SOSE4BD: Service-Oriented Software Engineering Framework for
Big Data Applications
Muthu Ramachandran
School of Computing, Creative Technologies, and Engineering, Leeds Beckett University,
Headingley Campus, Leeds LS6 3QS, U.K.
Keywords: Service-Oriented Software Engineering for Big Data Applications (SOSE4BD), Software Engineering
Framework for Service and Cloud Computing (SEF-SCC), Cloud Software Engineering, Service-Oriented
Architecture (SOA), Service Computing, Reference Architecture, Service Reuse, Software Engineering for
Service and Cloud Computing (SE-Cloud), Business Process Driven Service Development Lifecycle
(BPD-SDL), Business Process Modelling Notation (BPMN), Service-Oriented Architecture Modelling
Language (SOAML), Quality of Service (QoS).
Abstract: Service computing has emerged to address the notion of delivering software as a service and Service-Oriented
Architecture emerged as a design method supporting well defined design principles of loose coupling,
interface design, autonomic computing, seamless integration, and publish/subscribe paradigm. Integrated big
data applications with IoT, Fog, and Cloud Computing grow exponentially: businesses as well as the speed
of the data and its storage. Therefore, it is time to consider systematic and engineering approach to developing
and deploying big data services as the data-driven applications and devices increasing rapidly. This paper
proposes a software engineering framework and a reference architecture which is SOA based for big data
applications’ development. This paper also concludes with a simulation of a complex big data Facebook
application with real-time streaming using part of the requirements engineering aspect of the SOSE4BD
framework with BPMN as a tool for requirement modelling and simulation to study the characteristics before
big data service design, development, and deployment. The simulation results demonstrated the efficiency
and effectiveness of developing big data applications using the reference architecture framework for big data.
1 INTRODUCTION
Service computing has emerged to deliver software as
a service, based on established design principles of
reuse, composability, autonomic computing,
stateless, platform and enterprise integration. SOA is
a way of architecting and structuring and designing
reusable software assets: service components and
resources using message passing as the core design
principle to maximise reuse (design for reusable
services based on the design principles of
composition and scalability). In other words, make it
available for reuse and scalability. Big data can be
defined by the famous 5Vs (Volume, Velocity,
Veracity, Variety, and Value) with extensive data sets
captured from multiple channels (NIST). Big data
applications with IoT, Fog, and Cloud Computing
grow exponentially: businesses as well as the speed
of the data and its storage. Therefore, it is the correct
time to consider the systematic engineering approach
to developing and deploying big data services. For
example, based on 2017 data, google search handles
3.5 million searches per minute and Facebook
handles 1 billion active users and stores more than
300 petabyte per minute.
In this changing era of development, services are
to be Robust, Agile, Accessible and Available to its
clients. For secured and guaranteed delivery of
services, every big organization is shifting their
service delivery model to Enterprise Service Bus
(ESB) which is the key design paradigm of Service-
Oriented Architecture (SOA) and guarantees Reuse,
Reliability, Resiliency (3Rs), as well as Availability.
In this context, the following research questions are
posed:
• What are the design principles for an SOA driven
reference architecture?
• What are services comprise reference architecture
for big data systems?
• How to classify technologies and products/ servi-
248
Ramachandran, M.
SOSE4BD: Service-Oriented Software Engineering Framework for Big Data Applications.
DOI: 10.5220/0007708702480254
In Proceedings of the 4th International Conference on Internet of Things, Big Data and Security (IoTBDS 2019), pages 248-254
ISBN: 978-989-758-369-8
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

ces of big data systems?
This paper presents a systematic software engineering
approach to developing big data services and
analytics services and applications. This paper also
presents a service-oriented software engineering
framework for big data (SOSE4BD). Section 1
discusses an introduction and sets research agenda in
the area of software engineering in the era of big data,
IoT, and cloud computing technologies and software
as a service paradigm. Section 2 presents background
studies. Section 3 presents SOSE framework.
2 BACKGROUND: SOFTWARE
ENGINEERING FOR BIG DATA
APPLICATIONS
One of the main characteristics of big data systems is
commonly known as 3Vs, 5Vs, and 7Vs and are
discussed as velocity, volume, variety, veracity, and
value of incoming data in real-time or a captured data
over several time periods which is shown in Figure 1.
 Velocity: BD requires real-time processing at
varying intervals and may include stream as well
as batch processing
 Volume: BD provides a massive historical data
over several time periods (years, months, weeks,
days, etc.)
 Variety: The BD captured may be in a variety of
formats (multiple files and multi-modal data) and
may be structured and unstructured.
