=Paper=
{{Paper
|id=Vol-2326/paper1
|storemode=property
|title=Extension of iStar for Big Data Projects
|pdfUrl=https://ceur-ws.org/Vol-2326/paper1.pdf
|volume=Vol-2326
|authors=Chabane Djeddi,Nacer Eddine Zarour,Pierre-Jean Charrel
|dblpUrl=https://dblp.org/rec/conf/icaase/DjeddiZC18
}}
==Extension of iStar for Big Data Projects==
https://ceur-ws.org/Vol-2326/paper1.pdf
Extension of iStar for Big data projects
Chabane Djeddi Nacer eddine Zarour Pierre-Jean Charrel
LIRE Laboratory, LIRE Laboratory, IRIT Laboratory,
Constantine 2- A. Mehri Constantine 2- A. Mehri Toulouse 2 Jean Jaurès
University, Algeria University, Algeria University, France
chabane.djeddi@univ- nasro.zarour@univ- charrel@univ-tlse2.fr
constantine2.dz constantine2.dz
[Madd12], [OtPe15], [ShOt16], which make it crucial
and specific.
Abstract Authors in [OtPe15], [ShOt16] confirmed that there
is the necessity for the Big data software to include all
Big data is characterized by the volume, the three parameters (functional feature, time
variety, velocity, and complexity of the data constraint, and verifiable during some period) to
which make it very difficult to handle. On the completely define the requirement specification for Big
other hand, requirements engineering (RE) is data projects. But all of the existing models are not
very important for the success of any software including the constrained time and verifiable time, and
system. As a result, the importance of specify the requirement only in terms of the functional
requirements engineering for Big data projects features. Until now, there is no work to create or to
is evident, but there is no RE method to adapt an existing RE method for Big data projects.
undertake them. We have analyzed the fields The modeling languages are classified into two
of Big data and RE to figure out how the RE classes: (i) Domain Specific modeling language
can allow taking into account the properties of (DSML) to model only one given domain, (ii) General
Big data. This paper presents BIStar which is Purpose Modeling Language (GPML), to model any
an extension of iStar for Big data projects to domain [GCAH18]. iStar [Ref96] is a GPML to
support its properties in the elicitation step of support the elicitation step in RE for any domain. It is
RE process. Our extension undertakes the widely used and adopted by the research
characteristics of Big data, which allow a community[GCAH18].
better elicitation of the requirements and iStar was extended in order to be adopted in many
therefore, it facilitates data analysis. domains like (security, data warehouse, social-technical
Keywords - 1st Big data, 2nd Requirements systems, etc.) [GCAH18]. In our work, we propose
engineering, 3rd iStar, 4th iStar extension BiStar (for Big data iStar) which is a DSML dedicated
to the modeling of requirements for Big data. BiStar is
based essentially on iStar, to not recreate all from
1. Introduction scratch. We extend iStar to undertake the properties of
Compagnies store a large amount of data every day as Big data. Like that, we benefit from the iStar and add to
transactions that are important to them. However, over it what we need and what is specific for Big data.
time, the management of these data by traditional
systems becomes impossible, even regarding in terms 2. Literature reviews
of analysis time, it becomes challenging to guarantee
efficient data processing in a short time. We find In this section, we describe briefly the domains of the
structured data but also semi-structured data and even requirement engineering and the Big data that are
unstructured data. This heterogeneity generates data related to our work.
incompatibility issues that threaten integrity and
consistency. 2.1 Requirements engineering
Big data has its own properties (Volume, Velocity,
The primary criterion for the success of any software is
Variety, Veracity, and Value) [ChML14], [KaWG13],
the degree of satisfaction of the goals fixed by the
Copyright © by the paper’s authors. Copying permitted only for private stakeholders. The requirements engineering (RE) is the
and academic purposes.
process of discovery of these goals [NuEa00].
