Bibliographic record
Abstract
Flexibility and efficiency are the prime requirements for any P2P search mechanism. Existing P2P systems do not seem to provide satisfactory solution for achieving these two conflicting goals. Unstructured search protocols (as adopted in Gnutella and FastTrack), provide search flexibility but exhibit poor performance characteristics. Structured search techniques (mostly distributed hash table (DHT)-based), on the other hand, can efficiently route queries to target peers but support exact-match queries only. In this paper we present a novel P2P system, called distributed pattern matching system (DPMS), for enabling flexible and efficient search. Distributed pattern matching can be used to solve problems like wildcard searching (for file-sharing P2P systems), partial service description matching (for service discovery systems) etc. DPMS uses a hierarchy of indexing peers for disseminating advertised patterns. Patterns are aggregated and replicated at each level along the hierarchy. Replication improves availability and resilience to peer failure, and aggregation reduces storage overhead. An advertised pattern can be discovered using any subset of its 1-bits; this allows inexact matching and queries in conjunctive normal form. Search complexity (i.e., the number of peers to be probed) in DPMS is O (log N + zetalog N/log N), where N is the total number of peers and zeta is proportional to the number of matches, required in a search result. The impact of churn problem is less severe in DPMS than DHT-based systems. Moreover, DPMS provides guarantee on search completeness for moderately stable networks. We demonstrate the effectiveness of DPMS using mathematical analysis and simulation results
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".