Big Data Processing Platform for Large Acoustic Datasets and Complex Data Pipelines: Leveraging Cutting Edge Open Source Software to Build Scalable Cost Effective Solutions
Bibliographic record
Abstract
This white paper explores the foundational Information Technology (IT) systems necessary to support the processing of large datasets, particularly acoustic data, within ocean observing organizations. As ocean monitoring technologies advance, the complexity and volume of collected data increase, necessitating robust data processing pipelines for quality control, standardization, and reprocessing. The paper addresses the challenges these organizations face, including budgetary constraints, diverse stakeholder needs, and stringent IT security policies, which complicate technology investment decisions. Utilizing the “Framework for Making Successful Technology Decisions” by the National Center for Applied Transit Technology (N-CATT), this study proposes a systems thinking approach to guide the IT decision-making process. The framework emphasizes empowering stakeholders and facilitating their leadership in decision-making. The paper details a structured approach encompassing problem definition, solution development, procurement, and implementation phases, with a specific focus on the first two phases. A novel technical solution is presented, leveraging open-source technologies and modern cloud computing architectures to address identified challenges. This solution includes a locally-hosted infrastructure with a Linux-based environment, a virtual private cluster for scalable computing resources, and a software architecture for data versioning and automation. By implementing these systems, organizations can ensure efficient and secure data processing, accommodate rapid changes in research requirements, and manage the inherent complexities of large-scale ocean data. The proposed architecture aims to maximize value, enhance data quality, and provide a scalable and sustainable IT infrastructure. Future work will extend this study to explore procurement and implementation strategies.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".