Storage and Indexing of Relational OLAP Views with Mixed Categorical and Continuous Dimensions
Bibliographic record
Abstract
Journal of Digital Information Management ABSTRACT: Due to the widespread adoption of location-based services and other spatial applications, data warehouses that store spatial information are becoming increasingly prevalent. Consequently, it is becoming important to extend the standard OLAP paradigm with features that support spatial analysis and aggregation. While traditional OLAP systems are limited to data characterized by strictly categorical feature dimensions, Spatial OLAP systems must provide support for both categorical and spatial feature dimensions. Such spatial feature dimensions are typically represented by continuous data values. In this paper we propose a technique for representing and indexing relational OLAP views with mixed categorical and continuous data. Our method builds on top of an established mechanism for standard OLAP and exploits characteristic properties of space-filling curves. It allows us to effectively represent and index mixed categorical and continuous data, while dynamically adapting to changes in dimension cardinality during updates. We have implemented the proposed storage and indexing methods and evaluated their build, update, and query times using both synthetic and real datasets. Our experiments show that the proposed methods based on Hilbert curves of dynamic resolutions offers significant performance advantages especially for view updates. Categories and Subject Descriptors H.3.1[Content analysis and indexing]; E.1 [Data Structures]
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".