ERIC EJ1064284: Development of an Information System for Diploma Works Management

2011·0.8 MB·English

by  ERIC

InformaticsinEducation,2011,Vol.10,No.1,1–12 1
©2011VilniusUniversity
Development of an Information System for
Diploma Works Management
TsvetankaGEORGIEVA-TRIFONOVA
DepartmentofMathematicsandInformatics
St.CyrilandSt.MethodiusUniversityofVelikoTarnovo
3ArchitectGeorgiKozarovstr.,VelikoTarnovo,Bulgaria
e-mail:[email protected]
Received:January2011
Abstract.Inthispaper,aclient/serverinformationsystemforthemanagementofdataanditsex-
traction from a database containing information for diploma works of students is proposed. The
developedsystemprovidesusersthepossibilityofaccessinginformationaboutdifferentcharacter-
isticsofthediplomaworks,accordingtotheirspecificinterests.Theclient/serverarchitectureof
thesystemisdescribedaswellastheservicesoffered.Theauthorpresentsthestructureofthecre-
ateddatabasethatstoresthenecessaryinformation.AclientapplicationADP(accessdataproject)
isimplemented,providingthepossibilityforinsertion,updatingandsearching,aswellasaclient
applicationisfulfilledwithJavafordiscoveringtheconstraint-basedassociationrules.
Keywords:database;client/serversystem;ADP(accessdataproject)application;diplomaworks;
associationrules.
1. Introduction
Inthelastyears,thecreation,thedistributionandtheusageofthetrainingandscientific
literatureisperformedlargelybymeansofitsdigitalizedform.Inthismannertheworkof
teachers,researchers,professionalsinparticularareasarefacilitated,aswellasthework
ofthestudentsandmainlyofthestudentspreparingtheirdiplomaworks.Providingfast
access to the electronic variant of the developed diploma works and related with them
materialscansufficientlysupportthestudentsintheirworkandincreaseitsquality.
Theaimofthepresentedpaperistorepresentaclient/serversystemforkeepingin-
formationondiplomaworksofstudents.Theimplementedclient/serversystemprovides
usersthepossibilityofextractinginformationaboutthedevelopeddiplomaworks.Ital-
lows students and teachers quick access by means of a convenient interface to the data
andthefiles,relatedtothediplomaworksofstudents,graduatedthebachelororthemas-
ter degreeof some of thespecialitiesinDepartmentof Mathematicsand Informaticsin
St.CyrilandSt.MethodiusUniversityofVelikoTarnovoaftertheyear2002.
Thestorageofthedataaboutthediplomaworksofthestudentsinadatabasemakes
suitableconditionsfordatamining(Barsegyanetal.,2008;Kantardzic,2003),i.e.,ana-
lyzingtheaccumulateddatawiththepurposeofextractingthepreviouslyunknownand
2 T.Georgieva-Trifonova
potentiallyusefulinformation.Thatmotivatestheutilizationofaprogramfordiscovering
theconstraint-basedassociationrules.Associationruleminingisaformofdatamining
todiscoverinterestingcorrelationrelationshipsamongtheattributesoftheanalyzeddata.
Anassociationrulerevealsthefrequentoccurringpatternsofthegivendataitemsinthe
database.
Therestofthepaperisorganizedasfollows.InSection2,thefeaturesoftheinfor-
mationsystemsfordiplomaworksarereviewed.Section3containsadescriptionofthe
client/serverarchitectureandtheinterfaceofthesystem.InSection4,werepresentthe
entity-relationship model (ER model) of the database that stores information about the
diploma works of the students. We also produce the relational tables obtained after the
transformationofthecreatedERmodelintorelational.Theserelationsareimplemented
byusingthedatabasemanagementsystemMicrosoftSQLServer.Section5consistsof
thedescriptionoftheclientapplicationsforupdatingthedataanddatamining.
2. ReviewoftheFeaturesoftheInformationSystemsforDiplomaWorks
The main features of the existing storage and retrieval systems providing access to the
electronicversionofthediplomaworksofthestudents(HKUSTLibrary;UMGraduate
School&FoglerLibrary;VirginiaTech;WSULibraries)are:
1. Insertionofthedataaboutagivenstudent.
2. Insertionofthedataaboutadiplomawork–topic,yearofitsdefence,filewiththe
electronicversionofthediplomawork.
