-
Journal of Computer and Communications, 2020, 08, 1-16
http://www.scirp.org/journal/jccISSN Online:2327-5227ISSN
Print:2327-5219
A Methodological Approach to Model CBR-basedSystems
Eliseu M. Oliveira1, Rafael F. Reale12 and Joberto S. B.
Martins1
1Universidade Salvador - UNIFACS, Salvador, Brazil2Instituto
Federal da Bahia - IFBA, Valena, BrazilEmail: [email protected],
[email protected], [email protected]
How to cite this paper: Oliveira,M. E.; Reale, R. F.; Martins,
J.S. B. (2020) A Methodological Ap-proach to Model CBR-based
Sys-tems, Journal of Computer andCommunications, 08,
1-16.https://dx.doi.org/10.4236/jcc.2020.89001.2020.89001
Received: July 22, 2020
Accepted: September 01, 2020
Published: September 04, 2020
Copyright c© 2020 by author(s) andScientific Research Publishing
Inc.This work is licensed under the Cre-ative Commons Attribution
Inter-national License (CC BY
4.0).http://creativecommons.org/licenses/by/4.0/
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fringilla id, consectetuer vel,pede.Nulla malesuada risus ut urna.
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quis tortor. Donec egestasornare nulla. Mauris mi tellus,porta
faucibus, dictum vel, non-ummy in, est. Aliquam erat vo-lutpat. In
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vulpu-tate orci. Donec et velit ac arcu in-terdum semper. Morbi
pede orci,cursus ac, elementum non, vehic-ula ut, lacus. Cras
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odio. Quisqueposuere purus ac orci. Vivamusodio. Vivamus varius,
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vestibulumneque arcu eu tortor. Donec iac-ulis tincidunt tellus.
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viverra et, adip-iscing nec, dolor. Praesent lacusmauris, dapibus
vitae, sollicitudinsit amet, nonummy eget, ligula.Cras egestas
ipsum a nisl. Viva-mus varius dolor ut dolor. Fuscevel enim.
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facilisis facilisis. Etiam nislelit, cursus sed, fringilla in,
conguenec, urna. Cum sociis natoque pe-natibus et magnis dis
parturientmontes, nascetur ridiculus mus. In-teger at turpis. Cum
sociis na-toque penatibus et magnis dis par-turient montes,
nascetur ridiculusmus. Duis fringilla, ligula sed portafringilla,
ligula wisi commodo felis,ut adipiscing felis dui in enim.
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sitamet urna. Nulla volutpat ali-quet tortor. Cras aliquam,
tellusat aliquet pellentesque, justo sapiencommodo leo, id rhoncus
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orci orcialiquam orci, ut cursus turpis justoet lacus. Nulla vel
tortor. Quisqueerat elit, viverra sit amet, sagittiseget, porta sit
amet, lacus.In hac habitasse platea dictumst.Proin at est.
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sapien. Duis elitmagna, aliquet at, tempus sed, ve-hicula non,
enim. Morbi viverraarcu nec purus. Vivamus fringilla,enim et
commodo malesuada, tor-tor metus elementum ligula, nec ali-quet est
sapien ut lectus. Aliquammi. Ut nec elit. Fusce euismod luc-tus
tellus. Curabitur scelerisque.Nullam purus. Nam ultricies ac-cumsan
magna. Morbi pulvinarlorem sit amet ipsum. Donecut justo vitae nibh
mollis congue.Fusce quis diam. Praesent tempuseros ut quam.Donec in
nisl. Fusce vitae est. Vi-vamus ante ante, mattis laoreet, po-suere
eget, congue vel, nunc. Fuscesem. Nam vel orci eu eros
viverraluctus. Pellentesque sit amet au-gue. Nunc sit amet ipsum et
lacusvarius nonummy. Integer rutrumsem eget wisi. Aenean eu
sapien.Quisque ornare dignissim mi. Duisa urna vel risus pharetra
imperdiet.Suspendisse potenti.Morbi justo. Aenean nec dolor.In hac
habitasse platea dictumst.Proin nonummy porttitor velit. Sedsit
amet leo nec metus rhoncus var-ius. Cras ante. Vestibulum com-modo
sem tincidunt massa. Namjusto. Aenean luctus, felis et condi-mentum
lacinia, lectus enim pulv-inar purus, non porta velit nisl sederos.
Suspendisse consequat. Mau-ris a dui et tortor mattis pretium.Sed
nulla metus, volutpat id, ali-quam eget, ullamcorper ut,
ipsum.Morbi eu nunc. Praesent pretium.Duis aliquam pulvinar ligula.
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pede.Vivamus sodales elementum neque.Vivamus dignissim
accumsanneque. Sed at enim. Vestibulumnonummy interdum purus.
Maurisornare velit id nibh pretium ul-tricies. Fusce tempor
pellentesqueodio. Vivamus augue purus, laoreetin, scelerisque vel,
commodo id,wisi. Duis enim. Nulla interdum,nunc eu semper eleifend,
enim dolorpretium elit, ut commodo ligulanisl a est. Vivamus ante.
