A Data Warehouse-based Mining System For Academic
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 5 Issue 08, August-2016A Data Warehouse-based Mining System forAcademic Resource Capacity PlanningWilson NwankwoPh D, MCPN, PMP, RMPSenior Lecturer, Systems & Security,SouthEastern College of Computer Engineering & Information TechnologyOwerri NigeriaAbstract—Academic resource capacity planning isfundamental to the success of every academic program in anyinstitution whether at the primary, secondary or tertiary level.As a matter of fact, academic capacity planning is a continuousprocess often involving both core academic and non-academicpersonnel of an Institution. As simple as it may sound, academiccapacity planning may make or mar the quality of studentstrained as well as the reputation of the Institution amongst itspeers. This is why it is vital for every academic plannerparticularly at the department level to carefully considerimportant indicators such as: course requirements, previousstudent performances, academic achievements, future goals, etc.during the planning process. This research studies thefundamental processes in selected Institutions of HigherLearning in Nigeria as it affects academic planning specificallyduring the allocation of courses to academics during eachsemester and develops a model program based on data miningof historical performances of academics and students. Themodel when implemented would provide the necessaryinfrastructure for drawing insights through predictions on theresults of mined data thereby enhance decision-making as to theallocation of appropriate courses to suitable academic resources.Keywords— Data Warehousing; Academic resource; Capacityplanning; Data mining; Predictive Analytics; Higher learningI. INTRODUCTIONPlanning may be described as an intelligent process ofteninvolving thinking, reasoning, and organizing of tasks aroundcertain specific objectives in a bid to achieving goals. In theacademic sphere, planning is sine qua non to the growth anddevelopment of the human and material resources in theInstitution. Academic planning encompasses planning andapproval for new academic programs (degrees, majors andcertificates), substantial changes to those programs (renaming,mergers, suspending admissions, discontinuations), planningand approval for academic departments and centers/institutes,working with faculty governance on the development ofpolicies that support academic planning processes and changesassociated with Subjects and courses[1]. Failure to planalways imply acceptance of failure during implementation oran implicit plan to fail.Academic resource capacity planning is central and vital toevery academic planning process as it influences to no smallmeasure the effectiveness of learning and teaching. Capacityplanning is a thorough and very important process usuallyrequiring a professional with unique skills in privatebusinesses. It is usually geared engaging the best hands thatwill optimize productivity. It is also employed as a means ofefficient and effective resource management by privateIJERTV5IS080371businesses [2]. Academic resource planning though may notbe geared specifically at profit maximization, is aimed atengaging the best resources for teaching of approvedacademic courses and by so doing enhance productivity.Productivity in this spectrum is not measured by profitabilityin terms of monetary value but the extent and quality ofknowledge and skill transferred to the learner by the teacher(academic resource). This may be determined by examiningthe various performances of the students being taught over aperiod of time.Academic resource capacity planning enables academicdecision-makers to commit high priority courses and subjectswith confidence to skilled academics within the Institution,that have demonstrated experience and excellence in the areasin question. Thus, resource capacity planning providesdecision-makers with the: Ability to determine academic resource availabilityand course requirements and perform a matching ofperformance needs against a given academic resource Insight on resource need and performance gaps Flexibility of performing what-if scenario analysis tofind the optimal solution set. Ability to create resource projections to resolvefuture resource imbalancesThus academic resource planning may be considered a processof merging and balancing academic resource capacity againstsome concrete course performance requirements in order toachieve and sustain optimum learning and skill transfer. Thisoptimality is to be measured continually by the performanceof students.Data warehousing and mining have grown to be thetechnology of choice in any organization where large volumesof data are imminent and where timely and accurate decisionmaking is are based on available data. Data warehousing isaimed at the timely delivery of the right information to theright individuals in an organization by harnessing concretedata from disparate data sources such as legacy files anddatabases. Data warehousing is a pragmatic and revolutionarymeans for supporting the analysis and monitoring of criticalprocesses [3]. Like in many other areas, data warehousingplays an important information consolidation base andconstitutes a key component of modern organizationalintelligence and analytics systems. Generally, academicinstitutions generates data in various areas such as: students’records, human resources, projects, accounting and payroll,budgets, examinations, academic profiles, library information,www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)438
