Bibliographic record
Abstract
If your objective in business intelligence reporting is Business Objects success, this is the resource for you. Gives a thorough run-down of the software, plus coverage of Web intelligence, complex queries, multidimensional analysis, and more. Author Cindi Howson has plenty of hands-on experience with the product. Table of contents Part I: Getting Ready for BusinessObjects 1: Introduction to Business Intelligence 2: Goals of Deploying BusinessObjects 3: Segmenting Your Users 4: Marketing BusinessObjects Part II: A Better Universe 5: Universe Design Principles 6: Using Designer to Build a Basic Universe 7: Universe Joins 8: Classes and Objects 9: List of Values 10: Advanced Objects 11: Multidimensional Analysis 12: Incorporating Supervisor Settings into the Universe Design 13: Design Principles: Where to Put the Intelligence 14: Minimizing Universe Maintenance 15: Almost There Part III: Reporting with BusinessObjects 16: Introduction to Reporting 17: Report and Chart Formatting 18: Analyzing the Data: the Slice and Dice Panel 19: Exploring the Data: Multidimensional Analysis 20: Accessing New Data 21: Creating a New Query 22: Complex Queries 23: WebIntelligence 24: WebIntelligence Version 6.0 Bibliography
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.906 | 0.924 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".