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
Performance benchmarking has played an important role in the research and development in relational DBMS, object-relational DBMS, data warehouse systems, etc. We believe that benchmarking data mining algorithms is a long overdue task, and it will play an important role in the research and development of data mining systems as well. Frequent pattern mining forms a core component in mining associations, correlations, sequential patterns, partial periodicity, etc., which are of great potential value in applications. There have been a lot of methods proposed and developed for efficient frequent pattern mining in various kinds of databases, including transaction databases, time-series databases, etc. However, so far there is no serious performance benchmarking study of different frequent pattern mining methods. To facilitate an analytical comparison of different frequent mining methods, we have constructed an open test bed for performance study of a set of recently developed, popularly used methods for mining frequent patterns in transaction databases and mining sequential patterns in sequence databases, with different data characteristics. The testbed consists of the following components. A synthetic data generator, which can generate large sets of synthetic data in various kinds of data distributions. A few large data sets from real world applications will also be provided. A good set of typical frequent pattern mining methods, ranging from classical algorithms to recent studies. The method are grouped into three classes: frequent pattern mining, max-pattern mining , and sequential pattern mining . For frequent pattern mining, we will demonstrate Apriori, hashing, partitioning, sampling, TreeProjection, and FP-growth. For maximal pattern mining, we will demonstrate MaxMiner, TreeProjection, and FP-growth-max. For sequential pattern mining, we will demonstrate GSP and FreeSpan. A set of performance curves. These algorithms their running speeds, scalabilities, bottlenecks, and performance on different data distributions, will be compared and demonstrated upon request. Some performance curves from our pre-conference experimental evaluations will also be shown. An open testbed. Our goal is to construct an extensible test bed which integrates the above components and supports an open-ended testing service. Researchers can upload the object codes of their mining algorithms, and run them in the test bed using these data sets. The architecture is shown in Figure 1. This testbed is our first step towards benchmarking data mining algorithms. By doing so, performance of different algorithms can be reported consistently, on the same platform, and in the same environment. After the demo, we plan to make the testbed available on the WWW so that it may, hopefully, benefit further research and development of efficient data mining methods.
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 | 0.001 |
| Open science | 0.005 | 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