Edge Computing with Big Data Cloud Architecture: A Case Study in Smart Building
Bibliographic record
Abstract
The growth of buildings embedded with technologies that can monitor the internal building environment with respect to energy consumption such as heating, ventilation, air conditioning, wind, motion as well occupancy have an immense potential. From energy management, occupancy administration, security maintenance as well as improving the health and quality of life for humans in indoor or outdoor spaces. These potentials can be realized by a clear understanding of the interplay between vast environmental conditions, humans and their health as well as the many smart products they interact with in their lives. This is a complex process that requires thorough testing and evaluation within smart buildings simulation environments where multiple buildings data can be generated and then effectively analyzed. This can be facilitated by a robust data management process that utilizes big data computing technologies to harness large volumes, variety and velocity of data that can be captured within smart buildings while maintain the security and privacy of data sources.In this paper we describe a smart building architecture that has been designed and developed for management of data from a smart building. In particular the architecture enables acquisition, processing and distribution of simulated environmental building data to multiple consumers and workflows for further processing and analysis locally and in a high performance cloud computing platform. The research premise is that such an architecture enables effective management of multiple data sources within climatic based simulated testing in smart buildings to further research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".