 Veracity: The BD captured may contain unwanted
data which require extraction, transformation, and
cleaning
 Value: BD may contain very highly valuable as
well as not so useful data and it requires a skilled
data scientist to identify what to consider for
analytical processing and what to discard.
Figure 1: 5Vs of Big Data.
According to Internet Minute (2018), it captures,
973K logins in 60 seconds globally, 4.3 million
videos watched in 60 seconds, etc. This demonstrates
the increasing volume and velocity of data being used
and generated. In addition, the number of devices
used to generate these data are rapidly increasing and
the fusion of devices, applications and composition of
new applications and analytics is also on a fast pace.
Therefore, it is important to adopt a systematic
approach to developing, capturing, analysing,
measuring, and using big data.
Most of the big data projects fail due to lack of
findings on the ways to capture, systematically
manage, interpret, and to predict business directions
out of big data investments. Gorton (2014) says that
lack of knowledge in technologies, systematic
approaches, and discipline around big data are new
and therefore it is difficult for people to make
business judgement based on data visualisation alone.
Therefore, this paper emphasises on software
engineering approach to big data and its applications.
Gorton (2014) also states that big data is a complex
software engineering problem than a data science
problem. It has been proposed a lightweight
evaluation and architecture prototyping for big data
(LEAP4BD) which is based on creating a knowledge
base to derive quality requirements, evaluation
criteria, candidate selection and prototyping. Most of
the problems that have been identified are the size of
data, speed of data, horizontal and vertical scaling of
distribution, different political sources of data,
consistency of data, scalability of data, performance
of data and availability of data. We all know that over
fifty years of software engineering practices revealed
that scalable architecture, technologies, processes and
platforms have been successful in delivering cost-
effective solutions. In this context, SEI has developed
a knowledge base for big data architecture and
technologies known as QuABaseBD (2018).
Gorton et al., (2016) discuss what is known as
Eric Brewer’s CAP theorem which means a system
must be able to support Consistency, Availability, and
Partition (support for message between nodes in the
cluster). They also state that this theorem forces to
develop scalable architecture. However, the above
approach has limitation in providing software
engineering approach to big data problem. Therefore,
in this paper, we believe, a reference architecture is
the best solution to tackle large scale applications of
big data with a systematic software engineering
approach.
Therefore, in this paper, we propose a software
engineering framework for big data (SEF4BD) which
provides a systematic process for big data projects
SOSE4BD: Service-Oriented Software Engineering Framework for Big Data Applications
249

and a reference architecture for big data (REF4BD),
providing strict architectural structuring based on
reference architecture model.
Karakaya (2017) discusses big data frameworks
such as Hadoop, Spark, Storm and Flink which are
specifically developed to solve big data applications
by providing facilities to collect, process, manage,
monitor and to analyse big data. However, this paper
also discusses the big data applications and their
limitations without software engineering approach.
Madhavji et al., (2015) present a contextual model
of big data software engineering which includes
scenarios of data capturing, storing and visualising
support. Arruda and Madhavji, (2017) and Xu et al.,
(2018) have proposed requirements engineering
artefact model for big data systems in which they
classified requirements engineering activities into
four categories such as data consumer requirements,
data transformation requirements, data source
requirements, and data capability requirements. All
are part of big data requirements which include
traditional requirements engineering aspects such as
functional and non-functional. Non-Functional
requirements should include key quality attributes for
big data systems such as performance, reliability,
privacy, and security. However, it is ongoing research
and details of RE for BD remains unclear.
Arndt (2018) discusses the importance of the
interplay between software engineering and big data
and has discussed two distinct areas for further
exploration:
1. Software Engineering for Big Data which can
provide a systematic process for improving the
development of big data systems. The process
includes requirements gathering for BD, software
architecture for BD, testing and debugging BD
systems (performance, reliability, and security)
where the logs of analysing 5V characteristics
should be included, SE process for BD which
could include CMMI, and finally Managing BD
projects.
2. Big Data Software Engineering is an area of
research which should focus on utilising BD for
the benefit of improving SE practices and to
improve software production. The typical
activities should include analytics for software
engineering, data mining software repositories,
visual analytics for software engineering, and
self-adaptive systems which utilises data
generated and self-learn.
Similarly, Bagriyanik and Karahoca (2016) has
discussed extensive systematic literature survey on
big data in software engineering and have concluded
that there is a need for a holistic approach to
developing a big data system. Kacha and Zitouni
(2018) presented a data security model based on
cloud characteristics and security attributes
(confidentiality, integrity, and availability) to be built
into the data lifecycle (stored, used, and transitioned
data).
