Open-Source Innovation in Practice: A Lean-Based Development Process Leveraging Open-Source Big Data Tools
Bibliographic record
Abstract
Innovation depends on the exploitation of market potential with products that are aligned with customer needs. However, building innovative products is becoming gradually more challenging because of increased market volatility, uncertainty, complexity and ambiguity. In our Innovation Lab, inside an e-Procurement Company, we encountered several challenges when implementing an innovation process to develop an initial unstructured data processing Minimum Viable Product (MVP) based on opensource big data tools: (i) raising and prioritizing user demands; (ii) deciding about adequate tools; and (iii) understanding how to promptly set up a viable product. In this paper, we share our open-source innovation experience in bringing novel solutions to an oil company's suppliers. In general, we present and discuss how we have been applying our innovation process to create MVPs, and which technical decision helped us accelerate the MVP development in the presence of a large-scale, unstructured database and open-source big data tools. Overall, we believe the proposed lean-based development process can help practitioners and researchers who want to understand and improve their knowledge about lean products, how to build MVPs, and advance open-source innovation involving big data tools.
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 | 0.054 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".