A Schema Integration Approach for Big Data Analysis
Bibliographic record
Abstract
A huge volume of data is analyzed by organizations to understand their clients and improve their services. In many cases, these data are stored separately in different database systems and need to be integrated before being used in analysis tools or prediction applications. One of the main tasks of data integration process is the definition of the global schema. Defining a global schema in the context of NoSQL systems is a demanding task since it necessitates dealing with a variety of issues, including the lack of local schemas, data model heterogeneity, and semantic heterogeneity. To address these challenges, this work aims to automatically define the global schema of a set of databases stored in heterogeneous NoSQL systems. The main contributions of this work are presented in three phases: (1) Schema extraction where we define the local schemas using a unified representation. (2) Schema matching in which we propose a hybrid approach to find matching attributes between the local schemas. (3) Schema integration where we define the global schema using the schema matching results. A Covid-19 use case as well as other benchmarks are presented in this paper to evaluate the results of the proposed approach and illustrate its effectiveness.
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 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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".