Bibliographic record
Abstract
Stream processing applications are becoming increasingly important in areas such as IoT, video analytics and social media. As a result, developers and operators must meet stringent time-to-market and scale requirements before bringing them to production. Unfortunately, testing a networked stream processing system is currently a cumbersome process that usually requires an expensive testbed and deep expertise on both networking and distributed systems. In this poster, we present Raptor, a tool for the fast prototyping of large-scale networked stream processing applications. Raptor builds on Mininet and Apache Kafka, two widely adopted platforms, to enable stakeholders to easily test their solutions under various operational conditions. Through a reasonably large setup (20 nodes) running on a single server, we show how unbalanced Kafka's leader selection algorithm can be and its implications on the overall system's throughput. We envision this work can help paving the way for more reproducible research in the stream processing domain, currently a first-class network application.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.100 | 0.098 |
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".