2) Research Question

To ensure a comprehensive research approach to the

proposed problem, the following research questions were

defined by mutual agreement among the authors:

• RQ1: What data modeling techniques can be used

for business data segmentation in the construction

of microservices?

• RQ2: How can the granularity of microservices be

modeled or established?

Table 1 presents the aspects that these research questions

aim to address.

TABLE I. OBJECTIVES OF THE RESEARCH QUESTIONS

Question ID

Objective

QR1

Determine which data modeling techniques exist for

data segmentation in the

implementation of

microservices. If any are found, detail the

characteristics of the proposed data segmentation

procedure and discuss its coverage in relation to the

proposed problem.

QR2

Identify the approaches used to define and measure the

levels of

functional and data granularity in the

implementation of microservices.

D. Inclusion and Exclusion Criteria

1) Inclusion Criteria:

• The article describes a method aimed at data

segmentation in microservices design.

• The article describes a process or data modeling

technique focused on data segmentation in

microservices design.

• The article describes aspects or criteria used to

define the granularity of microservices.

• The article presents examples or case studies

demonstrating the application of data

segmentation in microservices design.

• The article discusses the challenges of not

having or being unable to define a data

segmentation method in microservices design.

2) Exclusion Criteria:

• Articles that only define the concepts, benefits,

criteria, or implementations of microservices

architecture, without addressing the

incorporation of a method for data segmentation

in microservices design.

• Articles that focus solely on the design or

applicability aspects of microservices without

addressing data modeling.

• Articles that address data segmentation but do

not focus on its applicability to microservices.

3) General Criteria:

• The search will be conducted in recognized

scientific databases: Web of Science, IEEE

Xplore, Scopus, and Science Direct.

• Only articles with full-text availability will be

considered.

• Articles from the last three years will be

considered, as this period encompasses the

existence of microservices architecture.

• Additional articles will be considered through

snowballing or opportunistic searches, as

needed.

E. Search Strategy

1) Control Group

Based on the proposed topic, each author independently

conducted preliminary searches in scientific databases using

the terms identified in the research questions and their

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14449867

P. Patricio and V. Velepucha,

Data Domain Servitization for Microservices Architecture,

Latin-American Journal of Computing (LAJC), vol. 12, no. 1, 2025.

intuitive judgment to find related articles. The articles

presented by each author are listed in Table II.

TABLE II. INITIALLY IDENTIFIED ARTICLES

Author

Articles

Author 1 [1], [12], [13], [14], [15], [16], [17]

Author 2 [1], [14], [17], [18], [19], [20], [21]

A review meeting was subsequently held to focus on

selecting articles most closely aligned with the objective of the

study by reviewing titles and abstracts.

The control group was formed with the articles presented

in Table III.

TABLE III. ARTICLES FORMING THE CONTROL GROUP

Control Group Articles

[1], [14], [17], [18], [20], [21]

2) Search String

To construct an appropriate search string for this study, the

definition of Population, Intervention, Outcomes, and Context

(PICO) was used [22]. The terms were derived from the

review of the Control Group articles and the Research

Questions.

Additionally, synonyms or alternative words for key terms

were identified. This broadens the scope of the search and

helps to retrieve the maximum number of relevant primary

studies on the topic.

The results of this activity are shown in Table IV.

TABLE IV. ELEMENTS IN THE RESEARCH QUESTIONS STRUCTURE

Population

Software architects and

analysts,

software researchers

Software analysts,

software researchers

Intervention

Microservices

(alternative:

Microservitization)

microservice,

microservitization

Outcomes

Data granularity

(alternatives: database, data

segmentation, data modeling)

granularity,

database, data

segmentation, data

modeling

Context

Software development

companies, software

development teams, software

consultants, software

researchers, software product

creators, software industry

software companies,

software teams,

software researchers,

software product

creators

The search strings were constructed by combining terms

from the Intervention and Outcomes sections. The final

expression was formed by conjunctions between sub-

expressions in each group. A pilot test of preliminary search

strings was conducted in the Scopus digital library. The results

are presented in Table V.