Nulla leomassa, posuere nec, volutpat vitae,rhoncus eu,
magna.Quisque facilisis auctor sapien. Pel-lentesque gravida
hendrerit lec-tus. Mauris rutrum sodales sapien.Fusce hendrerit sem
vel lorem. In-teger pellentesque massa vel au-gue. Integer elit
tortor, feugiatquis, sagittis et, ornare non, la-cus. Vestibulum
posuere pellen-tesque eros. Quisque venenatis ip-sum dictum nulla.
Aliquam quisquam non metus eleifend interdum.Nam eget sapien ac
mauris male-suada adipiscing. Etiam eleifendneque sed quam. Nulla
facilisi.Proin a ligula. Sed id dui eunibh egestas tincidunt.
Suspendissearcu.Maecenas dui. Aliquam volutpatauctor lorem. Cras
placerat est vi-tae lectus. Curabitur massa lec-tus, rutrum
euismod, dignissim ut,dapibus a, odio. Ut eros erat,vulputate ut,
interdum non, portaeu, erat. Cras fermentum, felis inporta congue,
velit leo facilisis odio,vitae consectetuer lorem quam vi-tae orci.
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posuere nibh lacus necnibh. Maecenas laoreet dolor atenim. Donec
molestie dolor nec me-tus. Vestibulum libero. Sed quiserat. Sed
tristique. Duis pede leo,fermentum quis, consectetuer
eget,vulputate sit amet, erat.Donec vitae velit. Suspendisseporta
fermentum mauris. Ut velnunc non mauris pharetra varius.Duis
consequat libero quis urna.Maecenas at ante. Vivamus var-ius, wisi
sed egestas tristique, odiowisi luctus nulla, lobortis dictumdolor
ligula in lacus. Vivamus ali-quam, urna sed interdum
porttitor,metus orci interdum odio, sit ameteuismod lectus felis et
leo. Prae-sent ac wisi. Nam suscipit vestibu-lum sem. Praesent eu
ipsum vi-tae pede cursus venenatis. Duis sedodio. Vestibulum
eleifend. Nulla utmassa. Proin rutrum mattis sapien.Curabitur
dictum gravida ante.Phasellus placerat vulputate quam.Maecenas at
tellus. Pellentesqueneque diam, dignissim ac, venenatisvitae,
consequat ut, lacus. Namnibh. Vestibulum fringilla arcumollis arcu.
Sed et turpis. Donecsem tellus, volutpat et, varius eu,commodo sed,
lectus. Lorem ip-sum dolor sit amet, consectetueradipiscing elit.
Quisque enim arcu,suscipit nec, tempus at, imperdietvel, metus.
Morbi volutpat purusat erat. Donec dignissim, sem idsemper tempus,
nibh massa eleifendturpis, sed pellentesque wisi purussed libero.
Nullam lobortis tor-tor vel risus. Pellentesque conse-quat nulla eu
tellus. Donec velit.Aliquam fermentum, wisi ac rhon-cus iaculis,
tellus nunc malesuadaorci, quis volutpat dui magna id mi.Nunc vel
ante. Duis vitae lacus.Cras nec ipsum.Morbi nunc. Aliquam
consectetuervarius nulla. Phasellus eros. Crasdapibus porttitor
risus. Maece-nas ultrices mi sed diam. Prae-sent gravida velit at
elit vehiculaporttitor. Phasellus nisl mi, sagit-tis ac, pulvinar
id, gravida sit amet,erat. Vestibulum est. Lorem ipsumdolor sit
amet, consectetuer adip-iscing elit. Curabitur id sem ele-mentum
leo rutrum hendrerit. Utat mi. Donec tincidunt faucibusmassa. Sed
turpis quam, sollici-tudin a, hendrerit eget, pretium ut,nisl. Duis
hendrerit ligula. Nuncpulvinar congue urna.Nunc velit. Nullam elit
sapien,eleifend eu, commodo nec, sem-per sit amet, elit. Nulla
lectusrisus, condimentum ut, laoreet eget,viverra nec, odio. Proin
lobor-tis. Curabitur dictum arcu velwisi. Cras id nulla venenatis
tor-tor congue ultrices. Pellentesqueeget pede. Sed eleifend
sagittiselit. Nam sed tellus sit amet lec-tus ullamcorper
tristique. Maurisenim sem, tristique eu, accumsanat, scelerisque
vulputate, neque.Quisque lacus. Donec et ipsum sitamet elit nonummy
aliquet. Sedviverra nisl at sem. Nam diam.Mauris ut dolor.
Curabitur ornaretortor cursus velit.Morbi tincidunt posuere arcu.
Crasvenenatis est vitae dolor. Viva-mus scelerisque semper mi.
Donecipsum arcu, consequat scelerisque,viverra id, dictum at,
metus.Lorem ipsum dolor sit amet, con-sectetuer adipiscing elit. Ut
pedesem, tempus ut, porttitor biben-dum, molestie eu, elit.
Suspendissepotenti. Sed id lectus sit ametpurus faucibus vehicula.
