Distributed evaluation of generalized path queries
Bibliographic record
Abstract
Nowadays, we are required to deal with more complex data, prime examples of which are data on the Web, XML data, biological data, etc. There are already proposed abstractions to handle these kinds of data, in particular in terms of semistructured data models. A semistructured model conceives a database essentially as a finite directed labeled graph whose nodes represent objects, and whose edges represent relationships between objects. In this paper, we focus on path queries, which are considered the basic querying mechanism for semistructured data. In essence, such queries are used to navigate, or discover paths that conform to specifications captured by regular expressions. In order to make the navigation more useful, we consider generalized path queries, in which the symbols could optionally be weighted by numbers. Such numbers can express a variety of information about the data that the query could possibly match or navigate.Motivated by the plethora of today's applications utilizing Web services and peer-to-peer architectures, we present a distributed algorithm for evaluating generalized path queries. We follow a realistic model with distributed (non-shared) memory and message-passing between processors. An optimal solution to the problem lies in the intersection of ideas related to distributed query evaluation, distributed shortest path computation, and queueing systems.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".