TABLE V. PILOT RESULTS FOR SEARCH STRING IN SCOPUS

Search String

Count

(''microservices'' AND ''granularity'')

Search String

Count

((''microservices'' OR ''microservitization'') AND

(''granularity'' OR ''database''))

((''microservices'' OR ''microservitization'') AND

(''granularity'' OR ''database'' OR ''data segmentation''))

((''microservices'' OR ''microservitization'') AND

(''granularity'' OR ''database'' OR ''data segmentation'' OR

''data modeling''))

The pilot results show that an adequate number of articles

were retrieved using the last proposed search string. The

search string includes all articles proposed for the control

group. The authors agree that the chosen search string for this

study is:

((''microservices'' OR ''microservitization'') AND

(''granularity'' OR ''database'' OR ''data segmentation'' OR

''data modeling''))

3) Candidate Studies

Searches were conducted in the following scientific

databases: Web of Science, Scopus, IEEE Xplore, and Science

Direct, using the search string selected in the previous step in

the advanced search option. The results of the articles found

are presented in Table VI.

TABLE VI. SEARCH RESULTS

Database

Count

Scopus

Web of Science

IEEE Xplore

Science Direct

F. Selection Process

1) Refinement

After querying the scientific databases, a total of 199

candidate articles were identified, establishing the baseline for

analysis. However, some of these articles were duplicated

across databases, necessitating a refinement process to remove

duplicates or articles with identical content. Additionally, any

references to entire conferences or workshops, rather than

specific articles, were excluded. This resulted in 174 articles

proceeding to the next stage.

2) Study Selection

Once the candidate studies were obtained, the selection

process proceeds as follows:

• Apply the previously determined inclusion and

exclusion criteria by reading the abstracts of the

articles (31 articles).

• Obtain the full-text versions of the remaining

articles, discarding those for which the full text

could not be accessed (25 articles).

• Each author conducted an exploratory reading of

each article to assess its relevance and

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14449867

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

contribution to the study and the research

questions (25 articles reviewed).

• The authors performed a cross-validation

through discussion, reaching a consensus on

which articles should be selected (16 articles).

At the end of the selection process, 16 articles were chosen

to proceed to the next stage.

3) Primary Studies

The selected articles were reviewed, considering the

inclusion of additional articles through snowballing or

opportunistic search techniques. The authors decided to use

only the previously selected articles.

Finally, the authors reached a consensus and approved the

list of articles for data extraction. A total of 16 primary articles

were selected to proceed to the data extraction stage. The

primary articles are as follows: [1], [3], [12], [15], [17], [20],

[21], [23], [24], [25], [26], [27], [28], [29], [30], [31].

G. Data Extraction

1) Feature Extraction

The data extraction process involved reading the primary

studies and highlighting elements that contribute to answering

the research questions. The Atlas.ti tool was used for this

purpose, allowing unified coding across all articles, providing

traceability, and structuring semantic networks to support the

writing of findings. Additionally, information on the

publication type, year, and authors was collected and

tabulated, enabling the organization of articles as needed. For

example, Figure 2 shows the distribution of primary articles

by year and publication type.

Fig. 2. Characteristics of Primary Studies

2) Model Extraction

Since one of the objectives of the study was to determine

if a data modeling method exists for business data

segmentation in the construction of microservices, the

expectation was to find graphical representations in the

primary articles outlining procedures for determining

granularity in each case. This section aimed to collect visual

schematics to complement the textual coding results.

However, upon reviewing the primary articles, no

representative graphical elements were found for this purpose.

Therefore, the analysis will be based solely on the textual

ideas presented in the primary studies.

III. D

ATA MODELING METHODS FOR MICROSERVICES

This research identifies architectural approaches for

modeling the granularity of microservices to address key

research challenges. The findings highlight the use of meta-

modeling techniques that define microservice boundaries as

adaptable entities, focusing on aspects such as business-driven

design, tool heterogeneity, and decentralized governance [1].

These approaches support analysis, evolution, and

localization, which are crucial for adapting microservice

granularity based on quality attributes [32].

