XBench - A Family of Benchmarks for XML DBMSs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
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 | 0.000 | 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