Design and Implementation of Meteorological Big Data Platform Based on Hadoop and Elasticsearch
Bibliographic record
Abstract
With the launching of high resolution meteorological satellites and the development of high spatial and temporal resolution numerical models, the types and amounts of various meteorological data are increasing year by year. The existing relational databases are no longer able to meet the business requirements of real-time or non-real-time data storage, processing and retrieval. The Hadoop ecosystem, combining with the Elasticsearch cluster (ES cluster) is used to build the meteorological big data platform. The real-time data is processed by Kafka message queue, combing with the Storm DataAnly topology and finally enters the ES cluster. The non-real-time data is mainly processed by the file monitoring component. The file metadata information such as indexes is stored in the ES cluster. The files are saved in the HDFS. The implemented Big Data platform can process about 1.5 million real-time and non-real-time meteorological data per day, while the Elasticsearch cluster can provide ultrafast searching at a speed level of millisecond in a dataset of 2.0 million. Experiments show that the meteorological big data platform can meet the needs of modern meteorological business.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".