Migrating a Legacy Web-Based Document-Analysis Application to Hadoop and HBase
Bibliographic record
Abstract
Migrating a legacy application to a more modern computing platform is a recurring software-development activity. This chapter describes the authors’ experience with a contemporary rendition of this activity, migrating a Web-based system to a service-oriented application on two different cloud software platforms, Hadoop and HBase. Using the case study as a running example, they review the information needed for a successful migration and examine the trade-offs between development/re-design effort and performance/scalability improvements. The two levels of re-design, towards Hadoop and HBase, require notably different levels of effort, and as the authors found through exercising the migrated applications, they achieve different benefits. The authors found that both redesigns led to substantial benefit in performance improvement, and that expending the additional effort required by the more complex migration resulted in notable improvements in the ability to leverage the benefits of the platform.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".