In: Proceedings of the 3rd Edition of the International Conference on
Advanced Aspects of Software Engineering (ICAASE18), Constantine,
Algeria, 1,2-December-2018, published at http://ceur-ws.org
Page 9
Extension of iStar for Big data projects ICAASE'2018
‘’Requirements engineering is the branch of software product is validated in software life cycle test phase on
engineering concerned with the real-world goals for, the basis of its requirements.
functions of, and constraints on software systems. It is In this work, we are interested in the first step, which is
also concerned with the relationship of these factors to requirement elicitation; because it is indispensable for
precise specifications of software behavior, and to their any RE step and we cannot do any others steps without
evolution over time and across software families." it.
[IeAI97].
The objective of RE is to know the requirements of the 2.1.2 The approaches of RE
stakeholders and to verify them in order to arrive at an
We find in the literature [ZoCo05] that there are three
agreement on the requirements. To fulfill this, we
basics approaches of RE (i) Goal Based Approaches,
perform the activities of elicitation, negotiation,
(ii) Scenarios Based Approaches, (iii) Viewpoints
documentation, validation, and management of the
Based Approaches. These approaches can be modified
requirements. One of the difficult parts in building a
or mixed to create new approaches.
software program is to decide what the software should
The fundamental premise of goal based approaches
exactly do. RE helps us to understand the problem. By
(GORE) is high-level goals. These goals represent
studying the RE specifications precisely, we can even
objectives for the system, they are decomposed (e.g.
estimate the cost of the project. Moreover, RE also
usually using AND and OR relationships) and
helps to know the limits of our system [MiNa11].
elaborated (e.g. with “Why” and “How” questioning)
into sub goals and then further refined, in such a way,
2.1.1 The steps of RE
elementary requirements are elicited [ZoCo05].
RE is usually divided into four steps [KoSo98] (i) Several methods can be considered as belonging to
Requirements elicitation (ii) Requirements analysis and GORE: iStar Framework [Ref96], NFR [ChPr09],
negotiation (iii) Requirements documentation (vi) KAOS [Vanl01]. Among all GORE methods, KAOS
Requirements validation. and iStar have been the most cited [WeOP09]. In our
Requirements elicitation: serves to capture the work, we choose iStar to extend because it is very used
requirements and it is usually divided into five sub- in academic research, and it is properly extensible
steps [ZoCo05], Understanding the application domain, [GCAH18].
Identifying the sources of requirements, Analyzing the Scenarios Based Approaches use narrative and specific
stakeholders, Selecting the techniques, Approaches, descriptions of current and future processes including
and Tools to use, Eliciting the requirements from actions and interactions between the users and the
stakeholders and other sources. system. Like use cases, scenarios do not typically
Requirements analysis and negotiation: focuses on the consider the internal structure of the system, and
review, understanding of the elicited requirements and require an incremental and interactive approach to their
their verification for quality in terms of accuracy, development. Naturally, it is important when using
completeness, clarity, and consistency. scenarios to collect all the potential exceptions for each
Requirements documentation: we document the step [ZoCo05].
requirements obtained from previous steps. Viewpoints Based Approaches: aim to model the
Requirements document can be considered as a base for domain from different perspectives in order to develop
controlling changes and evaluating future products and a complete and consistent description of the target
processes (system design, system test cases and system [ZoCo05]. Initially, the requirements are
validation) [MiNa11]. opaque, informal and only expressed through personal
Requirements validation: It is done for controlling the views. These views reflect the skills, objectives and
quality. it means confirming that requirements are roles of each participant. The elicitation activity is,
complete and well- written and supply needs of therefore, a collective activity. The expression of
customer. This step may continue repeating other multiple views allows for better elicitation of
requirements development phases because of identified requirements.
deficiencies, gap between requirements, additional
information and other issues. Implemented software
International Conference on Advanced Aspects of Software Engineering Page 10
ICAASE, December, 01-02, 2018
Extension of iStar for Big data projects ICAASE'2018
2.2 Big data The Complexity: This is how to ensure the correlation
and the links between the data, because the latter in a
In this section, we will present the Big data passing
Big data is collected from several heterogeneous
briefly through its definitions, properties, and
sources, and that is very important to guarantee the
importance.
integrity of the data, and not to be found in
There is no exact definition of Big data even though
unmanageable situations [KaWG13].