Migration to microservices, or microservitization,

enhances autonomy, replaceability, and governance while

improving the traceability of software architectures [33].

However, there is still a lack of consensus on the definition,

properties, and modeling techniques of microservices.

Effective migration involves determining optimal granularity,

deployment strategies, and orchestration methods [34].

One of the main challenges is establishing the optimal

granularity level, balancing microservice size and number to

meet both individual and overall system requirements [1].

Microservitization involves identifying optimal service

boundaries to enhance the Quality of Service (QoS) [35].

Recent trends, such as Service-Oriented-Architecture

(SOA) and Microservice Architecture (MSA), have emerged

as suitable approaches for cloud infrastructures [36]. MSA

aims to create flexible, modular applications, but its practical

implementation remains a significant research challenge.

Modernization efforts involve understanding and

transforming large applications into microservices, using

model-driven methods to manage complexity and

dependencies across business and data layers [37].

IV. D

ISCUSSION

Currently, more organizations with complex business

domains are moving away from monolithic software

applications and adopting distributed architectures based on

microservices. Microservices architecture aims for agile

software development using small services that communicate

via APIs, where each service implements complete business

functionalities and can run independently. Microservices can

be deployed across different machines, using diverse

programming languages and data dependencies that the

business requires, maximizing scalability and leveraging the

strengths of each platform. They are designed as small,

simple, and understandable executable units, which makes

them easier to modify and maintain.

However, modeling the domain for each microservice

with the necessary dependencies within the business context

requires software architects to clearly define each service

responsibilities and APIs to achieve good cohesion and low

structural coupling. The architecture should facilitate parallel

development with different teams working on separate

microservices, allowing services to be rewritten with minimal

effort if necessary. For microservices to be autonomous from

development to deployment, architects need strategies for

domain modeling, deployment, versioning, monitoring,

security, maintenance, and managing business-driven

changes.

The challenge posed by the research questions is to define

the appropriate data domain modeling for visualizing the

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14449867

P. Patricio and V. Velepucha,

Data Domain Servitization for Microservices Architecture,

Latin-American Journal of Computing (LAJC), vol. 12, no. 1, 2025.

structural granularity that each microservice must implement.

The research framework focuses on using software archetypes

for modeling business data for each microservice, as

archetypes are fundamental human mechanisms that organize,

summarize, and generalize domain information. This is

expected to have applications in software engineering. The

framework is built upon a literature review that includes

factors demonstrating the application of software archetype

principles for business data modeling in software and service

engineering. A case study will be conducted in Ecuadorian

public and private institutions to model business data and

evaluate the advantages and disadvantages of the proposed

model.

For technology companies, understanding their business

data is crucial, as it represents the backbone of their

information systems. Therefore, software engineering

employs various methodologies and techniques to address

data modeling from different perspectives, from business

domains to data storage. The proposed research framework

will analyze systems that use data archetypes for

microservices.

After conducting the study, the research questions are

revisited:

• RQ1: What data modeling techniques can be used for

business data segmentation in microservices

construction?

- It is proposed that services be stateless (do not manage

a database) [24].

- A centralized data model is proposed [25].

- The use of decentralized NoSQL databases is

proposed

A method is proposed where only the most critical

business elements are subdivided, based on the

benefits of microservices [27].

• RQ2: How to model or establish microservice

granularity?

- Proposes a type of microservice diagram and

microservice invocation diagram

- Proposes SMART and Entice methods [23].

V. T

HREATS TO VALIDITY

Throughout the study, continuous discussions were held

regarding the procedures to follow and the potential threats to

validity. Efforts were made at every step to maintain the rigor

and thoroughness required for the resulting document to

contribute to scientific knowledge.

A. Threats in Search String Formation and Primary

Study Selection

One challenge was determining the scope of our

study since data modeling for microservices is a relatively new

and less explored topic in the technological field (emerging

over the last three years). Different communities often use

varying terminologies for the same concepts. To cover the

research questions comprehensively and avoid bias, we

searched for terms related to microservices and data modeling

across various contexts. While this approach reduces bias, it

significantly increased the search effort, necessitating a

manageable scope.

