A pattern-oriented and model-driven architecture for interactive systems
Bibliographic record
Abstract
Day-to-day experiences suggest that it is not enough to approach a complex design equipped with design tips, guidelines, and hints. Developers must also be able to use proven solutions emerging from the best design practices to solve new design challenges. Without these, the designer is unable to properly apply guidelines or take full advantage of the power of technology, resulting therefore in poor performance, poor scalability, and poor usability. Furthermore, the designer might "reinvent the wheel" when attempting to implement a design solution. \n \nA number of design problems continue to arise, such as: (1) decoupling the various aspects of interactive systems (for example, business logic, the UI, navigation, and information \narchitecture) and (2) isolating platform specifics from the concerns common to all interactive systems. \n \nIn the context of a proposal for a Pattern-Oriented and Model-driven Architecture (POMA) for interactive systems, this thesis identifies an extensive list of pattern categories and types of models aimed at providing a pool of proven solutions to these problems. The models of patterns span several levels of abstraction, such as domain, task, dialog, presentation and layout. The proposed POMA architecture illustrates how several individual models can be combined at different levels of abstraction into heterogeneous structures which can then be used as building blocks in the development of interactive systems. \n \nThis document is divided into six chapters: the first chapter presents a background and related work on "Patterns" in general and on various architectures for interactive systems development such as "N-tiers architectures", "Pattern-Oriented Design" (POD), "Pattern- Supported Approach" (PSA), and "Model-Driven Architecture" (MDA). The second chapter introduces the research topic with its objectives, its limits, the research methodology, and research steps. The third chapter describes primarily the most important parts of the research which is the development of a new architecture called Pattern-Oriented and Model-Driven Architecture, facilitating the development of interactive systems including fundamentals and key concepts, an overview, justifications versus N-tiers, POD, PSA, and MDA architectures and specifications. The fourth chapter describes architectural levels and categories of patterns used in POMA. The fifth chapter describes the categories of models used in POMA. The sixth chapter presents an exploratory case study applied to the architecture proposed in this research. The last chapter presents a conclusion on this research work and its expected evolution in the future.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".