several definitions have appeared. Big data means a
large dataset that cannot be processed by traditional
2.2.2 The importance of Big data
tools [ChML14]. Big data can be seen from several
perspectives, (i) on the infrastructure perspective Big Big data have great importance in a lot of fields (the
data is seen as a significant amount of data industry, risk analysis, social networks).
characterized by (Volume, Velocity, Variety, Veracity, In the industry: Companies store a large amount of data
and Value), (ii) on the analysis perspective Big data is every day as transactions that are important to them.
seen as events, (iii) on the business perspective Big However, over time, the management of these data by
data can be considered as the output that can be used traditional systems becomes impossible. Traditional
directly for the improvement of the work [OtPe15], systems cannot support a large amount of data, here we
[ShOt16]. The most crucial problem is not how to store can clearly see the utility of Big data for businesses,
data, but rather to analyze heterogeneous data in a short because it allows them to store a significant amount of
time [Madd12]. data for a long time [KaWG13].
There is a solid relationship between Big data and other In risk analysis: Many companies use their data to
technologies such as Cloud and IoT. Cloud can be an calculate risk. Without Big data technologies, they use
infrastructure for Big data, and IoT is considered the small amounts. With their arrival, it becomes possible
most massive source of Big data [ChML14]. to analyze a large amount of data, which allows better
Consequently, our contribution in Big data will risk management [KaWG13].
influence other technologies. In social networks: the most common use of Big data is
in the areas of social networks and user preferences.
2.2.1 The properties of Big data Social networks use a large amount of data collected
from user reviews and choices. That way, they can
The Variety: the data manipulated today are not from a
analyze the data and make it known that they are the
single representation, we have structured data, but also
preferences of the users in a short time, in order to
we have semi-structured data and even unstructured
improve their products and to change their decisions to
data such as web pages, social networks, making it very
have a good position in the market [KaWG13].
difficult to manipulate these data using traditional
systems [ChML14], [KaWG13].
The volume : the name itself in the word Big data 3. Case study
means that volume takes an important role in the We have chosen to present the case study in this section
creation of the Big data concept since the data handled in order to be able to use it in the modeling with iStar
today are in quantity of zettabytes at most large and BiStar (iStar extended) that we propose in the
companies, this is of course, one of the limitations of following sections. This example will accompany us
traditional systems [ChML14], [KaWG13]. throughout the paper.
The velocity: the speed of incoming data from various We will take an example of the presidential elections of
sources is so critical, which make it difficult for the 2019 in Algeria. The community of a camp wants to
traditional systems to undertake the situation increase the chance of success of its candidate. For that,
[KaWG13]. they want to create a Big data project to study the
The value: the stored data is important. A user can opinions of the people, which allow them to know the
execute some queries against stored data or may misuse keys for which they can focus in order to lunch targeted
existing data, and this can cause false results for advertisements to improve the chances of success of
decision makers [KaWG13]. their candidate.
International Conference on Advanced Aspects of Software Engineering Page 11
ICAASE, December, 01-02, 2018
Extension of iStar for Big data projects ICAASE'2018
To do this, they collect data from social networks, and to develop‘’ to launch targeted advertising. ‘’Social
analyze them to know the essentials points in the networks‘’ depend on the ‘’elector‘’ to collect
opinion of the different categories of population. On information about their preferences. The ‘’system to be
these points, they make a presidential plan and present developed‘’ depends on the ‘’social networks‘’ to
it to the people. After that, they collect the opinion of receive the elector information resource.
people to make changes to the plan and make targeted The ‘’advertising manager‘’ depends on the ‘’system
advertising. to be developed‘’ to provide the summary information
This example is a Big data project, because we will on electors. The ‘’system to be developed‘’ depends on
manipulate a large amount of data with different the ‘’advertising manager‘’ to accomplish the goal of
natures (structured, semi-structured and even developing targeted advertising.
unstructured) within a limited time. Therefore, these
data cannot be processed using traditional systems. 4.2 The Strategic Rationale (SR) Model
The Strategic Rationale (SR) Model is used to detail
4. iStar the reasoning of each actor apart. We represent what
In this session, we explain the iStar method, as well as happens inside an actor, which allows a deep
their diagrams. iStar [DaFH16], [I*wi00], [Ref96] is a understanding of the process.
goal-oriented RE method, it is very used for Figure [2] shows the application of the strategic
requirements elicitation. We first start with the rationale model on the case of study of the presidential
identification of the actors and the relations of the elections.
strategic dependencies between them, and then we
detail the reasoning of each actor. It consists of two
models: The Strategic Dependency (SD) Model, and
The Strategic Rationale (SR) Model.