3. Searchingdiplomaworksbytopic,author,year.
Theneedofasystemprovidingthelistedfeaturesforourstudentsisthebasicmoti-
vationforthedevelopmentofthesystemrepresentedinthepresentedpaper.Moreover,
asaresultofourresearch,wehaveestablishedthataninformationsystemfromthecon-
sideredkindcouldproposeadditionalfeatures,whichmakeitmoreusefulfortheusers.
4. Insertionofthefilesrelatedtothediplomaworks.
Besidesthefileswiththecontentofthediplomaworks,thecorrespondingpresen-
tationsfromtheirdefences,themultimediafiles,theprograms,thedatabases,the
programssourcecodeandtheotherscanbeadded.Ononehandthissupplement
ishelpfulfortheusers.Ontheotherhand,asubstantialadvantageisthatprovid-
ing the electronic sources permits the students better and more complete ways to
representtheirdevelopments.
5. Applyingalgorithmsfordatamining.
Applying different algorithms for data mining on the data collected as a result
of the usage of the system, could lead to extracting useful information about the
diplomaworks,theirtopicsduringthedifferentyears,theobtainedmarksfromthe
defences,etc.
The basic features (1)–(3) are included in the developed information system and a
variantfortheimplementationoftheadditional(4)–(5)isproposed.
In the presented paper, the client/server architecture of the developed system is de-
scribedandtheservices,includedinitsrealization.Thestructureofthecreateddatabase
DevelopmentofanInformationSystemforDiplomaWorksManagement 3
is represented, keeping the necessary information. A client application ADP (Pearson,
2004) is proposed, designed for insertion, editing and searching the data. Besides this
aJavaprogramisapplied(Eck,2006;Eckel,2001),providingthepossibilityfordiscov-
eringtheconstraint-basedassociationrules.
3. OverviewoftheClient/ServerArchitectureoftheSystem
Theclient/serverarchitectureofthedevelopedsystemiscreatedonthebaseofthetwo-
layerinformationmodel(Fig.1).
Thelayerfordataprocessingisimplementedbyusingthedatabasemanagementsys-
tem.ForthepresentsystemweuseMicrosoftSQLServer,whichallowsefficientstorage
oflargedatabasesandprovidesfunctionalityforaccessingthedata(Bienieketal.,2006;
Kroenke,2003;MicrosoftCorporation).
The client part consists of an ADP application, providing a convenient interface for
insertion, updating and searching the data, as well as a Java program for mining the
constraint-basedassociationrules.
3.1. SQLServerDatabaseforDataStorage
The DiplomaWorksDB database serves for storage and processing the data for the
diplomaworks,thestudentsandtheiradvisors.Informationonthestudent’sfacultynum-
ber,student’snames,thescientificand/oreducationaldegree,thespeciality,theformof
training,thetopicandtheannotationofthediplomawork,thestudent’sadvisor,there-
viewers,the date of the defence of the diploma work, the mark obtainedby the student
forthediplomaworkismaintained.
Foreachdiplomaworkapossibilityforinsertionofanadditionalinformationispro-
vided–files(.doc,.pdf)withitscontent;presentation(.ppt,.pdf)ofthestudentforthe
defenceofthediplomawork;applicationimplementedbythestudent(suchasadatabase,
aprogramofC,Java,etc.)andothers.
Fig.1.Thearchitectureofthesystem.
4 T.Georgieva-Trifonova
Thebasicfunctionsofthedatabaseinclude:
• additionofanewstudentinthedatabase;
• editionofthedataaboutthestudents;
• deletionofstudentsfromthedatabase;
• browsingthedataforthestudents;
• searchingbydifferentcriteria.
3.2. ClientApplicationADPforInsertionandSearchingtheData
MicrosoftAccessallowsestablishingaconnectionbetweenthecurrentdatabaseandta-
blesfromotherdatabasesofMicrosoftSQLServerandotherdatasources.ADPiscon-
nected with a database of SQL Server and provides an access to the objects created in
thatdatabase(suchastables,views,storedprocedures,triggers,etc.).Thedataarestored
in thedatabaseof SQLServer.ADPdoesnotcontainanydataandtables,butit canbe
usedforeasycreationofforms,reports,macros.Asaresultofthat,theenduserfeatures
opportunity for insertion, editing, and deletion of the data by means of a comfortable
interface.