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 5 Issue 08, August-2016etc. and it is not unusual to see large file cabinets in manyoffices in such institutions whether public or private.Data mining is an evolving science as well as an art.A. Problem DefinitionIn simple terms, every policy on education revolvesmuch around manpower development that is, equippingstudents in academic institutions with appropriate knowledgeand skills for growth and socioeconomic advancement.Policies do not translate to goals on the surface but requirecareful implementation. Prior to implementing any academicpolicy, there must be well thought-out plans to guide thepolicy implementation process. The bane of academicplanning is to enhance coordination with resultant growth andsustenance of quality academic performance that is inconsonance with regulatory standards and international bestpractices in education. Academic resource planning is acritical aspect of academic planning. In most institutions inNigeria and sub-Saharan Africa, academic resource planningis a tasking and cyclical process particularly in publicinstitutions where sociocultural and religious affiliations exertsignificant influence on procedures and practices regardless oflaid down rules. This is worsened by inadequate manpower inmost institutions. Even where the number of academicsavailable is relatively sufficient, the question as to whichacademic personnel would meet the average performancerequirements set for a particular course or subject becomes anassociative prevailing factor. This is often a challenge to manyheads of academic departments who usually chair the courseallocation panel or board. The “HOD” as he/she is fondlycalled, in many occasions allocates courses randomlyregardless of course requirements, current courseachievements, previous students’ performances and futureexpectations. The challenge may even be more overwhelmingin institutions like Universities, which run different academicprograms such as undergraduate and graduate programs;amidst various crippling segmented politicalized academiccliques (in various departments) with varying group interestswhich often hijack formal procedures in a way that do not inany way benefit the average student.As the allocation of courses to academic resources is adone on semester by semester basis, the head of department isburdened by two key problems: How to ascertain courses or subjects which do nothave adequate academic resourcesHow to gain insight as to determining an academicresource to undertake a course Conventionally, these two problems may require access tophysical records of the available academic resources. Theseproblems are in no doubt beyond the records whether physicalor electronic because adequacy is not reflected by the numberof available academic resources but by: the previous andpresent student performances, and the future expectations. Inaddition, gaining an insight as to matching a course against anacademic resource is not possible without a deep research intohistorical data on the department. The question as to how tosimplify this process of gaining insight is the problemunderlying this study as well as the basis for its conception.IJERTV5IS080371B. Objectives of the StudyThe objectives of this study are:a. To study the basic academic resource capacity planningprocesses vis a vis the basis for the semester allocation orallotment of taught academic courses to the variousacademic staff in institutions of higher learning;b. To apply the principles of data warehousing in creating acentral repository that would concretize historical recordsin areas that are relevant to academic resource planning;c. To integrate data mining techniques that use statisticalmodels to make predictions thus enhancing effectiveallocation of academic courses to most proficientacademic resource based on a number of prevailingfactors on a periodic basis.C. Scope of the StudyThis study is restricted to academic resource capacityplanning in institutions of higher learning in the sub-SaharanAfrica with Nigeria in focus. Academic resource capacityplanning is a multifaceted task chain which spans throughmany units or departments with administrative functions.Typical academic resource capacity planning tasks mayinclude:a. Design and review of academic resource policies inline with global and national