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Cybersecurity-Enhanced Business Intelligence

2025· book-chapter· en· W4409262508 on OpenAlexaff
Soobia Saeed, Mehmood Naqvi, Manzoor Hussain

Bibliographic record

VenueAdvances in information security, privacy, and ethics book series · 2025
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsMohawk College
Fundersnot available
KeywordsBusiness intelligenceComputer securityBusinessComputer scienceKnowledge management

Abstract

fetched live from OpenAlex

The purpose of this research is to improve the understanding and functionality of all Business Intelligence (BI) tools in sales and marketing teams within an organization, spanning management from upper management to operational staff. BW&BI are business warehousing and intelligence applications that would help organizations enhance the return on investment (ROI) and track operations in real-time. By giving improved analytics and actionable insights, organizations can make informed and efficient decisions. The project will implement fundamental concepts and techniques of BI in a data model built upon their organizational needs. The research emphasizes designing such an engaging and user-friendly environment that captures interest and eases access and operation of the case study. Thus, ultimately, the principal goal of this research is to build a DW and BI solution that efficiently supports both historical or structured and unstructured data.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.028
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.297
Teacher spread0.263 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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