I created PoC for merging similar system requirements and performing gap analysis between vendor documentation and internal company SOPs and policies.
Everything in Python flask + Azure OpenAI. I am working on moving AI part to Claude Sonet. Seems that results are bit different since GPT model has more rough approach to merging requirements and also less sensitive to finding gaps.
Keep in mind that many of the manuals at this link are declared obsolete because of outdated medical practices such as sucking the poison out of snake bites and such.
I would aim for guides published on or after circa 2008.
From my experience, these resources are worth read:
[1] Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop
Andreas Brandmaier's permutation distribution clustering is a method rooted in the dissimilarities between time series, formalized as the divergence between their permutation distributions. Personally, I think this is your "best" option
http://cran.r-project.org/web/packages/pdc/index.html
Eamonn Keogh's SAX (Symbolic Aggregate Approximation) and iSAX routines develop "shape clustering" for time series
There are approaches based on text compression algorithms that remove the redundancy in a sequence of characters (or numbers), creating a kind of distance or density metric that can be used as inputs to clustering, see, e.g.:
This paper by Rob Hyndman Dimension Reduction for Clustering Time Series Using Global Characteristics, discusses compressing a time series down to a small set of global moments or metrics and clustering on those:
Chapter 15 in Aggarwal and Reddy's excellent book, Data Clustering, is devoted to a wide range (a laundry list, really) of time-series clustering methods (pps 357-380). The discussion provides excellent background to many of the issues specific to clustering a time series"
I agree. We’ve recently migrated to sales force. We like it. But we started with a mishmash of bugzilla, Trello, and google sheets for managing sales leads, production planning and service issues. As our needs grew it made sense to move to something like SF. But we totally did not need that on day 1.
As was already mentioned. Trustness is the key. You need to be a trusted partner for such sensitive type of work.
Wrong people with root access to core infrastructure can make much more damage and cost more than problem which isn't solved at all.
There is also no space for learning on-the-fly. You have to know what you are doing, and how to perform certain steps correctly (t-shoot, recovery, migration).
From my experience, almost all my contracts (> 90%) came from word-of-mouth. Almost all online and printed advertisements, online profiles were waste of my time and resources.
Also, if friend of mine recommend me to someone else, I can believe that I will get paid (or there is only low risk). In case of blind-contract via internet, there is still a bigger risk that you will not get paid. I am not sure if I can (and want) trust published ratings on such web-pages.
Everything in Python flask + Azure OpenAI. I am working on moving AI part to Claude Sonet. Seems that results are bit different since GPT model has more rough approach to merging requirements and also less sensitive to finding gaps.