Computing A Well-Representative Summary of Conjunctive Query Results
Bibliographic record
Abstract
Data summarization is a powerful approach to deal with large-scale data analytics, which has wide applications in web search, recommendation systems, approximate query processing, etc. It computes a small, compact summary that preserves vital properties of the original data. In this paper, we study the data summarization problem of conjunctive query results, i.e., computing a k-size subset of a conjunctive query output, for any given k>0, that optimizes a certain objective. More specifically, we are interested in two commonly studied objectives: cohesion, which measures the maximum distance between a tuple in the query result tuples and its closest tuple in the summary (k-center clustering); and diversity, which measures the pairwise distances between the summary items. A simple approach that computes the entire query output and then applies existing algorithms on top of these materialized tuples suffers from high computational complexity because the query output can be large, e.g., for a relational database of N tuples, the number of result tuples can be N O(1). We propose O(1)-approximation algorithms that compute well-representative summaries of size k in time O(N*k O(1) ), or even O(N+ k O(1) ) in some cases, without computing all result tuples. We also propose the first efficient (2+\eps)-approximation algorithm for the k-center clustering problem over relational data. Our main idea is to formulate a few oracles that enable us to access specific query result tuples with certain properties, to show how these oracles can be implemented efficiently, and to compute desired summaries with few invocations of these oracles.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".