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magna.Aenean laoreet aliquam orci.Nunc interdum elementum
urna.Quisque erat. Nullam temporneque. Maecenas velit
nibh,scelerisque a, consequat ut, viverrain, enim. Duis magna.
Donecodio neque, tristique et, tincidunteu, rhoncus ac, nunc.
Maurismalesuada malesuada elit. Etiamlacus mauris, pretium vel,
blanditin, ultricies id, libero. Phasellusbibendum erat ut diam. In
congueimperdiet lectus.Aenean scelerisque. Fusce pretiumporttitor
lorem. In hac habitasseplatea dictumst. Nulla sit amet nislat
sapien egestas pretium. Nuncnon tellus. Vivamus aliquet.
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ip-sum. Quisque tincidunt auctor au-gue. Nunc imperdiet ipsum
egetelit. Aliquam quam leo, con-sectetuer non, ornare sit amet,
tris-tique quis, felis. Vestibulum anteipsum primis in faucibus
orci luc-tus et ultrices posuere cubilia Cu-rae; Pellentesque
interdum quam sitamet mi. Pellentesque mauris dui,dictum a,
adipiscing ac, fermentumsit amet, lorem.Ut quis wisi. Praesent quis
massa.Vivamus egestas risus eget lacus.Nunc tincidunt, risus quis
biben-dum facilisis, lorem purus rutrumneque, nec porta tortor urna
quisorci. Aenean aliquet, libero sempervolutpat luctus, pede erat
laciniaaugue, quis rutrum sem ipsum sitamet pede. Vestibulum
aliquet,nibh sed iaculis sagittis, odio do-lor blandit augue, eget
mollis urnatellus id tellus. Aenean aliquet ali-quam nunc. Nulla
ultricies justoeget orci. Phasellus tristique fer-mentum leo. Sed
massa metus,sagittis ut, semper ut, pharetra vel,erat. Aliquam quam
turpis, eges-tas vel, elementum in, egestas sitamet, lorem. Duis
convallis, wisi sitamet mollis molestie, libero maurisporta dui,
vitae aliquam arcu turpisac sem. Aliquam aliquet
dapibusmetus.Vivamus commodo eros eleifenddui. Vestibulum in leo eu
erat tris-tique mattis. Cras at elit. Cras pel-lentesque. Nullam id
lacus sit ametlibero aliquet hendrerit. Proin plac-erat, mi non
elementum laoreet,eros elit tincidunt magna, a rhoncussem arcu id
odio. Nulla eget leo aleo egestas facilisis. Curabitur quisvelit.
Phasellus aliquam, tortor necornare rhoncus, purus urna
posuerevelit, et commodo risus tellus quistellus. Vivamus leo
turpis, tem-pus sit amet, tristique vitae, laoreetquis, odio. Proin
scelerisque biben-dum ipsum. Etiam nisl. Praesentvel dolor.
Pellentesque vel magna.Curabitur urna. Vivamus congueurna in velit.
Etiam ullamcorper el-ementum dui. Praesent non urna.Sed placerat
quam non mi. Pellen-tesque diam magna, ultricies eget,ultrices
placerat, adipiscing rutrum,sem.
Abstract
Artificial intelligence (AI) has been used in various areas to
supportsystem optimization and find solutions where the complexity
makes itchallenging to use algorithmic and heuristics. Case-based
Reasoning(CBR) is an AI technique intensively exploited in domains
like man-agement, medicine, design, construction, retail and smart
grid. CBRis a technique for problem-solving and captures new
knowledge by us-ing past experiences. One of the main CBR
deployment challenges isthe target system modeling process. This
paper presents a straight-forward methodological approach to model
CBR-based applications us-ing the concepts of abstract and concrete
models. Splitting themodeling process with two models facilitates
the allocation of exper-tise between the application domain and the
CBR technology. Themethodological approach intends to facilitate
the CBR modeling pro-cess and to foster CBR use in various areas
outside computer science.
Keywords
Artificial Intelligence; Case-based Reasoning; CBR Modeling;
Bandwidth Allo-cation Model.
1. Introduction
Artificial Intelligence (AI) and Machine Learning (ML)
techniques are be-ing extensively used in an ever-increasing number
of areas and systems.They provide benefits on adopting them, such
as efficient optimizationmethods and the possibility to solve
rather complex multi-objective andmulti-constrained problems that
were difficult or eventually impossible to
DOI: 10.4236/jcc.2020.89001 Sep. 04, 2020
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http://www.scirp.org/journal/jcchttps://dx.doi.org/10.4236/jcc.2020.89001
.2020.89001https://dx.doi.org/10.4236/jcc.2020.89001
.2020.89001http://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by/4.0/10.4236/jcc.2020.89001.2020.89001
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E. M. Oliveira et al
solve with current algorithmic or heuristics solutions [1].
ML-assisted applications are a trend, and many researchers and
devel-opers are rushing to apply ML and recover their inherent
potential benefits[2] [3].
