Columnar Storage and List-based Processing for Graph Database Management\n Systems
Bibliographic record
Abstract
We revisit column-oriented storage and query processing techniques in the\ncontext of contemporary graph database management systems (GDBMSs). Similar to\ncolumn-oriented RDBMSs, GDBMSs support read-heavy analytical workloads that\nhowever have fundamentally different data access patterns than traditional\nanalytical workloads. We first derive a set of desiderata for optimizing\nstorage and query processors of GDBMS based on their access patterns. We then\npresent the design of columnar storage, compression, and query processing\ntechniques based on these desiderata. In addition to showing direct integration\nof existing techniques from columnar RDBMSs, we also propose novel ones that\nare optimized for GDBMSs. These include a novel list-based query processor,\nwhich avoids expensive data copies of traditional block-based processors under\nmany-to-many joins, a new data structure we call single-indexed edge property\npages and an accompanying edge ID scheme, and a new application of Jacobson's\nbit vector index for compressing NULL values and empty lists. We integrated our\ntechniques into the GraphflowDB in-memory GDBMS. Through extensive experiments,\nwe demonstrate the scalability and query performance benefits of our\ntechniques.\n
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".