Event Stream Processing with Beep Beep 3 : Log crunching and analysis made easy
Bibliographic record
Abstract
Event logs and event streams can be found in software systems of very diverse kinds. For instance, workflow management systems and ERP platforms produce event logs in some common format based on XML. Financial transaction systems also keep a log of their operations in some standardized and documented format, as is the case for web servers such as Apache and Microsoft IIS. Network monitors also receive streams of packets whose various headers and fields can be analyzed. Recently, even the world of video games has seen an increasing trend towards the logging of players’ realtime activities. Analyzing the wealth of information contained in these logs can serve multiple purposes. Business process logs can be used to reconstruct a workflow based on a sample of its possible executions; financial database logs can be audited for compliance to regulations; suspicious or malicious activity can be detected by studying patterns in network or server logs. However, the available tools to process logs or streams of events are often large systems that are hard to setup, and even simple examples seem needlessly complicated. In this book, you will learn about BeepBeep, a versatile Java library intended to make the processing of event streams both fun and simple. Through more than a hundred simple, illustrated code examples, you will see how running event processing tasks can be done in just a few lines of code—and what is more, code that you actually understand. From generating plots to computing statistics and evaluating temporal logic specifications, BeepBeep can prove a handy addition to a developer’s toolbox.
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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 | 0.006 |
| 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 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.027 |
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".