XBench - A Family of Benchmarks for XML DBMSs
Bibliographic record
Abstract
XML is beginning to be extensively used in various application domains, and as a result, large amounts of XML documents are being generated. Researchers in both industry and academia have proposed a number of approaches to e#ciently store, manipulate, and retrieve XML documents. The individual performance characteristics of these approaches as well as the relative performance of various systems is an ongoing concern. The range of XML application and the XML data that they manage are quite varied and no one database schema and workload can properly capture this variety. We propose a family of XML benchmarks, collectively call XBench, to measure and evaluate the performance of di#erent approaches to deal with the management of XML documents. The family is defined according to a classification of applications, and each class has its own database and workload. We discuss the general requirements for an XML DBMS benchmark, followed by a detailed explanation of the XBench, including the methodology of database generation, the workload, and the setup of test environment. A brief discussion of other existing XML benchmarks and comparison among them will be given as well. Contents 1
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".