Cardinality Estimation in Streaming Graph Data Management Systems
Bibliographic record
Abstract
Graph processing has become an increasingly popular paradigm for data management \nsystems. Concurrently, there is a pronounced demand for specialized systems dedicated \nto streaming processing that are essential to address the continual flow of data and the \ninherent dynamism in streaming data. Yet, the lack of a standardized, general-purpose \nquery framework specifically for streaming graphs is a notable gap in existing technologies. \nThis shortfall emphasizes the necessity for a more comprehensive solution for processing \nand analyzing streaming graph data efficiently in real time. Enhancing this solution is \ncrucially dependent on improving the query processing pipeline, especially on cardinality \nestimation and query optimization, both of which are key factors in ensuring optimal \nsystem performance. \n \nIn this thesis, a novel cardinality estimation technique, called GraphSketch, that \nis tailored for streaming graph database management systems (GDBMS) is proposed. \nGraphSketch is a sketch-based framework designed to concisely summarize streaming \ngraphs, enabling both accurate and efficient cardinality estimations. The thesis delves \ninto the theoretical foundations of GraphSketch, outlining its conceptual design and the \nspecific methodologies employed in its construction. Additionally, the thesis elaborates \non the suitability of GraphSketch for streaming systems, highlighting its capability for \nincremental updates, which are pivotal in maintaining efficiency in the rapidly evolving \nenvironment of streaming data.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".