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EDUCATION

Done By :

- Elham Alawad Suliman

- Fatima Sayed Alsheikh

- Omnia Yassir Izzeldin

- Rasha Osman Ahmed

Introduction

Introduction

Key Terms

Key Term

Accumulating snapshots:

are appropriate for short-lived processes that have a defined beginning and end, with standard intermediate milestones.

Surrogate key:

 is any column or set of columns that can be declared as the primary key instead of a "real" or natural key.

Factless fact table:

is a fact table that does not have any measures. It is essentially an intersection of dimensions (it contains nothing but dimensional keys).

Bus matrix:

is a data Warehouse planning tool and model and one of high abstraction and visionary planning on the Data Warehouse architectural level. 

Introduction

Introduction

In this presention we will go through the following concepts:

  • an accumulating snapshot--which is used to monitor potential student applicants as they progress through admissions activites .
  • other primary concept is the factless fact table.
  • higher education institutions are obviously want to create a strong educational environment also focused on:

-attracting high qualified students

-talented faculty/staff .

Similaraty

University into parts

Universities are simultaneously :

  • a real estate property management company (residential student housing)
  • restaurant with multiple outlets (dining halls)
  • retailer (bookstore)
  • events management and ticketing agency (athletics and speaker events)
  • police department (campus security)
  • professional fundraiser (alumni development)
  • consumer financial services company (financial aid)
  • investment firm (scholarship management)
  • invester capitalist (research and development)
  • job placement firm (career planning)
  • construction company (buildings and facilities maintenance)
  • medical services provider (health clinic).

Bus Matrix

Bus Matrix

Introduction

Introduction

The bus matrix a small piece covers several core processes within an educational institution:

  • Traditionally, there has been less focus on revenue and profit in higher education, but with ever-escalating costs and competition, universities and colleges cannot ignore these financial metrics.
  • They want to attract and retain students who align with their academic and other institutional objectives.
  • There’s a strong interest in analyzing what students are “buying” in terms of courses each term and the associated academic outcomes.
  • Colleges and universities want to understand many aspects of the student’s experience, along with maintaining an ongoing relationship well beyond graduation.

Bus Matrix Row of Education

Bus Matrix Row of Education

Snapshot & Pipline

Snapshot &

Pipline

Characteristics accumulating Snapshot Fact Tables

Characteristics accumulating Snapshot Fact Tables

The distinguishing characteristics of an accumulating snapshot fact table:

  • A single row represents the complete history of a workflow or pipeline instance.
  • Multiple dates represent the standard pipeline milestone events.
  • The accumulating snapshot facts often included metrics corresponding to each milestone, plus status counts and elapsed durations.
  • Each row is revisited and updated whenever the pipeline instance changes; both foreign keys and measured facts may be changed during the fact row updates.

Applicant pipeline

Applicant pipeline

  • In the case of applicant tracking, prospective students progress through a standard set of admissions milestones.

  • Perhaps you’re interested in tracking activities around key dates:

such as initial inquiry, campus visit, application submitted, application file completed, admissions decision notification, and enrolled or withdrawn.

- At any point in time:

  • Admissions and enrollment management analysts are interested in:

- how many applicants are at each stage in the pipeline??

The process is much like a funnel, where many inquiries enter the pipeline.

  • Admission personnel:

analyze the applicant pool by a variety of characteristics.

Applicant pipeline grain

Applicant pipeline grain

  • The grain of the applicant pipeline accumulating snapshot:
  • is one row per prospective student
  • also represents the lowest level of detail captured when the prospect enters the pipeline.
  • more information is collected while the prospective student progresses toward: application, acceptance, and enrollment,you continue to revisit and update the fact table row

Applicant Dimension

Applicant Dimension

  • There are multiple dates in the fact table corresponding to the standard milestone events. Each of these dates is treated as a role-playing dimension, with a default surrogate key to handle the unknown dates for new and in-process rows.
  • The applicant dimension contains many interesting attributes about prospective students.
  • Analysts are interested in slicing and dicing by applicant characteristics such :as geography, incoming credentials ,gender, date of birth, ethnicity, preliminary major, application source, and a multitude of others
  • The facts in the applicant pipeline fact table include a variety of counts that are closely monitored by admissions personnel

Applicant pipeline (Alternative Applica...

Applicant pipeline (Alternative Applicant Pipeline Schemas)

  • This type of fact table enables you to see an updated status and ultimately final order of each applicant.
  • because accumulating snapshot rows are updated, they do not preserve applicant counts and statuses at critical points in the admissions calendar.
  • analysts might also want to retain snapshots at several important cut-off dates.
  • Alternatively, you could build an admission transaction fact table with one row per transaction per applicant for counting and period-to-period comparisons.