4.1 The Strategic Dependency (SD) Model
The strategic dependency model represents a network
of strategic dependencies between the different actors
of the future system. One actor (the dependee) depends
on another one (the depender) to accomplish a goal.
There are nodes and links between them, the nodes
represent the actors, and the links represent the
dependencies. There are four types of dependencies, (i)
Goal dependency serves to present a dependency to
accomplish a goal, (ii) Task dependency serves to
present a task dependency between two actors, (iii)
Resource dependency serves to present a resource
dependency, (the depender) depends on (the dependee)
to offer it a resource, (iv) Softgoal dependency serves
to present a dependency of performance between two
actors.
Figure [1] represents the application of the strategic
dependency (SD) model of the iStar method on the case
study of presidential elections. The ‘’candidate‘’
Figure 1 : Strategic dependency (SD) model for
depends on the ‘’elector‘’ for the goal of winning the
elections
elections. The ‘’system to be developed‘’ depends on
the ‘’candidate‘’ to accomplish the task of offering him
its information. The ‘’elector‘’ depends on the ‘’system
International Conference on Advanced Aspects of Software Engineering Page 12
ICAASE, December, 01-02, 2018
Extension of iStar for Big data projects ICAASE'2018
Big data project must not only meet a need, but also
respond in a very short time by processing a big amount
and a specific nature of data (structured, semi-
structured, unstructured).
During the RE phase for Big data projects, we are
interested in what to collect rather than how to collect
because current techniques and approaches of RE are
valid for Big data projects. Big data projects are like
traditional projects on how to collect requirements. It is
on the Big data properties (Volume, Velocity, Variety)
that we are going to focus to collect and model them in
the RE phase by BiStar.
Also, The papers [OtPe15], [ShOt16] confirmed that
there is the necessity for the Big data software to
include all the three parameters (functional feature,
time constraint, verifiable during some period) to
completely define the requirement specification for Big
data projects .
5.2. The Concepts added to iStar
Based on the needs of requirements for Big data in the
literature [ChML14], [KaWG13], [Madd12], [tPe15],
[ShOt16], we have chosen to add the concepts of
execution time, volume of data to process, variety of
data, and durability of a goal. In the rest of this
subsection, we will explain each concept and clarify
why we added it.
Figure 2 : Strategic rationale model for elections
5.2.1. The execution time
5. BiStar: An extension of iStar for Big data In a Big data project, the execution time must be exact.
projects A late result is considered a wrong one.
We take the case study of presidential elections
In this session, we present BiStar (Big data iStar) which
presented in section 3. The stakeholder needs the goal
consists of an extension of the iStar method for Big
‘’Generate information synthesized on the profiles of
data projects. We start with clarifying the needs for an
electors‘’, and does not specify in what time it should
extension of iStar to support elicitation of the
be performed. The project will well be done and
requirements for Big data projects; then we explain the
finished. But the goal must be achieved in 15 days. So
concepts to add, after that, we perform the BiStar on
the project has failed to satisfy the stakeholder’s need.
the case of study of the presidential elections.
We conclude that the execution time of each goal must
be specified at the beginning of the project.
5.1. The needs for an extension of iStar
In this part, we explain the situation and the important 5.2.2. The volume of data to process
points that we find them as critical ones.
The volume of data is one of the most important
The Elicitation is the most crucial step in RE, if it is not
features of Big data projects, the volume is often large,
well done can lead to projects that do not respond well
but stakeholders are not aware of what can be done and
to the needs of stakeholders. In the case of Big data
what cannot be done. Even using Big data technologies
projects, it is getting more and more complicated. A
International Conference on Advanced Aspects of Software Engineering Page 13
ICAASE, December, 01-02, 2018
Extension of iStar for Big data projects ICAASE'2018
like (Hadoop and nosql systems), volume remains a
crucial point when talking about Zettabytes [KaWG13].
