▲ | Y_Y a day ago | |||||||||||||||||||||||||||||||
As you compose fuzzy operations your errors multiply! Nobody is asking for perfection, but this tool seems to me a straightforward way to launder bad data. If you want to do a quick check of an idea then it's probably great, but if you're going to be rigorous and use hard data and reproducible, understandable methods then I don't think it offers anything. The plea for citations at the end of the readme also rubs me the wrong way. | ||||||||||||||||||||||||||||||||
▲ | anishathalye a day ago | parent [-] | |||||||||||||||||||||||||||||||
I think semantic data processing in this style has a nonempty set of use cases (e.g., I find the fuzzy sorting of arXiv papers to be useful, I find the examples in the docs representative of some real-world tasks where this style of data processing makes sense, and I find many of the motivating examples and use cases in the academic work compelling). At the same time, I think there are many tasks for which this approach is not the right one to use. Sorry you didn't like the wording in the README, that was not the intention. I like to give people a canonical form they can copy-paste if they want to cite the work, things have been a mess for many of my other GitHub repos, which makes it hard to find who is using the work (which can be really informative for improving the software, and I often follow-up with authors of papers via email etc.). For example, I heard about Amazon MemoryDB because they use Porcupine (https://dl.acm.org/doi/pdf/10.1145/3626246.3653380). Appreciate you sharing your feelings; I stripped the text from the README; if you have additional suggestions, would appreciate your comments or a PR. | ||||||||||||||||||||||||||||||||
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