standards;b. Ensuring compliance with fiscal and humanresources policies and procedures;c. Planning academic and non-academic resourcehiring;d. Development of new financial and human resourcessystems;e. Determination and review of Academic resourcecapacity needs of various academic departments;f. Matching every academic course specification toavailable academic resource proficiencies withemphasis on previous performances;g. Preparing and monitoring the annual budget;h. Developing annual enrollment targets;i. Managing contingency funds and ensuring fiscalsolvency of departments;j. Preparing financial reports for and managing theaccounts of units within Academic AffairsAdministration;k. Conduct facility planning and coordinate academicspace.In this study we examine how the challenges encountered bydecision makers in the academic circle as to harnessing andextracting useful information from large volume of historicaldata generated by academic departments in the process ofensuring the attainment of academic successes, are eliminatedthrough the use of mining techniques applied to a datawarehouse. One key area of emphasis is the matching of everytaught academic course to academic resource persons basedon academic proficiencies and previous performances.www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)439
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 5 Issue 08, August-2016D. Significance of the StudyData mining and Data warehousing are two computingtechnologies that are highly associated with analytics andintelligence gathering thereby helping decision-makers realizeeffective decisions at all levels. This study presents apragmatic and implementable prototype of a system for notjust harmonizing the data generated from the academicresource allocation processes and consequent results into adata warehouse but a computer-based model for extractingand mining hidden information for use in making predictions.Academic resource allocation decision makers need to beempowered with the vital tools to make important demandcommitment decisions based upon limited resources orcapacity information. In a nutshell, this study is important inthat it will:a.b.c.Support the mining and analysis of academic staffdata, to determine their relationships with staffperformance;Support the identification of areas where academicstaff performances are not improving, and thusenhance decisions-making on resource allocationreviews;Provide a simple environment for data analysis andreporting;II. METHODOLOGYDeveloping a new system is a problem solving process andrequires the selection of a coherent method or suite ofmethods often called a methodology. Methodology may bedescribed as a framework used to structure, plan, and controlthe process of developing an information system and consistsof steps, methods, techniques and procedures which governthe collection, analysis and design of a particular project [3].Some of the common methods that are employed todevelop data warehouses include: Top-down, Bottom-up,Agile, and Object-based methods. The object-based methodwas employed in this study due to its support for formalanalysis, modeling of dynamic complex systems, and rapiddevelopment and maintenance. InA. DocumentationThis study involved an investigation and documentationon three institutions of higher learning drawn from the SouthEast, South-South and South-West geopolitical zones ofNigeria respectively. That is, one institution from each zone.These institutions include: Federal university of TechnologyOwerri in the South-East, University of Lagos in the SouthWest and Rivers State University of Science and TechnologyPort Harcourt in the South-South zones respectively. Relevantdata including sample historical data were collected from theDepartmental heads of two academic in each institution.In each institution, Academic resource capacity planningas it relates allocation of taught courses in each department isdone based on the courses offered by the given departmentwith particular emphasis on the available academic resourcecapacity. The process may takes note of the following factors:a. Requisite academic qualifications;b. Publications;c. Previous course handling experience;d. Completed researches/Areas of specialization;e. Course performance histories.IJERTV5IS080371B. Development toolsThe tools that were used for analysis and development are:Erwin Data modeler; Microsoft Visual Studio 2012; PentahoData Integration (Kettle), and Microsoft SQL Server 2014.C. Analysis of the Present SystemTo match a course with an academic resource does notseem to be an easy task when the factors listed above arecarefully considered by a decision maker. However, to presentthe workings of the existing system, we specified thefollowing:a. The actors i.e. those who play a role(s) in the system;b. The process model that is, what happens in thesystem or what is going to happen in the new systemusing an activity diagram;c. External users(if any) that