However, using ML techniques to solve any problem do require
someprevious background and expertise. For example, it is vital to
choose theML technique that better suits the target application in
terms of availablecomputational capability and expected target
results. In sequence to anadequate ML technique choice, it is
typically necessary to model the prob-lem under the premises of the
chosen technique. The modeling processmay include, as an example,
an MDP-based markovian process (MarkovDecision Process) like
Q-Learning or SARSA formulation for Reinforce-ment Learning or the
definition of a neural network structure for NeuralNetworks (NN)
[4] [5].
Case-based Reasoning (CBR) [6] is a technique for
problem-solving andfor capturing new knowledge (learning) based on
the stored knowledge ofpast experiences. CBR paradigm has a base of
past experiences, called acase-base, and attempts to solve new
problems by recovering similar solu-tions in this database and
adapting them to new problems. CBR, to someextent, mimics the human
behavior in activities like management and di-agnostics in which
the previous knowledge and experience is the driver inlooking for
the solutions for new near-equivalent problems [7] [8].
CBR was proposed more than a decade ago as an AI technique [6],
issimple to use, has minimal learning requirements, and does not
typicallyrequire intensive computational resources [9]. More
recently, CBR receivesthe attention from the artificial
intelligence community and is gaining trackin domain areas like
medicine, expert systems, retail, smart grid, construc-tion,
manufacturing, design, agriculture and management [10] [11] [12]
[13][14] [15].
Like other AI techniques, CBR requires the target system to be
mod-eled to allow a similarity search in its database. This process
is not clearlydetailed or methodologically described in the
literature. Our approach pro-vides a way to methodologically model
the target system. In our approach,the CBR modeling for
problem-solving requires a specialized abstract modelof the target
system and, derived from it, a concrete CBR representationof the
variables and parameters involved in the process.
The objective of this work is, in summary, to propose a
methodologi-cal approach to model the CBR process based on a
mapping between theabstract and concrete representations of the CBR
process variables andparameters. The proposed method aims to
facilitate the CBR modelingprocess and contribute to promoting the
widespread use of CBR. We alsoexpect the contribution can be
relevant to CBR application areas wherethe computer science
expertise of the professionals involved is less frequentor even
unavailable.
The remaining of this paper is organized as follows. Section 2
presentsan overview of the CBR fundamentals and section 3 discusses
the relatedworks. Sections 4 and 5 present the CBR modeling
methodology and sec-tion 7 follows with an example of how to use
the approach for the cognitivemanagement of bandwidth in network
links. In section 8, the final consid-erations are presented.
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2. Case-based Reasoning Fundamental Aspects
In summary, in CBR, a new problem is solved through the
knowledge andinformation available or acquired by a previous
similar problem adapted tocreate a new solution.
The essential components used to solve a problem with CBR are
acase, the case-database of past cases or past experiences, and the
similarityfunction.
The case is the way we represent the experience we have about
thetarget problem. All previous cases representing the acquired
knowledgeare stored in a database, called case-database. A case is
represented by apair problem and solution that are the fundamental
aspects present in allCBR systems:
• Problem: It contains all the information regarding the past
event thatyou want to remember. That is, it describes all the
essential datafor the representation of knowledge in the specific
domain. Thesedata can include contextualization data, application
objectives, de-scriptions of what happened, qualitative data, and
quantitative data,among others.
• Solution: It presents the necessary information to solve the
problemrelated to the past event. This solution can be any
information oraction that totally or partially solves the problem
presented in thecase description. The representation of the
solution must always takeinto account the application domain.
The execution of the CBR process includes 4 phases using its
essentialcomponents, namely (Figure 1) [16] [17]:
• Recover : Performs the search for similar cases in the
case-database.This comparison is made using the similarity
function. The similarityfunction is responsible for comparing the
base cases with the actualproblem and should return the most
similar cases found.
• Reuse: The phase in which the description of the current
problemis composed with the solution of the case recovered in the
previousphase. Then the solution found is applied to the
environment inquestion.
• Revision: In the review phase, the specialist in the
technological do-main must assess whether the solution employed
brings the expectedresults. The specialist has the opportunity to
make fine adjustmentsto optimize, adjust, or adapt the recommended
solution.
• Retention: After making the necessary adaptations, the
specialistmust confirm the new case as a valid case and
consequently save it inthe case base for later reuse.
From the methodological point of view, the utilization CBR to
solve aparticular problem involves the following modeling and
operational steps:
• Case and Knowledge Representation: The case components and
itsstructure embed the experience and the knowledge for a
particularproblem. As such, case descriptions document real
experiences, andthe CBR system may acquire new knowledge by
retaining new cases.
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Figure 1: CBR 4R Cycle [16]
• Similarity Measure: A similarity function calculates the
similaritymeasure, and it defines to what extent a case in the
case-database issimilar to the case being processed.
• Adaptation: In CBR, the operational adaptation step takes a
similarcase and adapts it to the current situation. In general,
adaptationuses a defined mechanism, expert knowledge, or a mix of
both.
• Learning : It is an operational step that allows the CBR
system tomemorize its successful and unsuccessful solutions, which
means ef-fectively to acquire new knowledge.