The existing studies have started to identify the
importance of the 50 years of software engineering
practices and to benefit from the emerging big data
approaches and technologies to improve businesses.
However, the field is at an early stage, and therefore
there is a lack of a clear picture of software
engineering role.
Our earlier work on a software engineering
framework for service and cloud computing
(Ramachandran, 2018) and business intelligence
architecture for big data systems (Ramachandran,
2017) have established a standardised method and
process in the cloud-based services. Hence, this paper
provides a framework for big data software
engineering which is a service based (SOA), data
service component model, SOSE Development
lifecycle for BD, and a reference architecture for BD.
3 SOSE4BD:
SERVICE-ORIENTED
SOFTWARE ENGINEERING
FRAMEWORK FOR BIG DATA
APPLICATIONS
SOA has emerged supporting business integration by
providing service components, architectural
framework with unique and unified enterprise service
bus, service orchestration, and service composition.
Therefore, it is beneficial to design and construct big
data applications using well established over 50 years
of software engineering best practices. Big data have
emerged to improve business best practices by
utilizing various data that have been generated in the
past as well as at present in a various formats and
from a variety of sources (multi-channels). Therefore,
it is essential to merge the two disciplines of big data
and service-oriented software engineering, Service-
Oriented Software Engineering for Big Data
(SOSE4BD). The basic principles of this new
discipline is shown in Figure 2, the Four Pillars of
SOSE4BD Principles.
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250

Figure 2: Four Pillars of SOSE4BD Principles.
It consists of four quadrants that provide an
integrated approach to BD, SOA, and SE as follows:
1. Service-Oriented SE for BD Applications. This
is essentially the integration of best practices of
three disciplines viz: BD, SE, and SOA, known as
SOSE4BD. It mainly consists of a software
engineering framework for big data, software
engineering framework for service computing and
cloud computing (SEF-SCC, Ramachandran,
2018), Agile practices for service computing,
requirements engineering for service computing
with BPMN modelling and simulation to verify
service process and its efficiency, design for
service reuse (building-in reusability), design for
security (build security in (BSI)).
2. Integrated Services (IoT-Cloud-BD). It
promotes integrating Data Processing
Architecture with cloud and IoT based data
streaming services.
3. Data Security (Build Security In (BSI)). We
believe in the principle of developing a secured
system throughout the lifecycle. (Ramachandran,
2012). It also provides Data Security with Data
Lifecycle (Stored, Used, Transferred, Live, &
Real-time Streaming Data), Integrating data
security attributes (confidentiality, integrity and
availability) and apply Software Security
Engineering Principles while
4. Data Modelling for BD such as misuse and abuse
cases for service requirements, threat modelling
and secure SDLC
5. Reuse-Oriented Knowledge Discovery &
Patterns. In this principle, the main aim is to
cultivate service level reuse through service
composition, autonomic computing, knowledge
discovery, extraction, patterns for reuse, reuse by
analytics patterns & predictive analytics patterns,
and BDSE metrics with data points.
Our approach to big data software engineering which
integrates software engineering, best practices with
the use of repositories for software engineering data
(Menzies and Zimmermann, 2018; Yang et al., 2018),
SOA, Service Computing, and Cloud Computing.
Therefore, Figure 3 presents a framework known as
SOSE4BD which consists of requirements
engineering for modelling and simulating service
requirements with BPMN as shown in SOSE lifecycle
(Figure 6), well proven software design using SoaML
and architecture principles, a reference architecture
for big data (REF4BD) which is a service-centric
based on SOA.
REF4BD is based on well proven design concepts
and principles as shown in Figure 4. SOSE4BD also
recommends tools for big data software engineering
analytics and predictive modelling with SAS, Visual
Paradigm, and Azure/ML. SOSE4BD also supports a
Figure 3: SOSE4BD Framework.
A number of data security-centric services are part
of ongoing research such as SOSE4BD as a service,
Bug Prediction as a Service with MLaaS (Azure
Machine Learning as a service), etc. SOSE4BD also
supports an adoption model for employing big data in
an organisation, and an evaluation of the framework
through simulation and a number of applications such
as British gas energy efficiency using our approach
(Ramachandran, 2017).
