Bibliographic record
Abstract
An ever increasing number of organizations store their data using the XML data model. In this respect, the XML data model is used as encoding for information exchange, e-commerce messaging and complex data formats such as genome and protein data. These XML encoded data are stored in XML documents and a collection of XML documents is know as an XML database. In a simplified model, an XML document can be modeled as a tree. A challenge to the current database research is how to query these tree-encoded databases. The de facto XML query language is XQuery and there are many XQuery implementations ranging from simple XQuery prototypes to complex implementations that are part of database systems that provide XML support. The common denominator of these XQuery implementations is the immaturity of the XML query optimizer. The query optimization techniques proved to be the foundation of success for the object/relational database systems. We believe that without a sound query optimization technique querying large XML databases will not be feasible. In this thesis we present query optimization techniques for XML databases. These query optimization techniques are based on an original query optimization paradigm that we call two-level query optimization and in the context of this paradigm we introduce two novel optimization strategies, namely holistic path summary pruning and access-order selection. Furthermore, we propose the usage of path summaries (a concise representation of an XML instance) in the query optimizer as system catalogs. The query optimization techniques that we propose are employed at the access method selection level. An access method is a query operator that accesses a data sources. In this thesis we present three access methods: ToXStream, which works on streamed documents, TwigStackScan and ToXinScan that employ path summaries. The soundness of the optimization techniques that we propose are verified through experiments on XML query benchmarks. The experimental results suggest that, when applicable, these optimization techniques induce performance improvements up to two orders of magnitude over the state-of-the-art.
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.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".