Distributed evaluation of generalized path queries
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it