A reference architecture is the key to achieving
standard practice of developing software product
lines and services based on common architectural
style across the product family and family of software
services. Hence, SOSE4BD framework has
developed a reference architecture for big data as
shown in Figure 4 and SOSE4BD framework has also
developed a set of service component models re-
enforcing to map services into REF4BD. Oracle
(2013) also recommends a reference architecture for
big data whereas REF4BD’s reference architecture is
a service based (based on the principles of Service-
Oriented Architecture). It consists of three sets of
•DataSecuritywithData
Lifecycle(Stored,Used,
Transferred,Live,&Real‐time
StreamingData),Integratedata
securityattributes
(confidentiality,integrityand
availability) andapplySoftware
SecurityEngineeringPrinciples
whileDataModelling forBD:
•MisuseandAbusecasesfor
servicerequirements
•ThreatModelling
•SecureSDLC
•ServiceComposition
•AutonomicComputing
•KnowledgeDiscovery,
Extraction,&Patternsfor
Reuse
•ReusebyAnalytics
Patterns&Predictive
Analytics Patterns
•BDSEMetricswithData
Poin ts
•DataProcessingArchitecture
forIntegratingIoT‐Cloud‐BD
•Supportbothstream&batch
processingdata
•Designfordatacomplexity
•SOSE4BD
•AgileforBDSE
•RequirementsEngineeringforBD
withBPMNSimulation
•DesignforDataServiceReuse
•DesignforDataSecurity(Achieving
BSI)
•SOSE4BDFramework:Design
methodsbasedonsoaML &service
components,Process,Applications,
andEvaluation
Service‐
OrientedSE
forBD
Applications
Integrated
Services(IoT‐
Cloud‐BD)
DataSecurity
(Building
SecurityIn
(BSI))
Reuse‐
Oriented
Knowledge
Discovery&
Patterns
Process: Business Process Driven Service Development Lifecycle
(BPD-SDL)
Methods and Design Principles: service components with soalML
Reference Architecture for big data (REF4BD)
Tools (SAS, Visual Paradigm, BonitaSoft, Bizagi Studio, Tabulea,
Mathematica
,
Azure/ML
)
SOSE4BD as a Service (SOSEaaS), BDaaS, Big Data Adoption
Framework as a Service (BAaaS), Software Engineering Analytics as a
Service(SEAaaS), SE Prediction Model as a Service (SEPaaS), Bug
Prediction as a Service with Azure/ML (MLaaS), BD Metrics as a Service
(BDMaaS)
Adoption Models
Evaluation & Applications
SOSE4BD: Service-Oriented Software Engineering Framework for Big Data Applications
251

layers namely BD Source & Storage Layer which
focusses services on data stream and data storage,
followed by an big data enterprise service bus which
integrates multi-channel data sources (mobile, IoT,
sensors, actuators, location-based services, etc),
followed by Big Data Processing Layer which mainly
focus on data processing, data transformation, data
visualization, data analytics, and knowledge
discovery of identifying data patterns and behaviors’
for knowledge extraction, and the top layer known as
Big Data Application and Prediction Services which
focus on providing other business and improvements
monitoring services through service orchestration,
prediction modelling based on machine learning as a
service such as Microsoft Azure/ML, and provides
data security.
Figure 4: Reference architecture for big data (REF4BD).
Figure 5 shows a SOSE4BD lifecycle which
consists of starting with BD requirements stage of
identifying data source such as software repositories
for big data software engineering projects and other
data sources as shown by Menzies and Zimmermann
(2013), identifying goal for improving software
process, methods, project efficiencies from software
project managers, users, and developers, and to
identify requirements for analytics and predictive
analytics. In addition, we need identify data
requirements such as data source, data
transformation, data streaming, data storage, data
capability, and business intelligence and business
continuity. Secondly, the BD design stage should start
soon after new data and software practices and
process improvement services are validated with a
BPMN process modelling and simulation for
efficiency and resource constraints. During, the BD
design stage, used soaML for service components and
REF4DB for mapping service components into
REF4DB architectural layers. Thirdly, SOSE4BD
lifecycle recommends container based technology for
big data service implementation which could include
capturing project artefacts autonomically by
deploying BD SE services as part of the IDE
(Integrated software development environment) or
into to a cloud driven software development services.
Figure 5: SOSEBD Lifecycle.
SOSE4BD framework recommend a number of
BD SE services such as handling real-time data with
multiple-channels and cloud service providers such as
Microsoft, IBM, Google, Opensource, etc. The
soaML SOA design for SOSE4BD based on REF4BD
architecture as shown in Figure 6.
Figure 6: SOA driven SOSE4BD data services.
The services include BD Analyst, Reuse of Data
Patterns as a Service, Bug Prediction as a Service
with Machine Learning (Subbiagh et al., 2018), Data
extraction as a Service, Data Streaming as a Service,
Data Modelling as a Service, Data ETL (Data
Extraction, Data Transformation, and Data Load as a
Service), and Visual Analytics as a service, and
finally Predictive modelling and Continuous
Improvement as a Service. The next section provides
an evaluation with a Facebook real-time data
analytics case study.
