Appendix A: OLAP and OLAP Server Definitions
Appendix B: Data Warehouse Terminology
Appendix C: OLAP Product Evaluation Rules
Appendix D: Case Studies and Action Research
Appendix E: Data Dictionary: Files & Elements (selected entries)
Appendix F: Data Warehouse Schema Definition
Appendix G: Data Dictionary: Elements Alphabetically (selected entries)
Appendix H: Review Questionnaire
The OLAP Council Glossary
Definitions from Creative Data, Inc.
This Appendix is reproduced from "Providing OLAP (On-line Analytical Processing) to User-Analysts: An IT Mandate", a paper by E.F. Codd, S.B. Codd and C.T. Salley(41)
The twelve rules for evaluating OLAP products are:
1. Multi-Dimensional Conceptual View
A user-analyst's view of the enterprise's universe is multi-dimensional in nature. Accordingly, the user-analyst's conceptual view of OLAP models should be multi-dimensional in nature. This multi-dimensional conceptual schema or user view facilitates model design and analysis, as well as inter and intra dimensional calculations through a more intuitive analytical model. Accordingly user-analysts are able to manipulate such multi-dimensional data models more easily and intuitively than is the case with single dimensional models. For instance, the need to "slice and dice," or pivot and rotate consolidation paths within a model is common. Multi-dimensional models make these manipulations easily, whereas achieving a like result with older approaches requires significantly more time and effort.
Whether OLAP is or is not part of the user's customary front-end (e. g., spreadsheet or graphics package) product, that fact should be transparent to the user. If OLAP is provided within the context of a client-server architecture, then this fact should be transparent to the user-analyst as well. OLAP should be provided within the context of a true open systems architecture, allowing the analytical tool to be embedded anywhere the user-analyst desires, without adversely impacting the functionality of the host tool.
Transparency is crucial to preserving the user's existing productivity and proficiency with the customary front-end, providing the appropriate level of function, and assuring that needless complexity is in no way introduced or otherwise increased.
Additionally, it should be transparent to the user as to whether or not the enterprise data input to the OLAP tool comes from a homogeneous or heterogeneous database environment.
The OLAP user-analyst must be able to perform analysis based upon a common conceptual schema composed of enterprise data in relational DBMS, as well as data under control of the old legacy DBMS, access methods, and other non-relational data stores at the same time as the basis of a common analytical model. That is to say that the OLAP tool must map its own logical schema to heterogeneous physical data stores, access the data, and perform any conversions necessary to present a single, coherent and consistent user view. Moreover, the tool and not the end-user analyst must be concerned about where or from which type of systems the physical data is actually coming. The OLAP system should access only the data actually required to perform the indicated analysis and not take the common "kitchen sink" approach which brings in unnecessary input.
4. Consistent Reporting Performance
As the number of dimensions or the size of the database increases, the OLAP user-analyst should not perceive any significant degradation in reporting performance. Consistent reporting performance is critical to maintaining the ease-of-use and lack of complexity required in bringing OLAP to the end-user.
If the user-analyst were able to perceive any significant difference in reporting performance relating to the number of dimensions requested, there would very likely be compensating strategies developed, such as asking for information to be presented in ways other than those really desired. Spending one's time in devising ways of circumventing the system in order to compensate for its inadequacies is not what end-user products are about.
5. Client-Server Architecture
Most data currently requiring on-line analytical processing is stored on mainframe systems and accessed via personal computers. It is therefore mandatory that the OLAP products be capable of operating in a client-server environment. To this end, it is imperative that the server component of OLAP tools be sufficiently intelligent such that various clients can be attached with minimum effort and integration programming.
The intelligent server must be capable of performing the mapping and consolidation between disparate logical and physical enterprise database schema necessary to effect transparency and to build a common conceptual, logical and physical schema.
6. Generic Dimensionality
Every data dimension must be equivalent in both its structure and operational capabilities. Additional operational capabilities may be granted to selected dimensions, but since dimensions are symmetric, a given additional function may be granted to any dimension. The basic data structure, formulae, and reporting formats should not be biased toward any one data dimension.
