A three-dimensional data model in HBase for large time-series dataset analysis
Bibliographic record
Abstract
In the transition of applications from the traditional enterprise infrastructures to cloud infrastructures, scalable database management system plays an important role in efficiently managing and analysing unprecedented massive amount of data. Compared to RDBMSs, NoSQL databases, are more attractive in addressing this challenge. However, it is not easy to manage data in NoSQL database effectively for non-expert users because of the rare data-organization support. A poor data organization may accidentally abuse the features of NoSQL database and achieve unsatisfactory performance. Therefore, a systematic method for NoSQL database data-schema design is a timely and important problem for researchers and practitioners. HBase, as a particular NoSQL database offering, relies (a) on HDFS, for its distributed and replicated storage, and (b) on coprocessors, for efficient parallel query processing. To harness the potential parallelism benefits, an appropriate partitioning of the data across the HBase storage is required. we investigate the effectiveness of the three-dimensional data model, which uses the “version” dimension of HBase to store the values of a data item over time. We have experimented and evaluated the performance impact of this type of data model with two data sets, of different sizes and different time lengths. For each of these data sets, we have compared the performance of several ad-hoc queries, implemented with HBase Coprocessors framework, across different data schemas, some of which (do not) use the third HBase dimension. The experiment results demonstrate improved performance with the data schemas that use the third dimension of HBase.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".