Bibliographic record
Abstract
Even though Data Lakes are efficient in terms of data storage, they increase the complexity of query processing; this can lead to expensive query execution. Hence, novel techniques for generating query execution plans are demanded. Those techniques have to be able to exploit the main characteristics of Data Lakes. Ontario is a federated query engine capable of processing queries over heterogeneous data sources. Ontario uses source descriptions based on RDF Molecule Templates, i.e., an abstract description of the properties belonging to the entities in the unified schema of the data in the Data Lake. This thesis proposes new heuristics tailored to the problem of query processing over heterogeneous data sources including heuristics specifically designed for certain data models. The proposed heuristics are integrated into the Ontario query optimizer. Ontario is compared to state-of-the-art RDF query engines in order to study the overhead introduced by considering heterogeneity during query processing. The results of the empirical evaluation suggest that there is no significant overhead when considering heterogeneity. Furthermore, the baseline version of Ontario is compared to two different sets of additional heuristics, i.e., heuristics specifically designed for certain data models and heuristics that do not consider the data model. The analysis of the obtained experimental results shows that source-specific heuristics are able to improve query performance. Ontario optimization techniques are able to generate effective and efficient query plans that can be executed over heterogeneous data sources in a Data Lake.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".