7. Dynamic Sparse Matrix Handling
The OLAP tools' physical schema must adapt fully to the specific analytical model being created to provide optimal sparse matrix handling. For any given sparse matrix, there exists one and only one optimum physical schema. This optimal schema provides both maximum memory efficiency and matrix operability unless of course, the entire data set can be cached in memory. The OLAP tool's basic physical data unit must be configurable to any subset of the available dimensions, in any order, for practical operations within large analytical models. The physical access methods must also be dynamically changeable and should contain different types of mechanisms such as:
Sparseness (missing cells as a percentage of possible cells) is but one of the characteristics of data distribution. The inability to adjust (morph) to the data set's data distribution can make fast, efficient operation unobtainable. If the OLAP tool cannot adjust according to the distribution of values of the data to be analyzed, models which appear to be practical, based upon the number of consolidation paths and dimensions, or the size of the enterprise source data, may be needlessly large and/or hopelessly slow in actuality. Access speed should be consistent regardless of the order of cell access and should remain fairly constant across models containing different numbers of data dimensions or varying sizes of data sets.
For example, given a set of input data from the enterprise database which is perfectly dense (every possible input combination contains a value, no nulls), it is possible to predict the size of the resulting data set after consolidation across all modeled data dimensions.
For example, in a particular five-dimensional analytical model, let us suppose that the physical schema size after model consolidation is two-and-one-half times the size of the input data from the enterprise database.
However, if the enterprise data is sparse, and has certain distribution characteristics, then the resulting physical schema might be one-hundred times the size of the enterprise data input. But, given the same size data set, and the same degree of sparseness, but with different data distribution, the size of the resulting physical schema might be only two and-one-half times the size of the enterprise data input as in the case of the perfectly dense example. Or, we could experience anything in between these two extremes. "Eyeballing" the data in an attempt to form an educated guess is as hopeless as is using conventional statistical analysis tools to obtain crosstabs of the data.
Because conventional statistical analysis tools always compare only one dimension against one other dimension, without regard for the other, perhaps numerous, data dimensions, they are unsuitable to multi-dimensional data analysis. Even if such tools could compare all dimensions at once (which they can't), the resulting crosstab would be the size of the product of all the data dimensions, which would be the maximum size of the physical schema itself.
By adapting its physical data schema to the specific analytical model, OLAP tools can empower user-analysts to easily perform types of analysis which previously have been avoided because of their perceived complexity. The extreme unpredictability and volatility in the behavior of multi-dimensional data models precludes the successful use of tools which rely upon a static physical schema and whose basic unit of data storage has fixed dimensionality (e.g., cell, record or two-dimensional sheet). A fixed, physical schema which is optimal for one analytical model, will typically be impractical for most others. Rather than basing a physical schema upon cells, records, two dimensional sheets, or some other similar structure, OLAP tools must dynamically adapt the model's physical schema to the indicated dimensionality and especially to the data distribution of each specific model.
8. Multi-User Support
Oftentimes, several user-analyst's have a requirement to work concurrently with either the same analytical model or to create different models from the same enterprise data. To be regarded as strategic, OLAP tools must provide concurrent access (retrieval and update), integrity, and security.
9. Unrestricted Cross-Dimensional Operations
The various roll-up levels within consolidation paths, due to their inherent hierarchical nature, represent in outline form, the majority of 1:1, 1:M, and dependent relationships in an OLAP model or application. Accordingly, the tool itself should infer the associated calculations and not require the user-analyst to explicitly define these inherent calculations. Calculations not resulting from these inherent relationships require the definition of various formulae according to some language which of course must be computationally complete.
Such a language must allow calculation and data manipulation across any number of data dimensions and must not restrict or inhibit any relationship between data cells regardless of the number of common data attributes each cell contains.