3.3. ClientApplicationforMiningtheConstraint-BasedAssociationRules
Thegoalofassociationrulesmining(Agrawaletal.,1993)istofindinterestingassoci-
ationsorcorrelationrelationshipsamongadataset,i.e.,toidentifythesetsofattributes-
items, which frequently occur together and then to formulate the rules characterizing
these relationships. The constraint-based association rule mining (Fu and Han, 1995)
aimstofindallrulesfromgivendataset,whichsatisfytheconstraintsrequiredfromthe
users.AnapplicationisimplementedwithJavafordiscoveringtheconstraint-basedas-
sociationrules,whichin(TrifonovandGeorgieva,2009)isutilizedforthedata,obtained
after applying the methods of digital processing of signals for analysis of the sounds
oftheuniqueBulgarianbells.ThisclientapplicationisconnectedwiththeDiploma-
WorksDB database with the purpose of performing the association analysis of the data
aboutthediplomaworksofthestudents.
4. ModelingoftheData
ThemodeloftheDiplomaWorksDBdatabase,inkeepingwiththeentity-relationship
model(ERmodel),introducedinChen(1976),isshowninFig.2.Theentitysetsofthe
ERmodelaredepictedasrectangles,theirattributesasellipsis,andtherelationshipsas
rhombs(Garcia-Molinaetal.,2002).
ThedatabaseisimplementedbymeansofthedatabasemanagementsystemMicrosoft
SQLServer.TherelevantrelationaltablesareshowninFig.3.
The structure of the database is defined to provide the best efficiency of the most
frequentlyusedoperations–insertion,updating,datasearching.
DevelopmentofanInformationSystemforDiplomaWorksManagement 5
Fig.2.ERdiagramoftheDiplomaWorksDBdatabase.
Fig.3.RelationalmodeloftheDiplomaWorksDBdatabase.
6 T.Georgieva-Trifonova
TheDiplomaWorksDBdatabaseofSQLServercontainsthecreatedviewsforex-
tractingthedatafromseveralrelatedtables,aswellasthestoredproceduresforobtaining
the information on the students, defended their diploma works during a specific month
andyear;thestudents,whosediplomaworks’topicscompriseaspecificstring.Thestored
procedures provide a better performance of the client/server system because they make
decreasingtheexchangetodatabetweentheclientandtheserver.Besidesthestoredpro-
cedures can accept parameters and therefore they can be executed from multiple client
applicationsbyapplyingdifferentinputdata.
5. ClientApplicationsforUpdating,SearchingandMiningtheData
AclientapplicationADPisdevelopedforupdatingandsearchingthedataaboutdiploma
worksofstudents,aswellasaclientapplicationimplementedwithJavaforminingthe
constraint-basedassociationrules.
5.1. ADPClientApplicationforInsertion,EditionandSearchingtheData
Formsforinsertionandupdatingthedataareimplemented.Theirpurposeistofacilitate
actualizationoftheinformation.Theformforinsertionofthedataaboutthestudentsand
theirdiplomaworksisshowninFig.4.
Besidesthis,theapplicationallowstheexecutionofdifferentqueries,whichperform
findingthespecificinformation,correspondingtothegivensearchingcriteria.Eachuser
canfulfilsearchbyfillingintextboxesand/orlistboxeswhichcorrespondtothechosen
Fig.4.Formforinsertionofthedataaboutstudents.
DevelopmentofanInformationSystemforDiplomaWorksManagement 7
characteristics of the diploma works of the students, stored in the database. The results
fromeachqueryarepresentedinaformatconvenientfortheenduser.Theformsandthe
reportsareimplementedwiththerecordsources–viewsandstoredproceduresdesigned
forextractingthedataabout:
• studentsfromachosenspeciality;
• studentswithachosendiplomawork’sadvisor;
• students,defendedtheirdiplomaworksduringachosenmonthandyear;
• students,whosediplomaworkscontainintheirtopicsagivenstring.
5.2. ClientApplicationforDiscoveringtheConstraint-BasedAssociationRules
The association rules are introduced in Agrawal et al. (1993) and they are utilized for
specifyingthecorrelationrelationshipsamongitemsetsinthedatabase.
LetI ={I ,I ,...,I }beasetofndifferentvaluesofattributes.LetRbearelation,
1 2 n
whereeachtuplethasauniqueidentifierandcontainsasetofitems,suchthatt⊆I.An
associationruleisanimplicationoftheformX →Y,whereX,Y ⊂I aresetsofitems
withX ∩Y =∅.ThesetX iscalledanantecedent,andY –aconsequent.
