Big data management and mining models and their applications
Bibliographic record
Abstract
The world is dynamic, so are big data. The evolving challenges of managing big data volume, variety, veracity, validity, and velocity has resulted in several studies focusing on solving one or more of these perplexing issues. In this Ph.D. research, I focus on the evolving issues arising from big data variety, veracity, privacy, and accessibility. First, I design a conceptual model for capturing and storing variety of big data types including structured, semi-structured and unstructured data types and in addition, design a metadata collection framework for managing the big data in support of machine learning and open data FAIR principle of Findable, Accessibility, Interoperability and Re-usability such that the information about the data are available beyond the life cycle of the data. Second, I design hierarchical spatial-temporal model (HSTM) for managing individual record in big data in the aforementioned open data lake architecture with metadata collection framework. Third, I extend the HSTM and design the resulting hierarchical spatial-temporal privacy preserving model (HSTPPM) for preserving privacy of individual record in big data. Fourth, I extend and design applications of the HSTPPM to big data co-occurrence pattern mining and big data visualization.
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.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".