For example, consider the difference between a single dimensional calculation and a cross-dimensional calculation. The single dimensional calculation: Contribution = Revenue - Variable Cost defines a relationship between attributes in only one data dimension, which we shall call D_ACCOUNTS. Upon calculation, what occurs is that the relationship is calculated for all cells of all data dimensions in the data model which possess the attribute Contribution
A cross-dimensional relationship and the associated calculations provide additional challenges. For example, given the following simple five-dimensional outline:
D(42)_Accounts Sales Overhead InterestRate et cetera D_Corporate United Kingdom London York et cetera France Paris Cannes et cetera D_FiscalYear Quarter1 January February March Quarter2 April May June et cetera D_Products Audio Video et cetera D_Scenario Budgeted Actual Variance et cetera Sample Five-Dimensional Outline Structure
The formula to allocate corporate overhead to parts of the organization such as local offices (Paris, Cannes, et cetera) based upon their respective contributions to overall company sales might appear thus:
Overhead equals the percentage of total sales represented by the sales of each individual local office multiplied by total corporate overhead
Here is another example of necessary cross-dimensional calculations. Suppose that the user-analyst desires to specify that for all French cities, the variable InterestRate which is used in subsequent calculations, should be set to the value of the BUDGETED MARCH INTERESTRATE for the city of Paris for all months, across all data dimensions. Had the user-analyst not specified the city, month and scenario, the attributes would alter and stay consistent with the month attributes of the data cell being calculated when the analytical model is animated. The described calculation could be expressed as :
If the value within the designated cell appears within the consolidation path D_Corporate, beneath the consolidation level France, then the global interest rate becomes the value of the interest rate for the month of March which is budgeted for the city of Paris
10. Intuitive Data Manipulation
Consolidation path re-orientation, drilling down across columns or rows, zooming out, and other manipulation inherent in the consolidation path outlines should be accomplished via direct action upon the cells of the analytical model, and should neither require the use of a menu nor multiple trips across the user interface. The user-analyst's view of the dimensions defined in the analytical model should contain all information necessary to effect these inherent actions.
11. Flexible Reporting
Analysis and presentation of data is simpler when rows, columns, and cells of data which are to be visually compared are arranged in proximity or by some logical grouping occur ring naturally in the enterprise. Reporting must be capable of presenting data to be synthesized, or information resulting from animation of the data model according to any possible orientation. This means that the rows, columns, or page headings must each be capable of containing/displaying from 0 to N dimensions each, where N is the number of dimensions in the entire analytical model.
Additionally, each dimension contained/displayed in one of these rows, columns, or page headings must itself be capable of containing/displaying any subset of the members, in any order, and provide a means of showing the inter-consolidation path relationships between the members of that subset such as indentation.
12. Unlimited Dimensions and Aggregation Levels
Research into the number of dimensions possibly required by analytical models indicates that as many as nineteen concurrent data dimensions (this was an actuarial model) may be needed. Thus the strong recommendation that any serious OLAP tool should be able to accommodate at least fifteen and preferably twenty data dimensions within a common analytical model.
Furthermore, each of these generic dimensions must allow an essentially unlimited number of user-analyst defined aggregation levels within any given consolidation path.
This data warehousing project was conducted in the form of a case study, and many of the methods of action inquiry or research are applicable. A statement from the Action Inquiry Network (ActNet) makes the point that "the action inquiry model focuses primarily on identifying and resolving difficult, complex, real-life problems critical to organizations and society. This includes the formidable challenges of leadership, innovation, informed participation, and prejudice."(43)
Although the action research model is applied more commonly in the fields of education (teaching and learning), health services, social sciences and economics, this statement is a very close fit for the issues that are confronted when developing a data warehouse. The success of the project is very dependent on the support and input of the senior leadership. Their informed participation is crucial. The project is primarily for the purpose of improving the decision making process. There also needs to be a willing open-mindedness to consider and suggest new ways of doing things, including organizational and social changes. An attitude of pre-judgment and prejudice is almost certain to deprive the organization of some of the best ideas for improvement.
"A crucial issue in action inquiry is whether a problem is routine or non-routine, trivial or difficult. The difference between routine/trivial and non-routine/difficult problems is, admittedly, not always easy to discern. Nevertheless, the action inquiry model focuses primarily on identifying and resolving difficult, complex, real-life problems critical to organizations and society. This includes the formidable challenges of leadership, innovation, informed participation, and prejudice. Bewildering problems often develop when members of a group try to formulate and carry out a new strategy and evaluate their work together.
