Bibliographic record
Abstract
As businesses continue to consume and produce ever-growing volumes of data, exploratory data analysis (EDA) is becoming an integral part of everyday operations. While online analytical processing (OLAP) systems in general - and column-oriented relational database management systems (RDBMS) in particular - are equipped with powerful tools to plough through petabytes of data, analytical queries may take seconds to execute, which is not always desirable in exploratory data analysis. Data scientists often need tools for fast visualization of data, and they are interested in identifying subsets of data that need further drilling-down before running computationally expensive analytical functions. In this paper, we describe our early work on extending SAP IQ (a disk-based columnar RDBMS) to support approximate query processing for exploratory data analysis using a technique known as query-time sampling. Specifically, we introduce two classes of novel samplers: (i) a stratified sampler with randomized row access to address the early-row bias problem in sampling, and (ii) hash-based equi-join samplers that are outlier-aware. We demonstrate how SAP IQ's polymorphic table function (PTF) technology can be utilized to implement these samplers as new query plan operators.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".