In the case study of presidential elections presented in
section 3, the stakeholder needs the goal ‘’Generate
information synthesized on the profiles of electors‘’,
and does not specify volume of data that must be
proceeded. However the goal needs to analyse 100
Zettabytes of data. So the project has failed. The
volume of data of each goal must also be specified at
the beginning of the project.
5.2.3. The variety of data Figure 3 : Concepts added to BiStar
In Big data projects, we find data with different
presentations (structured, semi-structured data, and
unstructured data). Building a Big data Project that 5.3. The application of BiStar on the case study
manipulates semi-structured data is different from Figure [4] shows the application of BiStar strategic
unstructured data. dependency model on the example of presidential
In the above example (see section 3), the stakeholder elections.
does not specify the nature of the data that must be
proceed. The goal needs to analyze semi-structured and
unstructured data. Consequently, the nature of data of
each goal must be also specified at the beginning of the
project.
5.2.4. The durability of a goal
Big data projects are built to meet the needs during
specified times; it turns out that their goals may become
dissatisfied for stakeholders, so we need to get an
agreement from the beginning on the time in which a
requirement can be satisfied.
In the case study considered in section3, the
stakeholder does not specify the durability of its goal.
When we validate the project with the stakeholder, he
says it is not what he wants; the goal must be satisfied
during the hall election. So the project failed to satisfy
the need of the stakeholder. Also, the durability of a
goal must be specified at the beginning of the project.
iStar does not support the properties presented above,
which do not allow a complete and refined elicitation of
the requirements for Big data. We see that to support
Big data projects by the iStar method; we must make
sure that the goals are attached to their properties
(execution time, the volume of data to be processed, the
variety of data, and the durability of goal).
Figure [3] shows graphically the concepts added to the
Strategic Dependency (SD) Model, and The Strategic
Figure 4 : Strategic Dependency model of BiStar for
Rationale (SR) Model.
the elections
International Conference on Advanced Aspects of Software Engineering Page 14
ICAASE, December, 01-02, 2018
Extension of iStar for Big data projects ICAASE'2018
We keep the same meaning explained in section 4.1.
However, we find in BiStar that new concepts are
linked to the goal ''Develop targeted advertising'' which
means that this goal must be done within 10 days, by
analyzing 100 Zettabytes of unstructured and semi-
structured data nature, and it must be in operation
during the elections. Like that we give more
completeness and refinement to the requirements.
Figure [5] shows the application of BiStar's strategic
rationale model on the example of presidential
elections. We keep the same meaning explained in
section 4.2, but also, we find in BiStar that new
concepts are linked to the goal ''Design an election
program'' and to the goal ''Generate synthesized
information about the profile of electors''. We
understand that the goal "Designe an Election
Program" must be done within 2 days, by analyzing 30
unstructured and semi-structured nature Petabytes and
it must be functional during the elections. And for the
goal ''Generate information synthesized on the profiles
of electors'', it must be done within 15 days, by
analyzing 100 Zettabytes of unstructured and semi-
structured nature, and it must be functional during the
elections.
6. Conclusion
In this work, we have proposed BiStar (Big data iStar)
a new extension of iStar to elicit the requirements for
Big data projects. This extension takes into account the
properties of Big data projects to ensure a proper Figure 5: Strategic Rationale model of BiStar for
elicitation of the requirements. elections
We applied iStar and BiStar on the same case study of
the presidential elections to show the utility of BiStar.
We can note that without BiStar we can mess some 7. References
important requirements. This modest research was the
first attempt to feel the gap in the field of the adaptation [ChML14] CHEN, MIN ; MAO, SHIWEN ; LIU, YUNHAO: Big
of RE methods for Big data projects. Data: A Survey. In: Mobile Networks and
We are completing this work by applying the rest of the Applications Bd. 19 (2014), Nr. 2, S. 171–
life cycle activities of RE (specification, validation...). 209
We hope that the research community gives more
attention to this field.