is, individuals outside thelogical boundary of the business area who may usethe system;d. Use cases that represent what the participants aredoing in the system/what the users will do with thenew system;e. The interaction among two or more classes orobjects using sequence/collaboration diagrams;f. Classes of objects/entities, their attributes,relationships and methods using class diagramsActors: academic staff, head of academic department,course evaluation officer, administrative officer, courseallocation officer.The process model of the existing system is presentedusing the activity diagram in Figure 1. The process model issummarized as follows:a. Administrative officer/Secretary compiles the list ofcourses offered in a semester and presents in a singlespreadsheet document;b. Head of Department reviews the academic resourcecapacity in the department visa a vis core and noncore departmental courses offered in the department;c. HOD/Course evaluation officer evaluates eachacademic staff profile including research areas,publications, qualifications, ranks, previous coursehandling experience;d. HOD assigns courses based on available academicresource while considering academic profiles andprevious experience;e. Course allocation report is tabulated, printed and sentto the academic staff.D. Use case analysisA use case is an abstract representation of a functionalitythat users need from the system. In object-oriented analysis,use cases are also used to depict the requirements analysisprocess. The functionalities defined by a use case arerepresented using the use case diagram. Figure 2 is the usecase diagram representing all the use cases associated with thepresent system of matching available academic resources tocourses. The interrelationships between the use cases are alsoestablished.www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)440
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 5 Issue 08, August-2016Prepare course list for thesemesterdepartmental coursesHOD receives/retrieves the spreadsheetAcademic resourceReview course requirements/previousperformancesReview resource profileaccess resource details, past performance,experience, strengths,publications,qualifications, etc.[No]Does course requirementsmatch resource's abilities?[Yes]Does the resource have enoughworkload already?[No][No][Yes][Yes]match resourceagainst courseGenerate reportIs the number ofacademic resourceexhausted?Fig. 1. Activity diagram representing the process model of the existing systemIJERTV5IS080371www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)441
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 5 Issue 08, August-2016Fig. 2. Use case diagram of the Academic resource to course matching processE. Domain analysisIn this phase, we identified and defined theobjects/concepts inherently present in the use cases using aconceptual model. The model described what data/informationis (would be) managed by the course matching process chain,and what data flow between users and the system. We usedclass diagrams and the unified modeling language for theconceptual modeling. The data represented by a class isbroken into: concept and association. The concept (condensedform of an object/class) is the representation of complexIJERTV5IS080371information that has a coherent meaning in the scanningoperations domain. Concepts aggregate attributes and may beassociated to each other. The identified concepts in theexisting are presented in the model in Figure 3. Theconceptual model consists of condensed classes withassociated relationships. The arrows show the relationships.The dotted arrow shows a dependency relationship, a solidarrow shows an association whereas an arrow with a triangularpointer shows inheritance.www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)442
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 5 Issue 08, August-2016publicationAttributescourse Attributes course code course credit titleOperationsteachesOperationsclass of publicationpublishertitleyear of publicationresearch*ownsAttributes*1.*Academic resourcejob historyAttributes end datefirst nameorganizationother namespositionresponsibilitiesstart datesurnamehas*1undertakescontact addressdate of birthdepartmentemailfirst namegenderhodnationalityother namesrankstate of erationsOperationscompletion datefield of researchstart datetitle of researchOperations*1Attributes 1Attributes completion datedescriptionproject ownerstart datetitleOperations11qualificationownsAttributes area of knowledgedate obtainedissuername1.*OperationsFig. 3. Conceptual model of the central objects of the existing systemIdentifying classes and their relationships is a very prior toimplementing the requirements of any system. The objectschema represented by the class diagram in Fig. 4 shows thevarious classes that form the foundation of the proposedmodel. Each object/class has static (attributes) and dynamicIJERTV5IS080371(behaviour/methods) characteristics. We are concerned withthe attributes alone, as shown in the model in Fig. 4. In themodel, the primary (PK) and foreign key (FK) attributes havebeen appropriately identified.www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)443
