Real-time computation: a formal definition and its applications
Bibliographic record
Abstract
The concept of "real-time" has different meanings in the systems and theory communities. As a consequence, the currently available formal real-time models do not capture all the practically relevant aspects of such computations. We propose a new definition, and we believe that it allows a unfied treatment of all practically meaningful variants of real-time computations. Then, we use the developed formalism to model two important features of real-time algorithms, namely the presence of deadlines and the real-time arrival of input data. We also emphasize the expressive power of our model by using it to formalize aspects from the areas of real-time database systems and ad hoc networks. We offer formulations of the recognition problem for real-time database systems and the routing problem in ad hoc networks. Finally, we suggest a variant of our formalism that is suited for modeling parallel distributed real-time algorithms. We believe that the proposed formalism is a first step towards a unified and realistic complexity theory for real-time parallel computations.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".