The foundation of the CBR that supports its capability to adapt,
learn,and retain knowledge are the case representation and the
choice of similaritymeasures. Accurately modeling these two
elements of the CBR operationis essential.
In this paper, we present a methodological approach that
addresses therepresentation of cases and the choice of similarity
measures for the problemof resource management with CBR.
The proposed methodological approach is composed of an
AbstractModel (AM) and a Concrete Model (CM). The abstract model is
a high-level representation that structures the knowledge
corresponding to thescope of actuation. The concrete model maps the
abstract model’s repre-sentation to the set of parameters used in
the CBR execution process toacquire knowledge for a set of
particular cases.
3. Related Work
Watson presents a discussion about the methodological approach
used byCBR in [18]. Watson argues that CBR can be better described
as a method-ology for problem-solving and, as such, differs from
other artificial intelli-gence techniques like neural networks, and
genetic algorithms that use moreformal mathematical methods.
Watson’s paper does not discuss or proposea methodological approach
to support CBR deployments.
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The case representation formalism is discussed in Martines [19].
The au-thors discuss how experiences can be represented by using
since simple fea-ture vectors to representational formalisms like
object-oriented, predicate-based and semantic nets, among
others.
Althof discusses in [20] the need to develop CBR applications as
a sys-tematic engineering activity. The paper addresses the
software developmentlife-cycle targeting the development of
software products that support CBRdevelopment.
A discussion about CBR modeling is presented in Krite [21]. The
paperdescribes how CBR can be used to compare, reuse, and adapt
inductivemodels that represent complex systems. The paper does not
address specificcases and knowledge representations.
4. Abstract Model
The proposed approach to model a CBR-based system for
problem-solvinghas two steps:
• Abstract Model (AM) definition; and
• Concrete Model (CM) mapping.
The definition of the AM requires expertise in the application
domaininvolved. The mapping to the CM from the AM requires a
minimum CBRexpertise.
The abstract model is a high-level representation to structure
the knowl-edge and, as such, to define the scope of action of the
problem-solving sys-tem. It requires specialist knowledge of the
application domain to whicha system using CBR is applied. This
model aims to represent knowledgeabout the scope of actions for the
CBR system.
The abstract model is composed by a Technological Domain
(TD)with general and specific objectives, attributes, measurements,
actions, andpremises.
4.1. Technological Domain (TD)
The technological domain defines, in general, the scope of the
targetproblem-solving or, in other words, what the problem is and
how to repre-sent it [16] [17].
The representation of the TD in the proposed model is as
follows:
• System and problem description and objectives;
• Static, contextual and dynamic attributes of the system;
• Measurements; and
• Actions.
This set of components adequately describes the target system
for gen-eral resource management problems like virtual machine
management, dat-acenter management, and network management to cite
some applicationexamples.
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4.2. TD Component - System and Problem Definition, Gen-eral and
Specific Objectives
The system and problem description is a formal or textual
description ofCBR problem and system.
The general objective is a high-level definition and
delimitation of thetarget management problem. It is associated to
the CBR resource manage-ment task.
The specific objectives detail the general objective of the CBR
manage-ment system’s multiple possible outcomes. In effect, this
corresponds to thedefinition of a multi-objective problem, possibly
under a multi-requirementscenario.
The specific objectives specify a subset of the context,
measurement, andaction attributes used in the CBR decision-making
and learning processes.
4.3. TD Component - Attributes
The static attributes describe static characteristics of the
target system andhave, in most cases, documentary value. The CBR
system operation doesnot index the static attributes, so they do
not interfere in the problem-solving process.
The contextual attributes indicate the context of the problem
and cor-respond to definitions and parameters that do not change
frequently. Fromthe CBR system perspective, contextual attribute
modification may implyin restarting the learning process with
CBR.
The dynamic attributes are the set of variables and parameters
thatindicate the target system’s global state. In the
problem-solving context,dynamic attributes impact the target
management process and are mea-surable.
4.4. TD Component - Measurements, Tolerance and Actions
The measurements are, in general, the set of variables actual
values acquiredby a monitoring system that are relevant to the
management process. Themeasurements, together with the context
attributes, provide a snapshot ofthe system current state.
The measurements are instances of the system variables and aims
tohave a snapshot of the systems and quantify the specific
objectives. Ineffect, the management of accepted values for
measurable variables quantify,in general, the specific
objectives.
Tolerance represents the accepted value for the scope of this
method-ology. So, in most cases, the specific objectives are
represented by ranges,upper or lower limits for defined managed
variables.
The actions are the operation set that is used to react upon the
identi-fication of a problem. The set of actions defined is
executed on the systemand are related to the defined specific
objectives.
4.5. Premisses
In the CBR abstract model, the premises are the set of problems
to whicha solution is known. Premises are optional and aim to
facilitate knowledgeacquisition by the CBR system. It is essential
to highlight that the CBRsystem is capable of learning from scratch
without any given premise.
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The premises can be confirmed or not, through the acquisition of
newknowledge. Wrong premises will negatively impact the learning
processsince the premise will be discarded to allow the acquisition
of new knowl-edge.