"Effective groups can resolve difficult problems by taking innovative action relatively soon. As the participants question their underlying programs or the credibility of ideas, they maintain high levels of interpersonal openness. They accept that while openness is actually or potentially embarrassing, threatening, or frustrating, it is simultaneously necessary to increase trust and individuality in their group. They may deny these difficulties but rarely do such instances not get noticed, challenged, and corrected. As they improve action inquiry skills, their minimally defensive interpersonal and group relations enable them to innovate and become productive."(44)
Addresses held as part of Common Information about persons
Item Type Size Description name_number C 6 CHAR 6 Computerised record number assigned to this person address_number C 1 CHAR 1 Address number of this address record date_address_modified D 8 ZONED-U 8 Date this address was modified good_address C 1 CHAR 1 Indicates the validity of this address address1 C 30 CHAR 30 1st line of address, eg number and street address2 C 30 CHAR 30 Line 2 of address (optional) town C 30 CHAR 30 Town or suburb state C 10 CHAR 10 State part of address postcode C 7 CHAR 7 Post code country C 20 CHAR 20 Country part of address phone C 15 CHAR 15 Telephone number part of person address PRIMARY KEY: address_key : name_number : address_number
Permanent record of student subjects taken, with results
Item Type Size Description name_number C 6 CHAR 6 Computerised record number assigned to this person year_yyyy C 4 CHAR 4 Year (YYYY) for this record semester C 1 CHAR 1 E353: Semester in which the load occurs code subject_code C 5 CHAR 5 Alphanumeric code assigned to this subject attendance_status C 1 CHAR 1 Type of attendance in this class - eg, Auditing, Normal date_started C 4 CHAR 4 Date (DD/MM) student commenced this subject completion_status C 1 CHAR 1 Final status of this subject for this student date_sub_terminated C 4 CHAR 4 Date (DD/MM) student quit doing this subject grade C 3 CHAR 3 Grade assigned to a subject unit_status C 1 CHAR 1 E355: Unit of study completion status checksheet_category C 2 CHAR 2 Checksheet category to assign this advanced standing to checksheet_subject C 5 CHAR 5 Subject code being granted advanced standing status PRIMARY KEY: subject_key : name_number : year_yyyy : semester : subject_code
create domain YEAR_DOM char(4); comment on domain YEAR_DOM is 'standard definition for year with century'; CREATE TABLE DW_STUDENT ( ref_year YEAR_DOM, course_code CHAR(5), student_id CHAR(10), semester CHAR(1), aou CHAR(3), course_name_abbrev CHAR(30), graduating CHAR(1), fos CHAR(6), date_of_birth DATE, age_in_ref_year INTEGER, sex CHAR(1), aborig_torres CHAR(1), citizen_resident CHAR(1), term_location CHAR(4), home_location CHAR(4), admission CHAR(2), commencement_date DATE, attendance_type CHAR(1), country_birth CHAR(4), language_home CHAR(2), tert_ent_score CHAR(3), total_eftsu_half INTEGER, hecs_prexmt_tot INTEGER, hecs_exmt_status CHAR(2), hecs_amt_paid INTEGER, PRIMARY KEY ( ref_year, student_id, course_code, semester ) deferrable); CREATE UNIQUE INDEX PKDW_STUDENT ON DW_STUDENT ( ref_year ASC, student_id ASC, course_code ASC, semester ASC ); CREATE TABLE DW_SUBJECT ( ref_year YEAR_DOM, semester CHAR(1), student_id CHAR(10), course_code CHAR(5), unit_study CHAR(10), aou CHAR(3), discipline_group CHAR(4), eftsu INTEGER, unit_status CHAR(1), PRIMARY KEY ( ref_year, semester, student_id, course_code, unit_study ) deferrable); CREATE UNIQUE INDEX PKDW_SUBJECT ON DW_SUBJECT ( ref_year ASC, semester ASC, student_id ASC, course_code ASC, unit_study ASC ); CREATE TABLE DW_PP_COHORT ( student_id CHAR(10), course_code CHAR(5), analysis_year YEAR_DOM, semester CHAR(1), course_name_abbrev CHAR(30), cohort_year YEAR_DOM, age_in_anal_year INTEGER, age_group CHAR(7), sex CHAR(1), aou CHAR(3), course_type_name CHAR(10), course_status CHAR(10), spu_annual INTEGER, spu_cumulative INTEGER, headcount INTEGER, eftsu INTEGER, PRIMARY KEY (course_code, student_id, analysis_year, semester ) deferrable); CREATE UNIQUE INDEX PKDW_PP_ANALYSIS ON DW_PP_COHORT (course_code, student_id, analysis_year, semester );
