Crawling rich internet applications: the state of the art
Bibliographic record
Abstract
Web applications have come a long way, both in terms of adoption to provide information and services, and in terms of the technologies to develop them. With the emergence of richer and more advanced technologies such as AJAX, web applications have become more interac-tive, responsive and user friendly. These ap-plications, often called Rich Internet Applica-tions (RIAs), changed the web applications in two ways: (1) dynamic manipulation of client-side state and (2) asynchronous communica-tion with the server. However, at the same time, such techniques also introduced new chal-lenges. One important challenge is the diffi-culty of automatically crawling these new ap-plications. Without crawling, RIAs cannot be indexed nor tested automatically. Traditional Copyright c © IBM Canada Ltd., 2012. Permission to copy is hereby granted provided the original copy-right notice is reproduced in copies made. Disclaimer: The views expressed in this article are the sole responsibility of the authors and do not necessarily reflect those of IBM. Trademarks: IBM and AppScan are trademarks or registered trademarks of International Business Ma-chines Corp., registered in many jurisdictions world-wide. Other product and service names might be trademarks of IBM or other companies. A cur-rent list of IBM trademarks is available on the Web at ”Copyright and trademark information ” at www.ibm.com/legal/copytrade.shtml. crawlers are not able to handle these newer technologies. This paper surveys the research on addressing the problem of crawling RIAs and provides some experimental results to com-pare existing crawling strategies. In addition, we provide some future directions for research on crawling RIAs. 1
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".