B. Threats to Study Selection and Data Extraction

Consistency

The formulation of research questions helped in selecting

relevant studies. However, two articles that appeared highly

relevant based on their abstracts could not be accessed in full-

text form and were therefore excluded. Due to time constraints

for tabulation and coding, it was not possible to perform a

detailed semantic analysis or comprehensive reading. The

primary articles were reviewed based on coverage and a focus

on relevant section.

VI. C

ONCLUSIONS

Microservices result from breaking down application

services into smaller components, with a distinctive feature of

having their own database separate from other microservices,

enhancing composability and deployment capabilities.

Organizations transitioning to a microservices-based-

architecture often start with an existing system that already has

a unified data model representing the entire business dynamic.

Therefore, designing a migration strategy to microservices

requires segmenting this model into smaller parts.

A preliminary literature review was conducted to find

articles proposing a method for determining the appropriate

level of granularity for model division. No references were

found on this topic. Performing this task without a theoretical

framework may lead to decisions based on intuition or trial

and error, increasing resource consumption and questioning

the need for microservices compared to the maturity of SOA-

based web services.

An empirical study is proposed using the Systematic

Mapping Study (SMS) method to conduct a thorough

literature review to validate the existence of methods that

define reasonable granularity in microservice design.

The literature review procedure followed these steps:

Structuring the Research Question, Defining Inclusion and

Exclusion Criteria, Formulating the Search Strategy,

Executing the Selection Process to identify primary studies,

and finally extracting the required data. This process enabled

the coding of relevant elements in the primary articles, leading

to the synthesis, analysis of results, and answering of the

research questions.

Future work involves experimentation with existing model

subdivision methods for microservices, so that, once validated

across different scenarios, these methods can be considered

best practices in implementing this architecture.

CKNOWLEDGMENT

The authors would like to express their sincere gratitude to

the Salesian Polytechnic University for providing the facilities

and resources essential for conducting this research. Special

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14449867

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

thanks are also extended to the National Polytechnic School

for its valuable academic support and for fostering an

environment that encouraged collaborative learning and

innovation throughout this project.

EFERENCES

[1]

S. Hassan, N. Ali and R. Bahsoon, "Microservice Ambients: An

Architectural Meta

Modelling Approach for Microservice

Granularity," p. 1–10, April 2017.

[2]

S. D. N. V. Duy, K. Eati, C. M. Ferreira, D. Glozic, V. Gucer, M.

Gupta, S. Joshi, V. Lampkin, M. martins, S. Narain and R. Vennam.,

"Microservices from Theory to Practice Creating Applications in IBM

Bluemix Using the Microservices Approach," Ibm, p. 170, 2015.

[3]

N. a. L. M. a. B. J. a. G. R. a. N. J. Viennot, "Synapse: A microservices

architecture for heterogeneous

database web applications,"

Proceedings of the 10th European Conference on Computer Systems,

EuroSys 2015, 2015.

[4]

L. Marco-Ruiz, D. Moner, J. A. Maldonado, N. Kolstrup and J. G.

Bellika, "Archetype

based data warehouse environment to enable the

reuse of electronic health record data," International Journal of

Medical Informatics, vol. 84, p. 702–714, September 2015.

[5]

J. Thönes, "Microservices," IEEE Software, vol. 32, 2015.

[6]

M. Yousif, "Microservices," IEEE Cloud Computing, vol. 3, p. 4–5,

2016.

[7]

I. Nadareishvili, R. Mitra, M. McLarty and M. Amundsen,

"Microservice Architecture: Aligning Principles, Practices, and

Culture," 2016.

[8]

I. a. H. A. a. M. R. d. S. a. S. F. Salvadori, "Publishing linked data

through semantic microservices composition," Proceedings of the

18th International Conference on Information Integration and Web

based Applications and Services - iiWAS '16, pp. 443--452, 2016.

[9]

C. a. C. S. a. N. S. B. Batini, "Conceptual Database Design: An Entity-

Relationship Approach," p. 470, 1992.

[10]

B. Kitchenham and S. Charters, "Guidelines for performing

Systematic Literature Reviews in Software Engineering," vol. 2, p.