Item Type Size Description Location aborig_torres C 1 CHAR E316: Aboriginal/Torres Strait Islander code SR_STUDENT_PERSONAL acad_award C 1 CHAR Academic Excellence Award given on graduating SR_STUDENT_COURSE acad_bridging_course C 1 CHAR Bridging course required for academic reasons SR_STUDENT_PERSONAL_SEM acad_prob_code N 1 ZONED-U Reason for granting Academic Probation Status SR_STUDENT_COURSE acceptance_status N 1 ZONED-U Stage of acceptance or otherwise SR_STUDENT_PERSONAL_SEM accredited C 1 CHAR Is the course accredited? SR_CODE_COURSE_MASTER address_number C 1 CHAR Address number of this address record CI_ADDRESS address1 C 30 CHAR 1st line of address, eg number and street CI_ADDRESS address2 C 30 CHAR Line 2 of address (optional) CI_ADDRESS admission C 2 CHAR E327: Basis for admission to current course SR_STUDENT_PERSONAL advanced_standing C 1 CHAR Is advanced standing being sought? SR_STUDENT_PERSONAL aou C 3 CHAR E333: Academic organisational unit code SR_CODE_COURSE_MASTER application_status N 1 ZONED-U Application and supplementary form received, etc SR_STUDENT_PERSONAL_SEM application_type N 1 ZONED-U 1=New student, 2=Returning, 3=Returning after absence SR_STUDENT_PERSONAL attendance_status C 1 CHAR Type of attendance in this class - eg, Auditing, Normal SR_STUDENT_SUBJECT attendance_type N 1 ZONED-U Full-time or Part-time SR_STUDENT_COURSE baptised C 1 CHAR B=Baptised SDA, U=Unbaptised SDA, O=Other SR_STUDENT_PERSONAL bridging_course C 1 CHAR Y=taking chemistry bridging course SR_STUDENT_PERSONAL_SEM bursary_amt N 1 ZONED-U Amount of bursary granted to this student SR_STUDENT_PERSONAL_SEM bursary_provider C 20 CHAR Name of provider of this student bursary SR_STUDENT_PERSONAL_SEM campus C 1 CHAR C=Cooranbong, W=Wahroonga SR_STUDENT_PERSONAL_SEM campus_taught C 1 CHAR C=Cooranbong, W=Wahroonga SR_CODE_SUBJECT cert_credit N 1 ZONED-U Total certificate credit points taken SR_STUDENT_PERSONAL_SEM checksheet_category C 2 CHAR Checksheet category to assign this advanced standing to SR_STUDENT_SUBJECT checksheet_subject C 5 CHAR Subject code being granted advanced standing status SR_STUDENT_SUBJECT citizen_resident C 1 CHAR E358: Citizen/resident indicator SR_STUDENT_PERSONAL college_sship C 1 CHAR Y=college scholarship available SR_STUDENT_PERSONAL_SEM completion_status C 1 CHAR Final status of this subject for this student SR_STUDENT_SUBJECT conference C 5 CHAR SDA conference of this person's home church SR_STUDENT_PERSONAL contact_address_code N 1 ZONED-U Address code holding this person's contact address SR_STUDENT_PERSONAL country C 20 CHAR Country part of address CI_ADDRESS country_birth C 4 CHAR E346: Country of birth code SR_STUDENT_PERSONAL course_code C 5 CHAR Course code SR_CODE_COURSE_MASTER course_code C 5 CHAR Course code SR_STUDENT_COURSE course_load N 2 ZONED-U E350: Course load SR_CODE_COURSE_MASTER course_name C 50 CHAR Course name in full for Avondale SR_CODE_COURSE_MASTER course_name_abbrev C 30 CHAR E309: Course name - abbreviated SR_CODE_COURSE_MASTER course_name_full C 72 CHAR E308: Course name - full SR_CODE_COURSE_MASTER course_record_number C 3 CHAR Pointer for course record SR_STUDENT_COURSE course_record_number C 3 CHAR Pointer to semester 1 course record SR_STUDENT_PERSONAL_SEM course_type N 2 ZONED-U E310: Course type code SR_CODE_COURSE_MASTER credit_lev1_sem1 