High performance framework for mining association rules from hierarchical data cubes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Online analytical processing (OLAP) is considered as a database prototype that gives a platform for the rich analysis of multidimensional data. A logical data structure known as the data cube often supports the OLAP. However, mining association rules from multidimensional data using OLAP techniques with data mining facilities is an issue of substantial complexity. In practice, the complexity is excited by the existence of dimension hierarchies that subdivide dimensions into aggregation layers of various granularity. Discovery of hierarchy-sensitive association rules can be very costly on large cubes. In this paper, we present an OLAP hierarchy-sensitive framework that supports the efficient and transparent manipulation of dimension hierarchies for extracting association rules from data cube. The experimental results show that, when compared to the alternatives, very slight overhead is required to handle streams of inter-dimensional association rules requests.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it