BDSource&StorageLayer:
DataIntegration&Storage:
IoT‐Cloud‐Data
BDProcessingLayer:BD
Extraction‐Processing‐
Analysis‐Knowledge
Discovery‐Analytics‐
VisualizationsServices
BDApplications&Prediction
Layer:BDServiceand
Knowledge&Reuse
IntegrationandPatterns,
MachineLearningServices
BD
Application
Services
DataExtractionasaService(DEaaS)
DataProcessingasa Service(DPaaS)
DataAnalysisasaService(DAaaS)
KnowledgeDiscoveryasaService
(KDaaS)
KnowledgePatternsasaService(KPaaS)
ReusePatternsasaService(RPaaS)
DataStreamingasaService
(DSaaS):MultiSo urces:IoT,
Sensors,Mobile,Cloud,Web
Services,etc.
DataBatchProcessingasa
Service(DBaaS)– depending
onthenatureofapplication
DataClustering,
Allocation,
Cataloguing&
PruningasaService
Multi‐Cloudasa
Service(MCaaS)
DataTransformationasa
Service(DTaaS)
DataAnalyticsasa
Service(DAtaaS)
DataVisualisation asa
Service(DVaaS)
DataIntegration,
DataAPI,Data
Analysisasa
Service(DIAAaaS)
SEF4CC
REforBD
BDDesign
withSoaML
forBD
Applications
andAdopt
SEF4CC
Mappingto
REF4BD
(Secure
reference
architecture
forBD)
CloudBased
Container
Implementati
on
(Hadoop/Spar
k/Flint)
Tes t,
measure
&deploy
BDAdoption
Framework
BigDataSourceRE
BigDataTransformationRE
BigDataConsumerRE
BigDataCapabilityRE
BigDataSecurity&PrivacyRE
BigDataVisualisation&Analytics RE
BusinessIntelligenceRE(Business
Improvements)
UseBPMmodelling&simulationtovalidate
newdata&businessimprovementservices
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252

4 SOSE4BD BIG DATA
REQUIREMENTS
ENGINEERING EVALUATION
WITH BPMN MODELLING
AND SIMULATION: BIG DATA
FACEBOOK CASE STUDY
Facebook handles trillions of multi-channel data in
real-time, batch processing, real-time analytics, and
response within seconds. Therefore, it needs a high-
performance computing architecture to handle big
data processing (Chen et al., 2016). Facebook is
mainly concerned in measuring performance, fault-
tolerance, correctness, and scalability. Chen et al.
(2016) have reported that Facebook uses their own
big data processing tools such as Puma, Swift, and
Stylus stream processing systems. In this case study,
we have used real-time processing business processes
mapped onto our SOA Based reference architecture
(REF4BD) as shown in Figure 7.
Figure 7: Facebook Big Data Processing with REF4BD.
As shown in the Figure, a snapshot of using Bizagi
Studio for BPMN 2.0 modelling and simulation,
consists of a number of business processes such as
start with 100s of real-time data split by a data
identifier (gateway notation in BPMN) into analytics
data or web service data or real-time streaming data.
This is then processed in our REF4BD data source
layer, and then passed onto other layers in the
reference architecture. The results show a number of
times a particular business service has been accessed
and executed to process that data, and time taken. In
addition, Bizagi BPMN also shows a number of times
each resource has been used such as an API, Data
Scientist (Human Tasks in BPMN), Data Repository,
Servers, Firewall, Data Storage, etc. The results show
by implementing Facebook types of big data
processing into REF4BD is more secure and uses
resources efficiently than suing non-standard
architectures. The efficiency result shows about 95%
use of automated processing by API and Data
Application (Service Components) services.
In conclusions, compared to Chen et al. (2016)
Facebook uses more filters to do real-time streaming
events. We argue that the filters can cause extra-
overheads and resources required whereas REF4BD
is more predictable, and can achieve correctness,
fault-tolerance, and scalability since it is standardised
across all data process applications and services.
5 CONCLUSIONS
SOA has emerged based on established software
design principles of find-request-service paradigm
suitable for service-oriented applications such as big
data processing and analytics. Therefore, it is time to
consider systematic and engineering approach to
developing and deploying big data services as the
data-driven applications and devices increasing
rapidly. In this context, this paper proposed a
software engineering framework and a reference
architecture which is SOA based for big data
applications’ development. This paper also concluded
with a simulation of a complex big data Facebook
application with real-time streaming using BPMN
simulation to study the characteristics before big data
service design, development, and deployment. The
simulation results demonstrated the efficiency and
effectiveness of developing big data applications
using the reference architecture framework for big
data.
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