5. Concrete Model
The concrete model corresponds to the mapping of the abstract
modeldefinitions in CBR cases, similarity function, parameters,
variables, andweighting priorities of the deployed CBR system.
The mapping from the abstract model to the concrete model is
achievedin the following way:
• TD components like attributes, measurements, and tolerances
aremapped in the CBR cases description.
• General and specific objectives are mapped in similarity
function andevaluation function.
• Actions are mapped in solutions for the CBR cases.
• Premises are mapped in the first cases for the CBR system.
The proposed sequence of steps to map from AM to CM model is
asfollows:
1. Mapping of attributes and measurements necessary to achieve
thespecific objectives;
2. CBR case description definition using the mapped set of
attributesand measurements;
3. Tolerance level definition for the CBR case components;
4. Mapping of the actions for the solution of CBR cases;
5. Similarity function definition;
6. Evaluation function definition; and
7. CBR 4R cycle operation process.
The last step, the CBR 4R cycle operation process, is described
in thispaper as a non-exhaustive sequence of how to do it
procedures and hintsthat can be used in the CBR 4R cycle.
5.1. The CBR Case
In CBR terminology a case is a problem situation composed by a
set ofparameters describing the problem domain and the associated
solution forthe problem (Figure 2) [22].
A generic CBR case is:
Cj = (pj , aj) (1)
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Figure 2: A CBR case
where,
pj = {SAj , CAj ,M j , T j} (2)
aj = {aj1, aj2, ..., ajx} (3)
SAj = {SAj1, SAj2, ..., SAjn} (4)
CAj = {CAj1, CAj2, ..., CAjk} (5)
Mj = {M j1,M j2, ...,M jz} (6)
Tj = {T j1, T j2, ..., T jy} (7)
The case Cj is composed by the set of parameters pj and the
associ-ated set of actions aj . The set of parameters pj includes
all the relevantstatic attributes (SAj), context attributes (CAj),
measurements (Mj) andtolerances (Tj) for the problem situation
being described.
The case solution is composed by the set of relevant actions aj
to achievethe defined objectives. These actions allow operations or
sets of operationsthat are used to react to a particular problem in
the system.
5.2. Similarity Function (SF)
Similarity is a crucial aspect of CBR. A similarity function is
used to re-trieve similar cases from the case-database when a new
case or unsolved
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case arrives at the system. In summary, a new case or unsolved
case is thedescription of a new problem to be solved.
Examples of similarity functions include the identification of
similaritybased on rules, correlation testing, K-nearest-neighbor
(KNN) techniques,and the cosine similarity measure, among others
[19] [11] [18] [16] [23].
The similarity function, regardless of the option, requires the
definitionof the evaluation indexes and their weights for the given
real-world problem.The definition of the similarity measures is an
actual challenging researchproblem and has a significant dependency
on the target problem [24].
From the methodological point of view, the similarity function
ismapped from the specific objectives and is composed of a set of
attributesand measurements with weights that define their priority
in the definitionof the similarity.
The similarity of one case to others can, from the
methodological pointof view, be defined by averaging the distinct
similarities of part of the case,for example, a case with three
indexed attributes (xi, xj , xl) will be similarto another case
(yi, yj , yl) if the attributes i, j and l of the cases x andy are
similar to each other. This partial similarity, by attribute, is
calledlocal similarity.
The local similarity is calculated according to the type of
attribute thatdefines the case, and a specific function can be used
for each type of data.The functions used for calculate local
similarity, as an example, are ladderfunction, linear function,
equality function, maximum function, intersectionfunction, and
contrast function [16].
The global similarity determines how similar one case is to the
otherusing the values of local similarities to which weights can
also be assigned.
Attributes and measurements have direct and indirect relation
with thespecific objectives. Consequently, two issues arrive in
terms of methodolog-ically mapping of the similarity function
parameters: i) to consider indirectsimilarities; and ii) to
consider similar cases in distinct contexts. Indirectrelations must
be indexed, and a similar case in distinct contexts must
bedifferentiated by choosing attribute weights adequately.
5.3. Evaluation Function (EV)
The evaluation function is an optional facility that may be
included in theCBR operation process. It is not conventionally
inserted in the CBR cyclebut might be helpful in the CBR operation
cycle. The EV is mapped fromthe objectives, attributes, and
tolerances of the concrete model.
The fundamental idea of using an evaluation function is to
interpret thestate of the measurements, comparing them with the
tolerances defined forthe CBR system. It provides a kind of
on-the-fly evaluation of the CBRsystem behavior.
This function can be used for two purposes: i) to generate
periodicwarnings and diagnoses of symptoms and alerts detected in
the CBR sys-tem; and ii) to check if a solution applied to the CBR
system meets theobjectives (general and specific) and tolerances
defined.
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6. CBR Operation Process - Hints and How to DoIt
After the mapping from the abstract model to the concrete model
with thedefinition of objectives, attributes, measurements,
actions, and cases, theCBR 4R cycle starts (Recover, Reuse,
Revision and Retain).
