Indexing Time-Evolving Data With Variable Lifetimes
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
Many applications store data items for a pre-determined, finite length of time. Examples include slidingwindows over on-line data streams, where old data are dropped as the window slides forward. Previous research on management of data with finite lifetimes has emphasized on-line query processing in main memory. In this paper, we address the problem of indexing time-evolving data on disk for off-line analysis. In order to reduce the I/O costs of index updates, existing work partitions the data chronologically. This way, only the oldest partition is examined for expirations, only the youngest partition incurs insertions, and the remaining partitions "in the middle" are not accessed. However, this solution is based upon the assumption that the order in which the data are inserted is equivalent to the expiration order, which means that the lifetime of each data item is the same. We motivate the need to break this assumption, demonstrate that the existing solutions no longer apply, and propose new index partitioning strategies that yield low update costs and fast access times.
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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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