1051, 2007.

[11]

K. Petersen, R. Feldt, S. Mujtaba and M. Mattsson, "Systematic

Mapping Studies in Software Engineering.," vol. 8, p. 68–77, 2008.

[12]

F. Rademacher, S. Sachweh and A. Zundorf, "Differences between

model

-driven development of service-

oriented and microservice

architecture,"

Proceedings -

2017 IEEE International Conference on

Software Architecture Workshops, ICSAW 2017: Side Track

Proceedings, p. 38–45, 2017.

[13]

B. Mayer and R. Weinreich, "A dashboard for microservice

monitoring and management,"

Proceedings -

2017 IEEE International

Conference on Software Architecture Workshops, ICSAW 2017: Side

Track Proceedings, p. 66–69, 2017.

[14]

A. Messina, R. Rizzo, P. Storniolo, M. Tripiciano and A. Urso, "The

database

-is-the-

service pattern for microservice architectures," vol.

9832, p. 223–233, 2016.

[15]

V. D. Le, M. M. Neff, R. V. Stewart, R. Kelley, E. Fritzinger, S. M.

Dascalu and F. C. Harris, "Microservice

based architecture for the

NRDC,"

Proceeding -

2015 IEEE International Conference on

Industrial Informatics, INDIN 2015, p. 1659–1664, 2015.

[16]

J. Jenkins, G. Shipman, J. Mohd-Yusof, K. Barros, P. Carns and R.

Ross, "A Case Study in Computational Caching Microservices for

HPC," 2017 IEEE International Parallel and Distributed Processing

Symposium Workshops (IPDPSW), p. 1309–1316, 2017.

[17]

S. Hassan and R. Bahsoon, "Microservices and their design trade-offs:

A self

-adaptive roadmap," Proceedings -

2016 IEEE International

Conference on Services Computing, SCC 2016, p. 813–818, 2016.

[18]

T. Thiele, T. Sommer, S. Stiehm, S. Jeschke and A. Richert,

"Exploring research networks with data science: A data

driven

microservice architecture for synergy detection,"

Proceedings -

2016

4th International Conference on Future Internet of Things and Cloud

Workshops, W-FiCloud 2016, p. 246–251, 2016.

[19]

S. Haselbock and R. Weinreich, "Decision guidance models for

microservice monitoring," Proceedings -

2017 IEEE International

Conference on Software Architecture Workshops, ICSAW 2017: Side

Track Proceedings, p. 54–61, 2017.

[20]

C. a. T. M. a. I. D. a. I. B. Gadea, "A reference architecture for real-

time microservice API consumption,"

3rd Workshop on CrossCloud

Infrastructures and Platforms, CrossCloud 2016 -

Colocated with

EuroSys 2016, 2016.

[21]

W. Hasselbring and G. Steinacker, "Microservice architectures for

scalability, agility and reliability in e-commerce," Proceedings -

2017

IEEE International Conference on Software Architecture Workshops,

ICSAW 2017: Side Track Proceedings, p. 243–246, 2017.

[22]

B. Kitchenham, "Procedures for performing systematic reviews,"

Keele, UK, Keele University, vol. 33, p. 28, 2004.

[23]

P. Di Francesco, "Architecting microservices," Proceedings - 2017

IEEE International Conference on Software Architecture Workshops,

ICSAW 2017: Side Track Proceedings, pp. 224--229, 2017.

[24]

N. H. a. {. D. T. a. {. T. X. a. F. L. a. R. C. Do, "A scalable routing

mechanism for stateful microservices,"

Proceedings of the 2017 20th

Conference on Innovations in Clouds, Internet and Networks, ICIN

2017, pp. 72--78, 2017.

[25]

C. a. C. A. a. C. K.-K. Esposito, "Challenges in Delivering Software

in the Cloud as Microservices," IEEE Cloud Computing,

vol. 3, no. 5,

pp. 10--14, 2016.

[26]

D. a. C. D. a. A. R. a. C. E. a. G. K. a. P. C. a. C. R. Escobar, "Towards

the understanding and evolution of monolithic applications as

microservices,"

Proceedings of the 2016 42nd Latin American

Computing Conference, CLEI 2016, 2017.