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 5 Issue 08, August-2016QualificationAttributes Job HistoryEmployeeAttributesAttributesdateObtained : IntegerempID : Integer FK issuer : StringKnowledgeAreaName : StringqualificationID : Integer PK *1Operations1empID : Integer PK hiredDate : DatepersonID : Integer FK position : Stringrank : Stringstatus : String *OperationsempID : Integer FK fromDate : DatehistoryID : Integer PK organization : Stringposition : Stringresponsibilities : StringtoDate : DateOperationsResearchAcademic resourceRoleAttributesAttributes empID : Integer FK ResourceID : Integer PK Attributes courseID : Integer FK roleID : Integer PK rolename : String ttributescourseCode : StringcourseID : Integer PK deptID : Integer FK lecturerID : Integer FK roleID : Integer FK semesterID : Integer FK sessionID : Integer FK Operations**Allocated Course completionDate : Integerduration : Stringfield : StringlecturerID : Integer FK researchID : Integer PK researchTitle : StringstartDate : IntegerAttributes classOfpublication : StringlecturerID : Integer FK location : StringpID : Integer PK publisher : Stringtitle : StringyearPublished : IntegerOperationsProjectAttributes completionDate : Datedescription : StringlecturerID : Integer FK pID : Integer PK projectName : StringprojectOwner : Stringprojectrole : StringstartDate : DateOperationsFig. 4. Class diagram of the existing systemF. Problems in the existing systemThe existing system of performing course-matching isfraught with complexities as itemized below:a. The existing system of resource to course matchingoften adopts a rule of thumb without recourse toprevious performances and future expectations but onavailability or which academic staff has been takingthe course from time immemorial;b. The process requires a lot of filtering and sorting byadministrative staff or the decision-maker and inmost cases become cumbersome with increasingnumber of courses and faculty members;IJERTV5IS080371c.Generating intelligence reports are often slow,ineffective and inefficient because the decisionmaker must spend time to access all the variousrecords needed to evaluate the suitability of theacademic resource as regards the course in question.These challenges were further validated with the use caseanalysis of the proposed model below.G. Use case analysis of the proposed systemThe functionality expected in the proposed system is reflectedusing the use case diagram in Figure 5. Here, the improvementis seen in the integration of warehousing to extract relevantdata and the subsequent use of mining techniques such asclustering to establish the most suitable resource for the givencourse based on the rated parameters.www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)444
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 5 Issue 08, August-2016Fig. 5. Use case diagram of the proposed systemIII. THE DATA WAREHOUSEThe prototype design is restricted to the vital componentsof the data warehouse that would be useful for mining fordecision making. The warehouse data is synthesized from datasources such as spreadsheet and database files. The designconsists of three phases namely: conceptual, logical, andphysical. We employed the Data mart approach. A data martrepresents a unit or departmental process within anorganization. It is composed of a fact table and associateddimension tables. The combination of one or more data martsconstitutes the data warehouse. Table 1 shows the fact anddimensions in the in the data mart.TABLE 1 ACADEMIC RESOURCE PACKAGEIJERTV5IS080371Building an Academic resource data warehouse to capture allfunctional warehouse needs would be a tasking project; hencethe data mart offered more flexibility in that a set ofrecognized data analysis requirements for a unit or departmentcould be captured on demand using a data mart. To this end,we have a single data mart called the academic resourceschema. The schema is designed using package stereotypes.Figure 6 shows a conceptual model containing packages. Infigure 6, a package contains fact or dimensional class. Thus,the packages constitutes a single star schema (data mart).A. Conceptual model designWhat is important in data warehouse design is not all thedata in a transactional data store, but those elements, whichare the essential drivers of the intelligent decision-makingprocess. These essential drivers are the grains which groupedinto a single package called the ‘scanning schema’. Theschema is divided into two parts: fact and dimension classes.A fact represents measures and context data. A dimension is aset of data that describe one business dimension. Dimensionsdetermine the contextual background for the facts. Both partsare derived from the classes in Fig. 4 above. Figure 6 showsthe conceptual star schema.www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)445
