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Record W92905524

Data warehousing in an undergraduate curriculum

2007· article· en· W92905524 on OpenAlexaffabout
Ron McFadyen

Bibliographic record

VenueJournal of computing sciences in colleges · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsData warehouseDatabaseDatabase transactionComputer scienceOnline analytical processingCurriculumKey (lock)Dimensional modelingData scienceTransaction processing
DOInot available

Abstract

fetched live from OpenAlex

Databases and data warehousing are important technologies for modern enterprises. Whereas a traditional online transaction processing database is designed to support the day-to-day events that occur in an organization, a database in a data warehouse is designed to support the analysis of data. The data warehousing framework includes legacy databases (source), data warehouse databases (target), and processes to move data from source to target databases. The undergraduate program in Applied Computer Science at the University of Winnipeg includes four database courses. The 4th-year data warehousing course was the most recent addition to the curriculum. This paper describes the key components of this course, our experience, and future plans.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0070.005
Open science0.0020.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0390.018

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.124
GPT teacher head0.378
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2007
Admission routes2
Has abstractyes

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