Geopot: a Cloud-based geolocation data service for mobile applications
Bibliographic record
Abstract
We propose a novel Cloud-based geolocation data service system, termed ‘Geopot’, for location-based mobile applications. The exponentially growing number of users of location-based mobile applications demand a data service that can easily be deployed and is scalable against a large volume of accesses from mobile devices across the world. The purpose of our work is to construct a scalable spatial data service that leverages the powerful benefits of a Cloud-based storage. We focus on highly scalable check-in and nearby search service, which is a common focus of location-based mobile applications. Our system comprises two parts: a local data service for indexing and storing geolocation data of check-ins to achieve a compact spatial index database. This is based on an in-memory R-tree and a local hash table, and it utilizes a Cloud storage that enables global accesses. The local data service maintains a compact spatial index, with tempo-spatial clustering, in which check-ins are grouped by their distance through a time window. A centroid of a cluster is used as a spatial index of R-tree, and members of the cluster are stored in the local networked hash table temporarily. This insures that R-tree remains in a compact size that can fit into the system memory. The data stored in the local hash table will be published into the Cloud storage to allow users to access it remotely with the same quality of service. Publishing the local data to a Cloud not only insures staying within the specified storage size limits of the local data service, but also promotes scalable access to the Cloud from mobile clients. Our contribution lies in the design of a new scale-out data service architecture and in implementing this for mobile applications. We encourage the building of a mobile application with our proposed system as well as a low-cost Cloud data service for linking to a large-scale spatial database.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".