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 5 Issue 08, August-2016B. Logical model designThe logical schema is an extension of the conceptual schemarepresented earlier using the stereotypes in Fig. 6. Weestablished the attributes of each class, and the relationships(using association) between the fact class and the dimensionclasses. Thus, the fact class is associated to the dimensionclasses. Each class (fact/dimension) is identified by a uniqueobject identifier {OID} attribute. Figure 7 shows the logicaldata model of the data warehouse. Every dimension classname is preceded by a “Dim” prefix likewise a fact class nameis preceded by a “fact” prefix. dimension Session dimension JobHistory fact factFaculty dimension Project dimension Research dimension publication dimension Feedback dimension Qualification dimension courseAllocation dimension Academicresource dimension dateFig.6. Conceptual star schemaIJERTV5IS080371www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)446
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 5 Issue 08, August-2016Dim SessionAttributesAttributesAttributes Dim JobHistoryDim nName{OID}sessionDimIDOperations 1.* im Academic1.*Attributes D***Dim Research*Attributesfact Faculty Attributes1.* *OperationscourseCodecourses AllocatednoOfFailuresOnCoursenoOfPassesOnCourseno OfProjectsno OfPublicationno ofResearchsemestersessionyear%ScoreOnFeedback{FK} cID**OperationsDIm ProjectAttributes* *****Attributes ionsDim earchIDtoYear{OID}researchDimIDDim FeedbackAttributes1.**Dim PublicationAttributes tleyearPublished{OID}publicationDimID perationsOperations%ScoreOnFeedback (DIM FEEDBACK.totalScore/DIM FEEDBACK.totalScore)/100.It is the total percentage feedback score of the Academic resource on an allocated course in a semester.This score can be compared to the pass and failure rates on the course respectively and data mining can be used to relayconnections between these parametersFig. 7. Logical model of the Data warehouseIJERTV5IS080371www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)447
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 5 Issue 08, August-2016C. Physical designThe most important activity in this phase is theconversion of the logical design into a physical model byusing database system structures such as tables, tablespaces,etc. The various classes in the logical model in Fig. 7 wouldbe mapped to tables, relationships to foreign key constraints,attributes to fields, and object identifiers to primary key
Academic resource capacity planning is central and vital to every academic planning process as it influences to no small measure the effectiveness of learning and teaching. Capacity planning is a thorough and very important process usually requiring a professional with unique skills in private businesses. It is usually geared engaging the best .
Management under Master Data Define Warehouse Numbers. 2. Check the warehouse number assignment in Customizing for Extended Warehouse Management under Master Data Assign Warehouse Numbers. 3. Check the warehouse number control in Customizing for Extended Warehouse Management under Master Data Define Warehouse Number Control.
Preface to the First Edition xv 1 DATA-MINING CONCEPTS 1 1.1 Introduction 1 1.2 Data-Mining Roots 4 1.3 Data-Mining Process 6 1.4 Large Data Sets 9 1.5 Data Warehouses for Data Mining 14 1.6 Business Aspects of Data Mining: Why a Data-Mining Project Fails 17 1.7 Organization of This Book 21 1.8 Review Questions and Problems 23
DATA MINING What is data mining? [Fayyad 1996]: "Data mining is the application of specific algorithms for extracting patterns from data". [Han&Kamber 2006]: "data mining refers to extracting or mining knowledge from large amounts of data". [Zaki and Meira 2014]: "Data mining comprises the core algorithms that enable one to gain fundamental in
Data Mining and its Techniques, Classification of Data Mining Objective of MRD, MRDM approaches, Applications of MRDM Keywords Data Mining, Multi-Relational Data mining, Inductive logic programming, Selection graph, Tuple ID propagation 1. INTRODUCTION The main objective of the data mining techniques is to extract .
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Inventory data Warehouse Outgoing Inventory IoT Cloud gathers warehouse inventory data from Warehouse IoT Cloud gathers dispatched inventory data from Warehouse . Based on the warehouse floor design, budget, type of industry and materials , suitable option or combination of options possible to choose.
1.3 Common Data Warehouse Tasks 1-4 1.4 Data Warehouse Architectures 1-5 1.4.1 Data Warehouse Architecture: Basic 1-5 1.4.2 Data Warehouse Architecture: with a Staging Area 1-6 1.4.3 Data Warehouse Architecture: with a Staging Area and Data Marts 1-6 2 Data Warehousing Logical Design 2.1 Logical Versus Physical Design in Data Warehouses 2-1
