TANGO: A Flexible Mobility-Enabled Architecture for Online and Offline Mobile Enterprise Applications
Bibliographic record
Abstract
A mobility-enabled architecture is usually required to facilitate the access of enterprise systems from mobile devices. Typically, mobility enabled architectures are separated in online architectures and offline architectures. The online architectures enable mobile clients to use the online model in which mobile clients access business logic and data located on backend systems using remote invocations. The offline architectures enable mobile clients to use the offline model in which mobile clients access business data locally on devices, and periodically synchronize data with backend systems. The selection of the architecture is based on the needs of the scenarios in accordance with the user requirements. For an enterprise system, there is usually more than one mobile scenario, and hence both online and offline architectures might be required. In this case, the discrepancies between the two architectures will increase the cost and decrease the interoperability between different solutions. In order to solve these problems, this paper proposes an innovative and lexible mobility enabled architecture that can be configured to be an online, an offline, or a mixed architecture in a unified framework. Using this architecture, different types (online, offline, or mixed) of mobile client applications could be built for different scenarios according to the user requirements. These applications can share the same mid-tier framework and access the same backend systems. Moreover, in this architecture, a unified access model to business logic is proposed to make it possible to adapt in time a mobile application from the online model to the offline model without architectural changes and vice versa. This paper introduces the basic requirements to build such an architecture, defines basic modules of the architecture and shows how to adapt this architecture to different (online, offline, or mixed) architectures using different configurations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".