N 3 ZONED-U Standard course credit points for level 1, semester 1 SR_CODE_COURSE_MASTER credit_lev1_sem2 N 3 ZONED-U Standard course credit points for level 1, semester 2 SR_CODE_COURSE_MASTER credit_lev2_sem1 N 3 ZONED-U Standard course credit points for level 2, semester 1 SR_CODE_COURSE_MASTER credit_lev2_sem2 N 3 ZONED-U Standard course credit points for level 2, semester 2 SR_CODE_COURSE_MASTER credit_lev3_sem1 N 3 ZONED-U Standard course credit points for level 3, semester 1 SR_CODE_COURSE_MASTER credit_lev3_sem2 N 3 ZONED-U Standard course credit points for level 3, semester 2 SR_CODE_COURSE_MASTER credit_lev4_sem1 N 3 ZONED-U Standard course credit points for level 4, semester 1 SR_CODE_COURSE_MASTER credit_lev4_sem2 N 3 ZONED-U Standard course credit points for level 4, semester 2 SR_CODE_COURSE_MASTER current_level N 1 ZONED-U Current level in course SR_STUDENT_COURSE current_semester N 1 ZONED-U Current semester in course SR_STUDENT_COURSE currently_offered C 1 CHAR Y=course is currently offered SR_CODE_COURSE_MASTER date_acad_prob C 6 CHAR Date that academic probation status was given SR_STUDENT_COURSE date_address_modified D 8 ZONED-U Date this address was modified CI_ADDRESS date_applic_rcvd C 6 CHAR Date application was received SR_STUDENT_COURSE date_commenced C 6 CHAR Date this student first commenced this course SR_STUDENT_COURSE date_full_accept C 6 CHAR Date full acceptance was given SR_STUDENT_COURSE date_prov_accept C 6 CHAR Date provisional acceptance was given SR_STUDENT_COURSE date_recommenced C 6 CHAR Date this student recommenced this course SR_STUDENT_COURSE date_registered C 6 CHAR Date this student registered SR_STUDENT_COURSE date_started C 4 CHAR Date (DD/MM) student commenced this subject SR_STUDENT_SUBJECT date_sub_terminated C 4 CHAR Date (DD/MM) student quit doing this subject SR_STUDENT_SUBJECT date_suppl_rcvd C 6 CHAR Date supplimentary form was received SR_STUDENT_COURSE date_terminated C 6 CHAR Date this student ceased being enrolled in this course SR_STUDENT_COURSE department N 3 INTEGER Avondale department code SR_CODE_SUBJECT dependent_disc_flag C 1 CHAR Override flag for denominational employee discount SR_STUDENT_PERSONAL_SEM dip_coor_credit N 1 ZONED-U Credit points for Cooranbong degree subjects SR_STUDENT_PERSONAL_SEM dip_wah_credit N 1 ZONED-U Credit points for Wahroonga degree subjects SR_STUDENT_PERSONAL_SEM disability C 8 CHAR E386: Indicates responses to 3 questions about disabilities SR_STUDENT_PERSONAL discipline_group C 4 CHAR E336: Discipline group code SR_CODE_SUBJECT dorm_room_code C 4 CHAR Room code of residence hall room allocated to this student SR_STUDENT_PERSONAL edu_type C 1 CHAR P=Primary, S=Secondary, N=Non-teaching SR_CODE_COURSE_MASTER eftsu N 1 ZONED-U Equivalent full-time student unit SR_STUDENT_PERSONAL_SEM equivalent_subject C 5 CHAR Other name for this subject (Same as subject code if none) SR_CODE_SUBJECT fee_student C 1 CHAR E349: Fee paying student indicator SR_STUDENT_PERSONAL fees_address_code N 1 ZONED-U Address code of address record containing fees address SR_STUDENT_PERSONAL fees_business_phone C 15 CHAR Fee payer's business phone number SR_STUDENT_PERSONAL fees_initials C 5 CHAR Fee payer's initials SR_STUDENT_PERSONAL fees_relationship C 1 CHAR Relationship of fee payer to student SR_STUDENT_PERSONAL fees_surname C 20 CHAR Surname of fee payer SR_STUDENT_PERSONAL fees_title C 4 CHAR Title of fee payer SR_STUDENT_PERSONAL fin_status C 1 CHAR Financial status SR_STUDENT_PERSONAL_SEM first_aid_cert C 1 CHAR Indicates if student has a first aid certificate SR_STUDENT_PERSONAL fos C 6 CHAR E311: Field of study code SR_CODE_COURSE_MASTER good_address C 1 CHAR Indicates the validity of this address CI_ADDRESS govt_funded C 1 CHAR Indicates