For each of these steps, some procedures shall be executed by
the CBRsystem. These procedures depend on the actual
problem-solving issue fo-cused on the CBR system, but there are
commonalities. We explore someof these commonalities to provide a
set of hints and possible how to doactions for the operation of CBR
systems.
6.1. Recover
The Cycle 4R has as its starting point the recovery phase that
retrieves asimilar case and evaluates it. This task can be
triggered in two differentways: reactively or proactively.
In reactive mode, an alarm requests analysis triggered by a
current prob-lem, to obtain a solution or optimize the system. In
proactive mode, theCBR system is activated to check the system’s
situation and occasionallypropose improvements proactively or a
solution.
When no case returns from the case-database, two procedures are
sug-gested:
• Use the method assisted by the manager, where he provides a
newsolution.
• Use an automated method where the solution is
automaticallymapped considering the defined objectives or an
arbitrary solutionis attributed to the current problem. The
arbitrary solution attribu-tion corresponds to a brute force
learning method.
6.2. Revision
In the review phase, CBR assesses the efficiency of the proposed
solution.For the review, it is necessary to wait a specific time
until the actions takeeffect, and the attributes and measurements
of the new state of the systemare updated.
In this step, the evaluation function is used to check if the
new solutionpresents improved performance. Without improvements,
the new case isconsidered unsuccessful. The new case, positive or
negative, is, by conven-tion, stored in the CBR database.
After applying the solution, the current and previous attributes
andmeasurements, cannot differ much from the previous state. With a
con-siderable variation, it is not possible to identify whether the
system hasimproved due to the solution adopted or simply because
the state of the re-sources has changed. As adopted in other AI
techniques, to use a discountis recommended in these cases.
Another recommendation is the creation of a configurable
equivalencethreshold for the cases. The aim is to avoid many very
similar cases pop-ulating the case-database and contribute to its
excessive growth, whichresults in a performance problem.
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False-positive cases can be negated in the next reuse. For
false-negativecases, we suggest a period of validity, both for
positive and negative cases,simulating the human forgetfulness of
old facts that are rarely used.
7. Using the Abstract and Concrete Models for anCBR-based
Application - Cognitive Managementof Bandwidth in Network Links
We exemplify now how abstract and concrete models can facilitate
thedefinition of the objectives, attributes, measurements, and
tolerances tomodel a CBR-based application.
The target CBR-based application (BAMCBR) aims to manage
band-width in links of an MPLS (MultiProtocol Label Switching
Network). Thelink management is executed by a Bandwidth Allocation
Model (BAM)that dynamically receives requests for link setup and
grants or denies theserequests based on the link bandwidth
availability in the network [25] [26].The cognitive management
consists of CBR deciding when should the BAMmodel be changed among
a set of options based on network parameters sta-tus. Oliveira in
[27] has a detailed description of this cognitive
managementapplication, and, in this paper, we focus on illustrating
how AM and CMcan be used to model the CBR application.
It follows the Abstract Model definition and Concrete Model
mappings.
7.1. BAMCBR Abstract Model
The first step in building the abstract (AM) model is the
representation ofthe technological domain with the definition of
its objectives, attributes,measurements, and tolerances.
7.1.1 BAMCBR Tecnological Domain (TD):
The CBR system’s technological domain is the cognitive
management of anMPLS/DS-TE type computer network with bandwidth
allocation models(BAMCBR Tool) [27].
7.1.2 BAMCBR General and Specific Objectives:
The general objective of the BAMCBR is to decide when should the
BAMmodel be changed among a set of BAM model options available
based onnetwork link-state performance parameters and input
traffic.
The specific objectives drive the BAM model reconfiguration
decisionprocess and are the following:
1. To maximize link throughput
2. To minimize link preemption; and
3. To minimize link devolution.
It is essential to highlight that only with the expertise about
theapplication domain (BAM model operation [26]) it is possible to
knowthat throughput maximization, preemption, and devolution
minimizationare management objectives achievable by the
reconfiguration of the BAMmodel [28].
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7.1.3 BAMCBR Static Atributes:
The static attributes are the BAM model static configuration
parameters.In the BAMCBR they are the total managed link bandwidth
and the con-figured bandwidth allocate per class of traffic in the
link (BC - BandwidthConstraint [26]).
7.1.4 BAMCBR Contextual Atributes:
The contextual attributes define the management context, and the
modifi-cation of these attributes, although less frequent, does
reflect the manager’sperception of what he wants from the CBR
system. In BAMCBR, theadopted BAM and the tolerances for
throughput, preemption, and devolu-tion are the main contextual
parameters. Any change on these attributesimplies in restarting the
CBR learning process.
7.1.5 BAMCBR Measurement Attributes:
The measurement attributes are the link variables that indicate
the link’sperformance and state in a given moment. These are
measurable dynamicvariables belonging to the target CBR system
(network link). Examples ofmeasurement attributes used by the
BAMCBR tool are link preemption,link devolution, packet loss, LSP
(Label Switched Path) request blocking,and link utilization
[27].
