Systematic Literature Review on Parallel Sorting and Searching Algorithms for Graph Analytics Problems
Bibliographic record
Abstract
This research paper aims to systematically review the literature published recently on the topic of parallel processing algorithms for sorting and searching methods. The paper emphasizesfinding the major gaps in the existing methods of using parallel sorting and searching methods for graph analytics problems and their use in scalable data science. Many peer-reviewed research papers and articles from international and national journals, conferences and other sources are searched using appropriate keywords and they are analyzed to understand the concept, current trend in the area and to find the research gaps, by employing a systematic review method of literature. The outcomes of this literature review haveresulted in the need to undertakea research work on "Design and Implementation of Parallel Sorting and Searching Algorithms for Graph Analytics Problems in Scalable Data Science". Also, we have identified the required research objectives and its plan of execution for the research proposal. Systematic literature review, Topic of research proposal along with research objectives, Listing of SWOT analysis of the research agendas, Listing of ABCD analysis of the research proposal
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 | 0.021 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".