if this is a Government funded course SR_CODE_COURSE_MASTER grade C 3 CHAR Grade assigned to a subject SR_STUDENT_SUBJECT graduating C 1 CHAR Y=Student graduates in this year SR_STUDENT_COURSE handbook_year C 4 CHAR This student course is based on the Handbook for this year SR_STUDENT_COURSE hecs_amt_paid N 1 ZONED-U E381: HECS amount paid - semester SR_STUDENT_PERSONAL_SEM hecs_exmt_status C 2 CHAR E380: HECS exemption status SR_STUDENT_PERSONAL_SEM hecs_prexmt_tot N 1 ZONED-U E382: HECS amount prior to exemption - total SR_STUDENT_PERSONAL_SEM home_location C 4 CHAR E320: Location code of permanent home residence SR_STUDENT_PERSONAL in_absentia C 1 CHAR Graduated in absentia SR_STUDENT_COURSE instructed_english C 1 CHAR Prior education was given in English SR_STUDENT_PERSONAL kin_address_code N 1 ZONED-U Address number of next of kin address SR_STUDENT_PERSONAL kin_business_phone C 15 CHAR Telephone number for next of kin SR_STUDENT_PERSONAL kin_given_names C 30 CHAR Full given names of next of kin SR_STUDENT_PERSONAL kin_initials C 5 CHAR Initials of next of kin SR_STUDENT_PERSONAL kin_occupation C 30 CHAR Occupation of next of kin SR_STUDENT_PERSONAL kin_surname C 20 CHAR Surname of next of kin SR_STUDENT_PERSONAL kin_title C 4 CHAR Title of next of kin SR_STUDENT_PERSONAL language_home C 2 CHAR E348: Language spoken at home indicator SR_STUDENT_PERSONAL late_application C 1 CHAR Application was late SR_STUDENT_PERSONAL_SEM late_registration C 1 CHAR Student registered late SR_STUDENT_PERSONAL_SEM ma_credit N 1 ZONED-U Total Masters credit points taken SR_STUDENT_PERSONAL_SEM major C 10 CHAR Standard code for major SR_STUDENT_COURSE major_code C 10 CHAR Standard code for major SR_CODE_COURSE_MASTER marital_status C 1 CHAR Marital status (single, married, etc) SR_STUDENT_PERSONAL_SEM minor C 10 CHAR Standard code for minor SR_STUDENT_COURSE misc_course C 1 CHAR This is a miscellaneous course SR_CODE_COURSE_MASTER name_code C 6 CHAR First 4 letters of surname + first 2 letters of given name SR_STUDENT_PERSONAL name_number C 6 CHAR Computerised record number assigned to this person CI_ADDRESS name_number C 6 CHAR Computerised record number assigned to this person SR_STUDENT_COURSE name_number C 6 CHAR Computerised record number assigned to this person SR_STUDENT_PERSONAL name_number C 6 CHAR Computerised record number assigned to this person SR_STUDENT_PERSONAL_SEM name_number C 6 CHAR Computerised record number assigned to this person SR_STUDENT_SUBJECT name_number_sibling C 6 CHAR NAME_NUMBER of a sibling (for family discount) SR_STUDENT_PERSONAL_SEM name_record_number N 3 ZONED-U Name history record number of person's name for this course SR_STUDENT_COURSE number_attended N 8 INTEGER Total number of students attending this subject SR_CODE_SUBJECT number_grades N 8 INTEGER Total number of numeric grades for this subject SR_CODE_SUBJECT nz_non_res_fee C 1 CHAR This student is a NZ non-residential for fee payments SR_STUDENT_PERSONAL offered C 1 CHAR This course is currently offered SR_CODE_SUBJECT other_field C 10 CHAR Study field in addition to 1st major and minor SR_STUDENT_COURSE overseas_sship_code N 1 ZONED-U Override code for overseas scholarship SR_STUDENT_PERSONAL_SEM permanent_resident C 1 CHAR E390: Permanent resident status SR_STUDENT_PERSONAL phone C 15 CHAR Telephone number part of person address CI_ADDRESS postcode C 7 CHAR Post code CI_ADDRESS prior_qualification N 1 ZONED-U Highest qualification prior to application for enrollment SR_STUDENT_PERSONAL ref_semester C 1 CHAR Reference semester for this record SR_STUDENT_PERSONAL_SEM ref_year C 4 CHAR Reference year for this record SR_STUDENT_PERSONAL ref_year C 4 CHAR Reference year for this record SR_STUDENT_PERSONAL_SEM religion_father C 15 CHAR