QDrill: Query-Based Distributed Consumable Analytics for Big Data
Bibliographic record
Abstract
Consumable analytics attempt to address the shortage of skilled data analysts in many organizations by offering analytic functionality in a form more familiar to in-house expertise. Providing consumable analytics for Big Data faces three main challenges. The first challenge is making the analytics algorithms run in a distributed fashion in order to analyze Big Data in a timely manner. The second challenge is providing an easy interface to allow in-house expertise to run these algorithms in a distributed fashion while minimizing the learning cycle and existing code rewrites. The third challenge is running the analytics on data of different formats stored on heterogeneous data stores. In this paper, we address these challenges in the proposed QDrill. We introduce the Analytics Adaptor extension for Apache Drill, a schema-free SQL query engine for non-relational storage. The Analytics Adaptor introduces the Distributed Analytics Query Language for invoking data mining algorithms from within the Drill standard SQL query statements. The adaptor allows using any sequential single-node data mining library (e.g. WEKA) and makes its algorithms run in a distributed fashion without having to rewrite them. We evaluate QDrill against Apache Mahout. The evaluation shows that QDrill outperforms Mahout in Updatable model training and scoring phase while almost keeping the same performance for Non-Updatable model training. QDrill is more scalable and offers an easier interface, no storage overhead and the whole algorithms repository of WEKA, with the ability to extend to use algorithms from other data mining libraries.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".