Research on Urban Water Big Data Platform based on Microservices Architecture
Bibliographic record
Abstract
With the continuous advancement of urban water management construction, various departments have established many business systems in the early stage. Due to the different times of information construction, there is a lack of unified data resource standards and application standards, and the problem of lack of data support in analysis and decision-making is becoming increasingly prominent. In response to the increasing problem of information silos, this article proposes a design of urban water big data platform based on microservices architecture. The server is developed using Java language, Spring Cloud framework as the backend development framework, and VUE 2.0 framework as the frontend development framework. The platform covers both offline and real-time data warehouses, with data computation using Spark and written in SCALA language. The offline data warehouse is implemented using Hive, providing a data computation support platform for different business scenarios. This study conducted corresponding testing and analysis on the performance of the platform. The success rate of data collection on the platform reached 89.1%, the data lineage coverage in the data warehouse accounted for 86%, and the success rate of interface calls for data applications reached 90%.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".