[27]

J.-P. a. T. D. Gouigoux, "From monolith to microservices: Lessons

learned on an industrial migration to a web oriented architecture,"

Proceedings -

2017 IEEE International Conference on Software

Architecture Workshops, ICSAW 2017: Side Track Proceedings,

pp.

62--65, 2017.

[28]

M. a. K. L. a. G. W. a. Z. O. Gysel, "Service cutter: A systematic

approach to service decomposition,"

Lecture Notes in Computer

Science (including subseries Lecture Notes in Artificial Intelligence

and Lecture Notes in Bioinformatics), vol. 9846, pp. 185--200, 2016.

[29]

P. a. T. Y. Kookarinrat, "Design and implementation of a

decentralized message bus for microservices,"

2016 13th

International Joint Conference on Computer Science and Software

Engineering, JCSSE 2016, 2016.

[30]

J. a. L. L. C. a. H. S. Lin, "Migrating web applications to clouds with

microservice architectures,"

2016 International Conference on

Applied System Innovation, IEEE ICASI 2016, 2016.

[31]

H. a. K. M. a. G. S. Vural, "A systematic literature review on

microservices,"

Lecture Notes in Computer Science (including

subseries Lecture Notes in Artificial Intelligence and Lecture Notes in

Bioinformatics), vol. 10409 LNCS, pp. 203--217, 2017.

[32]

S. Rochimah, H. I. Rahmani and U. L. Yuhana, "Usability

characteristi

c evaluation on administration module of Academic

Information System using ISO/IEC 9126 quality model," 2015.

[33]

M. a. J. C. a. S. T. Mikusz, "Software Business," Lecture Notes in

Business Information Processing, vol. 210, pp. 167--173, 2015.

[34]

C. Tantithamthavorn, S. McIntosh, A. E. Hassan and K. Matsumoto,

"Comments on Researcher Bias: The Use of Machine Learning in

Software Defect Prediction,"

IEEE Transactions on Software

Engineering, vol. 42, 2016.

[35]

R. Hammad, M. Odeh and Z. Khan, "Towards a Model-Based

approach to evaluate the effectiveness of e-Learning,"

Proceedings of

the European Conference on IS Management and Evaluation, ECIME,

Vols. 2015-Janua, 2015.

[36]

G. Prabu, N. Kannan, A. Kovalan and S. Thaddeus, "Software

measurement linkage for CMMI implementation,"

International

Journal of Control Theory and Applications, vol. 9, 2016.

[37]

S. M. Castro, E. Tseytlin, O. Medvedeva, K. Mitchell, S.

Visweswaran, T. Bekhuis and R. S. Jacobson, "Automated annotation

and classification of BI-

RADS assessment from radiology reports,"

Journal of Biomedical Informatics, vol. 69, 2017.

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

AUTHORS

Patricio Michael Paccha A. (Loja, Ecuador, March 1, 1980). Engineer in

Information Systems and Computing, Higher Fourth Level Diploma in

Strategic Marketing Management, Master in software engineering and

PhD student in Computing.

Skills and experience in managing innovation and software engineering

projects, Data y Software Architect, DBA, Business Application

Developer, Business Intelligence, Speaker in areas of technological

innovation. Currently Professor of Systems Engineering and Computer

Engineering at the National Polytechnic School – Ecuador.

Víctor Vicente Velepucha Bonett (). Engineer in Information and

Computing Systems from the National Polytechnic School, Master in

Project Management and Doctor in Computer Science.

Lines of research linked to Computing Applied to Software

Engineering, Software Creation and Management and the Organization

and Properties of Software. Currently teaching in the Department

of Informatics and Computer Sciences of the National Polytechnic

School – Ecuador.

Patricio Michael Paccha Angamarca

Victor Vicente Velepucha Bonett

P. Patricio and V. Velepucha,

Data Domain Servitization for Microservices Architecture,

Latin-American Journal of Computing (LAJC), vol. 12, no. 1, 2025.