7.1.6 BAMCBR Tolerances:
The tolerances represent the accepted values range for
attributes in general.Their definition requires expertise in the
domain area and they refines thespecific objectives. In the BAMCBR,
as an example, the managementaccepts a link utilization of 10% with
a 10% tolerance.
7.1.7 BAMCBR Actions:
The actions correspond to the set of operations used to solve a
problem fora certain case. In BAMCBR, the action is to reconfigure
the current BAMmodel among the available options: MAM (Maximum
Allocation Model),RDM (Russian Dolls Model) and ATCS
(AllocTC-Sharing) models [28].
7.1.8 BAMCBR Premises:
The premises are the set of problems or states to which a
solution is known.For example, when the current BAM is MAM or RDM,
and the utilizationis less or equal to 50 %, the BAM might be
reconfigured to ATCS [28].
7.2. BAMCBR Concrete Model
The concrete model (CM) maps the abstract model in the
parameters andvariables used by the CBR system.
7.2.1 BAMCBR Case Description:
The case description includes the attributes, measurements and
tolerancespreviously defined in the abstract model. In BAMCBR, it
includes BAM
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models, measurement variables like preemption, utilization,
loss, blockingand devolution, and the action options for BAM model
change (Figure 3).
Figure 3: Caso BAMCBR
7.2.2 BAMCBR Case Solution:
The actions set for BAMCBR is to dynamically switch between
ATCS,RDM and MAM models [28].
7.2.3 BAMCBR Evaluation Function:
The evaluation function verifies the actual state of the managed
systemby comparing the actual case with previous cases using
measurements andtolerances defined. For example, it must also be
able to verify if a solutionapplied to the network is closer to the
objective than the solution previouslyadopted. BAMCBR uses the WkNN
function to evaluate the current stateof the network in relation to
other previous states.
Weights in the evaluation function reflect management expertise.
Forexample, BAMCBR considers that devolution generates a negative
impactmore significant than preemption, which, in turn, generates a
more signifi-cant impact than blocking. As such, the weights for
devolution, preemption,and blocking are 3, 2, and 1,
respectively.
7.2.4 BAMCBR Similarity Function:
The functions for local and global similarity defined for the
BAMCBR toolare indicated in Table 1.
The cut-off threshold is used for recovering similar solutions
within sim-ilarity equal or superior to the indicated limit. The
equivalence thresh-old limit is used to avoid storing multiple
nearly identical solutions at the
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Table 1: BAMCBR Similarity Functions
Attribute/Measurement Similarity Function Weight
BAM Model Equal Function 40Throughput Linear Function 30
Blocking Linear Function 30Devolution Linear Function
20Preemption Linear Function 20
Global Similarity Function: WkNNCut-off Threshold 96%
Equivalence Threshold 98.5%
database and, thus, populated it excessively. In other words,
solutions withsimilarity equal or superior to the equivalence
threshold limit are not storedin the CBR database.
8. Final Considerations
The methodological approach to model CBR-based applications uses
thedefinition of an abstract model (AM) that is subsequently mapped
in aconcrete model (CM).
The abstract model represents the domain to which CBR is applied
andits definition needs essentially the knowledge of an expert in
the applicationdomain. The concrete model corresponds to the CBR
parameters whosemapping from the abstract model requires CBR
expertise. The splittingin two models facilitates the model process
and, in addition, allows theallocation of domain and CBR
specialists to the distinct phases of themodeling process.
As such, this proposal’s inherent advantage is that it allows a
taskdivision between specialists in the domain and specialist in
CBR. This fa-cilitates the modeling process and has the potential
to foster the utilizationof CBR in an even large number of areas
where computer science expertiseis less frequent.
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DOI: 10.4236/jcc.2020.89001 Sep. 4, 2020 16 Journal of Computer
and Communications
1 Introduction2 Case-based Reasoning Fundamental Aspects3
Related Work4 Abstract Model4.1 Technological Domain (TD)4.2 TD
Component - System and Problem Definition, General and Specific
Objectives4.3 TD Component - Attributes4.4 TD Component -
Measurements, Tolerance and Actions4.5 Premisses
5 Concrete Model5.1 The CBR Case5.2 Similarity Function (SF)5.3
Evaluation Function (EV)
6 CBR Operation Process - Hints and How to Do It6.1 Recover6.2
Revision
7 Using the Abstract and Concrete Models for an CBR-based
Application - Cognitive Management of Bandwidth in Network Links7.1
BAMCBR Abstract Model7.1.1 BAMCBR Tecnological Domain (TD):7.1.2
BAMCBR General and Specific Objectives:7.1.3 BAMCBR Static
Atributes:7.1.4 BAMCBR Contextual Atributes:7.1.5 BAMCBR
Measurement Attributes:7.1.6 BAMCBR Tolerances:7.1.7 BAMCBR
Actions:7.1.8 BAMCBR Premises:
7.2 BAMCBR Concrete Model7.2.1 BAMCBR Case Description:7.2.2
BAMCBR Case Solution:7.2.3 BAMCBR Evaluation Function:7.2.4 BAMCBR
Similarity Function:
8 Final Considerations