Research Grant Proposal Pipeline

Research Grant Proposal Pipeline

  • Another education-based example of an accumulating snapshot.
  • Faculty and administration are interested in viewing the lifecycle of a grant proposal as it progresses through the pipeline from preliminary proposal to grant approval and award receipt.
  • support analysis of the number of outstanding proposals in each stage of the pipeline by faculty, department, research topic area, or research funding source.
  • Having this information in a common repository would allow it to be leveraged by a broader university population

Factless Fact Table

Factless fact Table

Factless Fact Tables

Factless Fact Tables

  • has no measurement metrics; captures the relationship between the involved keys
  • captures the many-to-many relationships between dimensions, but contains no numeric or textual facts.
  • used for tracking a process or collecting stats. 
  • can be used to generate the useful reports.
  • Example: Tracking student attendance or registration events

Factless Fact Table Types

1

Event factless fact table

Admissions Events

Admissions Events

You can envision/plan a factless fact table to track each prospective student’s attendance at an admission event: such as a high school visit, college fair, alumni interview or campus overnight

Course Registrations

Course Registrations

Similarly, you can track student course registrations by term using a factless fact table.

The grain would be one row for each registered course by student and term

Course Registrations

Course Registrations

Term Dimension:

- In this fact table, the data is at the term level rather than at the more typical calendar day, week, or month granularity.

each date in the daily calendar dimension should identify the term (for example, Fall), term and academic year (for example, Fall 2013), and academic year (for example, 2013-2014).

-The column labels and values must be identical for the attributes common to both the calendar date and term dimensions.

Student Dimension and Change Tracking:

The student dimension is an expanded version of the applicant dimension that previously discussed.

  • To retain some information from the applicati...
  • To retain some information from the application process (for example, geography, credentials, and intended major) …….but supplement it with on-campus information, such as part-time or full-time status, residence, athletic involvement indicator, declared major, and class level status (for example, sophomore).
  • i.e. you could imagine placing some of above attributes in a type 4 mini-dimension because factions throughout the university are interested in tracking changes to them, especially for declared major, class level, and graduation attainment.
  • People in administration and academia are interested in academic progress and retention rates by class, school, department, and major.
  • Alternatively, if there’s a strong demand to preserve the students’ profiles at the time of course registration, plus filter and group by the students’ current characteristics, you should consider handling the student information as a slowly changing dimension type 7 with dual student dimension keys in the fact table.
  • The surrogate student key would link to a dimension table with type 2 attributes; the student’s durable identifier would link to a view of the complete student dimension containing only the current row for each student

2

Coverages factless fact table

Facility Utilization

Facility Utilization

  • The second type of factless fact table deals with coverage, which can be illustrated with a facilities management scenario.
  • In this case you’d insert one row in the fact table for each facility for standard hourly time blocks during each day of the week during a term regardless of whether the facility is being used.
  • The facility dimension would include all types of descriptive attributes about the facility, such as the building, facility type, square footage and capacity.
  • The utilization status dimension would include a text descriptor with values of Available or Utilized. Meanwhile, multiple organizations may be involved in facilities utilization. For example, one organization might own the facility during a time block, but the same or a different organization might be assigned as the facility user.

Student Attendance

Student Attendance

  • In this case, the grain would be one row for each student who walks through the course’s classroom door each day.
  • This factless fact table would share a number of the same dimensions discussed with registration events. The primary difference would be the granularity is by calendar date in this schema rather than merely term.
  • This dimensional model, allows business users to answer questions concerning which courses were the most heavily attended. Which courses suffered the least attendance attrition over the term? Which students attended which courses? Which faculty member taught the most students?

What didn’t happen

What didn’t happen

  • We can also add explicit rows to the fact table for attendance events that didn’t occur.
  • Creating rows for events that didn’t happen is ridiculous in many situations.
  • We must ask what doesn’t exist while framing the NOT EXISTS within a larger query.

Multidimensional handling of what didn’t happen

Multidimensional handling of what didn’t happen

  • OLAP databases do an excellent job of helping users understand of what didn’t happen.
  • When the data cube is constructed , the multidimensional database handles the transaction database while minimizing the overhead burden of storing explicit zeroes.

More Educational Analytic Opportunities

More Educational Analytic Opportunities

  • Research grants and alumni contributions are key sources of revenue, in addition to the tuition revenue.
  • Research grants analysis is often a variation of financial analysis

- The grain would include additional dimensions to further describe the research grant

there is a strong need to better understand and manage the budgeted and actual spending associated with each research project.

-The objective is to optimize the spending so a surplus or deficit situation is avoided, and funds are deployed where they will be most productive.

-understanding research spending rolled up by various dimensions is necessary to ensure proper institutional control of such monies.

  • The university’s alumni is much like better understanding a customer base,

-Improved access to a broad range of attributes about the alumni population would allow the institution to better target messages and allocate resources.

-sIn addition to alumni contributions, alumni relationships can be leveraged for potential recruiting, job placement, and research opportunities.

Summary

Sammary

1

Summary

  • First; we looked at the accumulating snapshot fact table to track application or research grant pipelines which is very useful for tracking the current status of a short-lived process with standard milestones.
  • accumulating snapshots are often complemented with transactional or periodic snapshot tables.

2

Summary

Second; we explored several examples of factless fact tables. These fact tables capture the relationship between dimensions in the case of an event or coverage, but are unique in that no measurements are collected to serve as actual facts

-We also discussed the handling of situations in which you want to track events that didn’t occur.

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