A three-dimensional data model in HBase for large time-series dataset analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it