International Conference on Advanced Aspects of Software Engineering Page 15
ICAASE, December, 01-02, 2018
Extension of iStar for Big data projects ICAASE'2018
[ChPr09] CHUNG, LAWRENCE ; DO PRADO LEITE, JULIO Requirement Engineering. In: Interaction
CESAR SAMPAIO: On non-functional Sciences (ICIS), 2011 4th International
requirements in software engineering. Conference on : IEEE, 2011, S. 181–184
In: Conceptual modeling: Foundations
and applications : Springer, 2009, S. 363–
379
[NuEa00] NUSEIBEH, BASHAR ; EASTERBROOK, STEVE:
[DaFH16] DALPIAZ, FABIANO ; FRANCH, XAVIER ; Requirements engineering: a roadmap.
HORKOFF, JENNIFER: istar 2.0 language In: Proceedings of the Conference on the
guide. In: arXiv preprint Future of Software Engineering : ACM,
arXiv:1605.07767 (2016) 2000, S. 35–46
[GCAH18] GONÇALVES, ENYO ; CASTRO, JAELSON ; [OtPe15] OTERO, CARLOS E. ; PETER, ADRIAN: Research
ARAÚJO, JOÃO ; HEINECK, TIAGO: A Directions for Engineering Big Data
Systematic Literature Review of iStar Analytics Software. In: IEEE Intelligent
extensions. In: Journal of Systems and Systems Bd. 30 (2015), Nr. 1, S. 13–19
Software Bd. 137 (2018), S. 1–33
[Ref96] ERIC SIU-KWONG YU.: Modelling strategic
[IeAI97] IEEE COMPUTER SOCIETY ; ACM SIGSOFT ; IFIP relationships for process reengineering,
WORKING GROUP 2.9 (Hrsg.): Classification University of Toronto, PhD Thesis, 1996
of Research Efforts in Requirements
Engineering. Los Alamitos, Calif : IEEE [ShOt16] SHARMA, KAPIL ; OTHERS: Quality issues
Computer Society Press, 1997 — with big data analytics. In: Computing for
ISBN 978-0-8186-7740-3 Sustainable Global Development
(INDIACom), 2016 3rd International
[I*wi00] i* Wiki | i* Guide. URL http://istar.rwth- Conference on : IEEE, 2016, S. 3589–3591
aachen.de/tiki-
index.php?page=i*+Guide. - abgerufen [Vanl01] VAN LAMSWEERDE, AXEL: Goal-oriented
am 2017-12-05 requirements engineering: A guided
tour. In: Requirements Engineering,
[KaWG13] KATAL, AVITA ; WAZID, MOHAMMAD ; 2001. Proceedings. Fifth IEEE
GOUDAR, R. H.: Big data: issues, International Symposium on : IEEE, 2001,
challenges, tools and good practices. In: S. 249–262
Contemporary Computing (IC3), 2013
Sixth International Conference on : IEEE, [WeOP09] WERNECK, VERA MARIA BENJAMIM ; OLIVEIRA,
2013, S. 404–409 ANTONIO DE PADUA ALBUQUERQUE ; DO PRADO
LEITE, JULIO CESAR SAMPAIO: Comparing
[KoSo98] KOTONYA, GERALD ; SOMMERVILLE, IAN: GORE Frameworks: i-star and KAOS. In:
Requirements engineering: processes WER, 2009
and techniques : Wiley Publishing, 1998
[ZoCo05] ZOWGHI, DIDAR ; COULIN, CHAD:
[Madd12] MADDEN, SAM: From databases to big Requirements elicitation: A survey of
data. In: IEEE Internet Computing Bd. 16 techniques, approaches, and tools. In:
(2012), Nr. 3, S. 4–6 Engineering and managing software
requirements : Springer, 2005, S. 19–46
[MiNa11] MINA ATTARHA ; NASSER MODIRI: Focusing
on the Importance and the Role of
International Conference on Advanced Aspects of Software Engineering Page 16
ICAASE, December, 01-02, 2018