Event-driven response architecture for event-based computing
Bibliographic record
Abstract
Service-based computing is rapidly replacing the more-traditional approaches to architecting distributed systems. The critical advantage of service-based architectures is that they require only a specification of protocol, and not of API. As such, they engender a significantly looser coupling than prior techniques, thus facilitating seamless collaboration across systems and across administrative domains.A Service-Oriented Architecture (SOA) is a middleware platform that provides a service-based computing environment. The publish-find-bind paradigm at the core of SOA enables the development of service-provision software separately from the development of service-consumption software. Closer observation of each aspect in this paradigm reveals that significant developer involvement is still required to assist the interaction between service provider and consumer. Developers of service-consumer software make the decision to employ a set of service providers at development time. Some SOAs provide facilities to programmatically search, bind, and even invoke services dynamically. However, it is still assumed that knowledge of both service providers and the service provided is known at development time, or the client must supply highly-detailed information about services they wish to use. This severely limits the possibility of dynamic run-time interactions among service providers and service consumers.In this paper we introduce EDRA, the Event-Driven Response Architecture for service-based computing. EDRA is a software framework that provides an infrastructure to dynamically select client-relevant service providers during run-time. Information services selected by EDRA on behalf of clients may send notification events in case of changes in the service. In such cases, our runtime will automatically process the notification based on a selection of user-choice, system defaults, and available action services. We have implemented a prototype of our framework, and show its operation in the domain of airline services.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".