Religion of student's father SR_STUDENT_PERSONAL religion_mother C 15 CHAR Religion of student's mother SR_STUDENT_PERSONAL residence_type C 1 CHAR Day or residential SR_STUDENT_PERSONAL_SEM sda_church_mship C 20 CHAR SDA church where membership is held SR_STUDENT_PERSONAL secondary_institution C 30 CHAR Institution where prior qualification was received SR_STUDENT_PERSONAL semester C 1 CHAR E353: Semester in which the load occurs code SR_CODE_SUBJECT semester C 1 CHAR E353: Semester in which the load occurs code SR_STUDENT_SUBJECT semester C 1 CHAR Semester for this record SR_STUDENT_COURSE single_room_request C 1 CHAR Student is requesting a single room SR_STUDENT_PERSONAL_SEM starting_semester N 1 ZONED-U Indicates in which semester (0/1/2) student will commence SR_STUDENT_PERSONAL state C 10 CHAR State part of address CI_ADDRESS student_account C 8 CHAR Student ledger account number SR_STUDENT_PERSONAL student_religion C 15 CHAR Religion of this student SR_STUDENT_PERSONAL student_status N 1 ZONED-U Registration status (registered, on leave, withdrawn, etc) SR_STUDENT_PERSONAL_SEM subject_code C 5 CHAR Alphanumeric code assigned to this subject SR_CODE_SUBJECT subject_code C 5 CHAR Alphanumeric code assigned to this subject SR_STUDENT_SUBJECT subject_credit_points N 2 ZONED-U Number of credit points, for academic credit and fees SR_CODE_SUBJECT subject_name C 50 CHAR Full description of this subject SR_CODE_SUBJECT subject_name_abbrev C 15 CHAR Abbreviated name of this subject SR_CODE_SUBJECT subject_type C 1 CHAR Degree/Certificate type of subject SR_CODE_SUBJECT sum_grades N 9 INTEGER Sum of all numeric grades for this subject SR_CODE_SUBJECT sum_grades_squared N 12 INTEGER Sub of the squares of all numeric grades SR_CODE_SUBJECT te_score_nsw N 1 ZONED-U NSW equivalent Tertiary Entrance Ranking SR_STUDENT_PERSONAL te_score_source C 1 CHAR State where TE score was obtained SR_STUDENT_PERSONAL term_location C 4 CHAR E319: Location code of semester/term residence SR_STUDENT_PERSONAL termination_code N 1 ZONED-U Code giving reason why student is no longer doing course SR_STUDENT_COURSE tert_ent_score N 1 ZONED-U E369: Tertiary entrance score SR_STUDENT_PERSONAL town C 30 CHAR Town or suburb CI_ADDRESS typing_competency C 1 CHAR Indicates whether typing competency has been achieved SR_STUDENT_PERSONAL unit_status C 1 CHAR E355: Unit of study completion status SR_STUDENT_SUBJECT vac_address_code N 1 ZONED-U Address number of vacation address SR_STUDENT_PERSONAL year_achieved C 4 CHAR Year (YYYY) in which prior qualification was achieved SR_STUDENT_PERSONAL year_arrival C 4 CHAR E347: Year of arrival in Australia SR_STUDENT_PERSONAL year_first_offered N 4 ZONED-U Year (YYYY) this course was first offered SR_CODE_COURSE_MASTER year_last_offered N 4 ZONED-U Year (YYYY) this course last offered SR_CODE_COURSE_MASTER year_yyyy C 4 CHAR Year (YYYY) for this record SR_CODE_COURSE_MASTER year_yyyy C 4 CHAR Year (YYYY) for this record SR_CODE_SUBJECT year_yyyy C 4 CHAR Year (YYYY) for this record SR_STUDENT_COURSE year_yyyy C 4 CHAR Year (YYYY) for this record SR_STUDENT_SUBJECT
Note: Responses to questions are given in italics.
1. The EIS/Data Warehousing concept
2. Needs Analysis
3. Design & construction
Was there adequate:
6. Desktop tools
8. The Future
(41) Codd E.F., Codd S.B. and Salley C.T.: E. F. Codd & Associates. Providing OLAP (On-line Analytical Processing) to User-Analysts: An IT Mandate. p18.
(42) "D_" is used each time to indicate that this top most aggregation level is the dimension.
(43) Brown T.C.: Action Inquiry Network (ActNet). Action Inquiry, Problem Types
(44) Brown T.C.: Action Inquiry Network (ActNet). Action Inquiry, Problem Types
That concludes this paper. Previous section or return to table of contents