Bibliographic record
Abstract
A Geographical Information System (GIS) is a system that captures, analyzes, and manages any spatially referenced data. One common problem in the GIS community is how to generate and publish customized web maps. The existing solutions either deal with spatial data directly which does not allow for applying the customized features, or require and rely on advanced and specialized programming skills. We believe that applying Service Oriented Architecture (SOA) to GIS can improve the interoperability of different GISs and can combine different GISs to provide customized web maps using a web service orchestration language. In this paper, we present a novel solution that applies SOA and Business Process Execution Language (BPEL) to orchestrate web map services into a customized web map. The process of requesting a map layer from a map service provider is an invocation of the remote GIS map service. The process of generating a customized web map becomes a process of combining different GIS map services into a BEPL process. This makes it possible to generate the business logic in BPEL first and then execute it to obtain a new map. Ideally, once the process is generated in BPEL, it can be plugged into any GIS system. This new solution generates a single new map after all layers are combined together, while the existing Asynchronous JavaScript and XML (AJAX) based solution gives a stack of map layers and the layers cannot be saved as one map. We have implemented a framework for the map creator to combine map layers published by different map service providers into a single new map, save the map composition process logic, and publish the new map as a service. Also, the framework provides map brokers more control of and easier interaction with the map composition process.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".