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Article summary:
1. FINEdex is a fine-grained learned index scheme that improves the scalability and performance of memory systems.
2. FINEdex uses a flattened data structure to concurrently process requests with low overheads.
3. Experiments demonstrate that FINEdex improves performance by up to 1.8x and 2.5x compared to existing schemes.
Article analysis:
The article appears to be reliable and trustworthy, as it provides evidence for its claims in the form of experiments conducted on YCSB and real-world datasets, which demonstrate that FINEdex improves performance by up to 1.8x and 2.5x compared to existing schemes. The authors also provide open-source codes of FINEdex for public use in GitHub, which further adds credibility to their claims. Furthermore, the article does not appear to be biased or one-sided, as it presents both sides of the argument equally and does not make any unsupported claims or omit any points of consideration or evidence for its claims made. Additionally, there is no promotional content present in the article, nor does it present any partiality towards either side of the argument. Finally, possible risks are noted throughout the article, making it a reliable source of information on this topic.