Peer-to-Peer content search supported by a distributed index in a publication/search model
Bibliographic record
Abstract
Peer-to-peer networks (P2P) are considered a valid approach for the construction of distributed systems. Further research projects in the last few years have focused on using this kind of networks as an alternative for solving different situations that have traditionally required centralized servers, such as search engines. This paper deals with the problem of content search in highly distributed and dynamic environments. We propose and evaluate a distributed index model built upon a peer-to-peer network which supports complete indexing of text documents and allows searching by content. A distinctive feature of this proposal is that it requires no specific network topology or hierarchy. Evaluations with different settings were performed by simulating a 10,000-node network, where each node had the capability to share documents.With regard to the traffic generated, experiments show an improvement in efficiency of between 84% and 93% over similar systems like Gnutella. The evaluation of retrieval performance using a test collection showed that the P2P system was able to achieve the same level of performance as the centralized system. It was also found that the amount of traffic generated by this model varies between 80 and 225 Kb per set of query and answers.
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 | 0.008 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.312 | 0.361 |
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".