Bibliographic record
Abstract
Many businesses are implementing big data applications to improve efficiency and performance; and reduce costs and resource consumption.As digitization has become an integral part of everyday life, data collection has resulted in the accumulation of huge amounts of data that can be used in various beneficial application domains.Effective analysis and utilization of big data is a key factor for success in many businesses.This paper reviews the applications of big data to support businesses in key areas including E-Commerce, Human Resources, Customer Relationship Management, and Accounting.The review reveals that big data concepts are being used successfully and businesses have harvested it benefits both in financial and non-financial terms.The competitive nature of businesses that have emerged from enabled insight prompts for every business to ensure that they reap meaningful information from the internet and use it to create a business opportunity.Consequently, the significance of big data in developing value that can be turned to a potential commercial gap, created from insight, which can be exploited remains an area that has limited exploration from analytics in the discipline.It remains critical to evaluate the efficient business insight strategies that can be developed to ensure an optimized value addition that is based on accurate insight from the wild count of data source.Additionally, the study reveals that several opportunities are available for utilizing Big Data in different types of businesses; however, there are still many issues and challenges to be addressed to achieve better utilization of this technology.Consequently, there is much that remain unexplored on efficient Big Data approaches that can be used to gain value for business, especially now a time of acute business competition.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".