Therearetwoparametersassociatedwitharule–supportandconfidence.Thesupport
softheassociationruleX → Y istheproportion(inpercentages)ofthenumberofthe
tuplesinR,whichcontainX ∪Y tothetotalnumberofthetuplesintherelation.The
confidence c of the association rule X → Y is the proportion (in percentages) of the
number of the tuples in R, which contain X ∪ Y to the number of the tuples, which
containX.
The task of association rules mining is to generate all association rules which have
valuesoftheparameterssupportandconfidence,exceedingthepreviouslygivenrespec-
tivelyminimalsupportmin_suppandminimalconfidencemin_conf.
To increase the efficiency of existing algorithms for data mining, during the mining
process constraints are applied with the goal for these association rules, of which only
thoseinterestingtotheuseraregenerated,insteadofallassociationrules.Inthiswaya
largepartofthecalculationsforminingthoserulesthatareremovedasunnecessary,can
be avoided. Usually the constraints, provided by the users, are constraints for the data,
constraintsfortheattributes,constraintsforformationoftherule.
We have developed an application for discovering the constraint-based association
rules,whichneedstomeetthefollowingpreliminaryrequirements:
• Theapplicationmustgivetheopportunityfortheusertoselecttheattributesinthe
antecedentandtheconsequencesofthesearchedrules.
Usually the user is interested in a specified subset of attributes and wants to ex-
pressinterestingcommonconnectionsbetweentheselectedattributes.Thereforea
facilitywithafriendlyinterfaceshouldbeprovidedtospecifythesetofattributes
tobeminedandexcludethesetofirrelevantattributesfromtheexamination.
• The user has to be able to define various values of the minimal support and the
minimalconfidence,whentheitemsaremined.
Thesupportreflectstheutilityongivenrule.Theminimalsupportmin_supp,which
anassociationrulehastosatisfy,meansthateachvalue,includedinthestudy,has
8 T.Georgieva-Trifonova
to appear a significant number of times in corresponding attribute of the initial
relation.
Theconfidencemeasuresthereliabilityoftheinferencemadebyarule.
• Reducingthenumberofthegeneratedassociationrulesmustbepossiblebydeter-
miningthecriteriathatthevaluesofselectedattributeshavetosatisfy.
Innumerouscasesthealgorithmsgeneratealargenumberofassociationrules.Itis
almostimpossiblefortheenduserstoencompassorvalidatesuchalargenumber
of association rules, limiting the results of the data mining is therefore helpful.
Besides, often the user is interested in definite values of the attributes, which are
includedinassociationrulesmining.
• Visualizingtheobtainedresultsmustberepresentedinatabularviewwithprovid-
ingtheopportunityfortheusertoorderthefoundrulesby:
◦ alphabeticorderoftheattributes,whichparticipateintheantecedentandthe
consequenceoftheassociationrules;
◦ thesupportoftheassociationrulesinascendingordescendingorder;
◦ theconfidenceoftheassociationrulesinascendingordescendingorder.
Intabularviewofassociationrules,alldiscoveredrulesarerepresentedinatabular
tablewitheachrowcorrespondingtoaruleandrepresentsinformationaboutrule
support and confidence. By this way the user has a clearer and complete view of
the rules and can locate a specific rule more easily. The tabular view facilitates
understandingthelargenumberofrules.
Anapplicationisdeveloped,whichallowstheusertosetconstraintsforsearchedrules
and finds constraint-based association rules. The application is used for performing the
associationanalysisonthedifferentcharacteristicsofthediplomaworks,theinformation
forwhichiskeptinaninformationsystemproducedforthegoal.
Totheuserthatstartstheapplication,thefollowingpossibilities,areprovided(Fig.5):
• settingtheattributes,beingsubjecttoanalysis;
• setting the minimal value of the support min_supp and the minimal value of the
confidencemin_conf;
• settingtheconditions(Booleanexpression)forthevaluesoftheattributes,which
canparticipateornotintheantecedentandtheconsequenceofthesearchedrules;
• all the rules can be displayed in different order – by alphabetic order of the at-
tributesparticipatingintheantecedentandtheconsequence;bythesupportorcon-
fidenceinascendingordescendingorder.
