Bibliographic record
Abstract
In order to extend the expressive power of the query language to relational completeness which is beyond that of the structured sets, we defined a typed functional query language. The OLAP data objects and query constructs are uniformly represented by functional symbols typed by a polymorphic record-based type system. In order to model the OLAP-specific query constructs, our type system differs in several ways from the existing type systems. We studied the type checking and type inference problem of our type system. The algebraic properties of the functions in the functional OLAP query language can be described as equations. We show that the OLAP related equations can be oriented into an infinite but finitely representable term rewrite system which is both terminating and confluent. The term rewrite system forms the basis of a complete algorithmic decision procedure for query equivalence with respect to the OLAP equational theory. We further show that this rewrite system can be used to solve an equational-theoretic version of the OLAP summarizability problem. Our solution to the OLAP summarizability problem covers cases which have not been dealt with by other work in summarizability. This thesis focuses on the formal aspects of Online Analytic Processing (OLAP) databases and the underlying data model, known as the multidimensional data model. We begin by studying the hierarchical and multidimensional structures of OLAP databases from a set-theoretic approach. We define structured sets to be sets equipped with alphabets which are interpreted to nested subsets. We demonstrate that structured sets can be used to model hierarchical dimensions and multidimensional data cubes. Moreover, OLAP selection queries of the data cubes can be seen as descriptions of subsets of structured sets. We define the minimal length description (MDL) problem of the structured sets, and study its computational complexity, tractability and the algorithmic solutions. The solution to the MDL problem is applied to OLAP selection query optimization and OLAP query results summarization.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".