Improving the efficiency of information collection and analysis in widely-used IT applications (abstracts only)
Bibliographic record
Abstract
Modern IT environments collect and analyze increasingly large volumes of data for a growing number of purposes (e.g., automated management, security, regulatory compliance, etc.). Simultaneously, such environments are challenged by the need to minimize their environmental footprints. A general solution to this problem is to utilize IT resources more efficiently. This paper describes our work to systematically evaluate the inefficiencies in the information collection and analysis of several widely-used IT applications, to implement a more efficient solution, and to quantify the improvements. In particular, the logging of HTTP transactions by the Apache Web server and of network events by the Bro intrusion detection system are converted from text files to DataSeries. The costs of recording, storing and analyzing the information in the different formats are thoroughly evaluated and compared. We converted the text logs to DataSeries online, with no discernable overhead on the logging applications. We achieved upto a 7x decrease in the logfile sizes relative to the sizes of the default text logs, and speedups of 3x-8.4x to analyze the logfiles.
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".