Theapplicationisutilizedfordiscoveringtheconstraint-basedassociationrulesinthe
database containing information about the diploma works of about 1000 students grad-
uated the bachelor or the master degree of the specialities “Mathematics and informat-
ics”,“Informatics”,“Computerscience”,“Informationsystems”,“Informationsecurity”,
“Computermultimedia”inDepartmentofMathematicsandInformaticsinSt.Cyriland
St.MethodiusUniversityofVelikoTarnovoaftertheyear2002.Theattributes,whichcan
be included in analyzing, are: student’s faculty number; student’s names; the scientific
and/oreducationaldegree;the speciality;the form of training; the topic; the annotation
DevelopmentofanInformationSystemforDiplomaWorksManagement 9
Fig.5.Discoveringtheconstraint-basedassociationrules.
ofthediplomawork;theyearofthedefenceofthediplomawork,themarkobtainedby
thestudentforthediplomawork;thestudent’sadvisor.
Figure 5 shows an example result from the execution of the implemented program
with given values of the minimal support, minimal confidence and conditions for the
valuesoftheattributes.Forinstance,letthefollowingrulebegeneratedfromthedatabase
withthediplomaworks:
Speciality(“Informatics”)→Mark(“6”)
withvaluesofthesupports=0.11164andtheconfidencec=0.61702.Thisrulemeans,
thatforthestudents,graduatedinthespeciality“Informatics”,whosediplomaworksare
intheareaofthedatabasesandtheinformationsystems,oneofthemostfrequentmarks
fromthedefenceoftheirdiplomaworksis6(with61.702%confidence)andthestudents
in Informatics with diploma works in databases and information systems and marks 6
represent11.164%fromallstudents,includedinthestudy.
Someotherexamplesofassociationrules,whichcanberetrievedfromtheexecution
oftheprogram,are:
• Advisor(A),Speciality(S) → Mark(M)withthevaluesoftheparameterssup-
port s = 0.19477 and confidence c = 0.64634, where the condition YearOfDe-
fence=Y isgiven;
10 T.Georgieva-Trifonova
Bymeansoftherulesofthiskindwecanestablishthatthestudentswiththeadvisor
A,thespecialitySandgraduatedinagivenyearY,oneofthemostfrequentmarks
fromthedefenceoftheirdiplomaworksisM(with64.634%confidence)andthey
represent19.477%fromallstudents.
• Mark(M) → Speciality(S) with the values of the parameters support s =
0.49644andconfidencec=0.41627.
Such rules allow extracting information about the percentage (49.644%) of the
studentsfromagivenspecialityS,whosediplomaworksareevaluatedwithcertain
mark M. Besides, we can conclude that the students, which are evaluated with
markM,arefromthespecialityS with41.627%confidence.
Theusercanestablishtherelationshipbetweenotherattributesincludedinthestudyby
meansofthesimilarrules.
6. Conclusion
In this paper, the automated system is proposed. It explores a client/server based ap-
proach to managing the information on the diploma works of the students. The created
database contains information about different characteristics of the diploma works and
it is implemented on Microsoft SQL Server. The interface is developed by means that
allow establishing a connection with the database of the ADP project. This gives users
the possibility of easily accessing detailed information about the diploma works of the
students.
In addition, an application is represented which provides the possibility for finding
theconstraint-basedassociationrulesofthedataaboutthediplomaworks.
Thebasicadvantagesfromtheusageoftheimplementedsystemcanbesummarized
bythefollowingway:
• Thesystemprovidesfastandeasyaccesstothedevelopeddiplomaworksandthe
related materials. The user can receive an extract about the existing works of the
graduated students on a given topic. Besides, browsing concrete diploma works
and their reviews allows acquiring a clearer notion about the requirements to the
finalviewofadiplomawork,fortheeventualnotesandrecommendations.
• Anadditionalmotivationofthestudentsisprovidedforbetterandfullerrepresen-
tationoftheirdevelopments.
• Analyzingtheaccumulateddatawiththeconstraint-basedassociationrulesreveals
aninterestinginformationaboutthecharacteristicsofthestudent’sdiplomaworks.
Our future work includes development of an application for mining the constraint-
basedassociationrulesinthetextofthediplomaworks(textmining),whichallowsper-
formingtheassociationanalysisofthedifferentwordsfromtheircontents.
References
Agrawal,R.,Imielinski,T.,Swami,A.(1993).Miningassociationrulesbetweensetsofitemsinlargedatabases.
In:ProceedingsoftheACMSIGMODInternationalConferenceonManagementofData,Washington,207–
216.