Data catalog tools: A systematic multivocal literature review
Bibliographic record
Abstract
A data catalog enables an organization to maintain an inventory of its data assets by collecting and managing the relevant metadata. We conducted a systematic multi-vocal literature review on data catalogs to understand their features and usage. We systematically selected and analyzed 86 literature sources and 39 catalog tools. We first utilized the findings from the literature to develop a classification framework comprising 24 fine-grained and five high-level features, along with three maturity levels. Next, we analyzed 39 tools based on the classification framework. Organizations typically include a data catalog as a component in their big data platforms and use it to support the various phases of the metadata management lifecycle. Hence, we also mapped the catalog features to the requirements of metadata-driven big data architectures, namely data mesh, data lake, and data lakehouse. Moreover, the mappings of the features to the phases in a metadata management lifecycle were developed. Our findings shall aid organizations in making informed decisions when choosing data catalog tools and help researchers identify the critical research issues in data cataloging and metadata management. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board .
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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".