Efficient incremental data analytics with apache spark
Bibliographic record
Abstract
As smart electricity meters are becoming more popular and starting to replace conventional meters worldwide, new area of research for meter data analytics has emerged. Wide spectrum of computations in this context has been applied, ranging from computationally inexpensive tasks such as calculating monthly bills and peak usage, to elaborate computations to provide energy saving feedback to consumers in order to reduce peak energy demand. Examples include model building approaches for usage predictions and recommendations. Although research efforts in this field are progressing, majority of research in this domain still has overlooked the incremental aspects of energy data analytics, or in best cases, researches have not been able to properly utilize the incremental nature of the energy data. We have noticed that incremental approaches can significantly improve performance of smart meter analytics. For example, per-hour readings of a smart meter can efficiently become integrated with the previous readings and result in an incremental re-computation of a particular smart meter task. In this paper, we introduce UW Incremental Spark Analytics (UWISA), our incremental smart meter data platform, which applies efficient incremental techniques for calculating “energy-temperature” model (also called three-line model) [9]. Our platform can achieve better multi-core scalability and speedup of 4.5× (on average) compared to non-incremental implementation and speedup of higher than 2× when compared to previous incremental research for smart meter datasets up to tens of GBs. We also investigate the reasons behind better performance of incremental method when compared to the non-incremental and Spark Streaming approaches.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".