xINvisionQ
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xINvisionQ aims to provide the best forecasts by using a small sample of data. It's versatile enough for anyone to plug in data, run the program, and get informative results in a range of minutes to hours: no extra work required. If the data loaded in has certain regularities, a function will be outputted. If not, a probabilistic forecast (matrix) will be produced using decision trees.
By using a quantum-like evolutionary algorithm, utilizing superposition to superpose all possible outcomes and Darwinian natural selection to evolve the most satisfactory one to produce the forecast, xINvisionQ can be used for a whole spectrum of fields, from financial trading to retail sales to drug design (QSAR) and scientific discovery. Whether for business or personal use, xINvisionQ can be your own personal "AI machine" data analyst, one that puts you at the forefront of cutting-edge revolutionary technology.
xINvisionQ Google Cloud DataflowxINvisionQ's answer
xINvisionQ can be seen as taking an out-of-the-box approach to forecasting. By drawing from quantum superposition and Darwinian evolution, xINvisionQ marries two of the greatest scientific theories with genetic programming to be able to produce forecasts for data with and without certain regularities - all without requiring the user to input a single line of code or prepare vast amounts of data.
xINvisionQ's answer
xINvisionQ primarily draws from quantum superposition and Darwinian evolution. In brief we superpose all the possibilities and corresponding actions that can be taken, and by natural selection evolve the most satisfactory one as the outcome (forecast probability). Essentially for every forecast, if the data loaded in has certain regularities xINvisionQ will find them and produce a function, if not then decision trees will be utilized to produce a probabilistic forecast (matrix). More information can be found in a our two published papers: Decision-making under uncertainty - a quantum value operator approach and On Laws of Thought - A quantum-like machine learning approach which can be found at: https://link.springer.com/article/10.1007/s10773-023-05308-w and https://www.mdpi.com/1099-4300/25/8/1213 respectively.
xINvisionQ's answer
xINvisionQ originally started off as a scientific inquiry research purely on decision-making under uncertainty and then gradually shifted towards an economic theory of the stock market and eventually evolved, no pun intended to the commercial software application it is today. For those who are interested in the exact storyline details of xINvisionQ's humble beginnings please email us at info@xinvisionq.com, we'd love to tell you the entire story.
xINvisionQ's answer
Given the versatility and flexibility of xINvisionQ, anyone with data that needs to be forecasting or analzyed can use xINvisionQ to get informative results. From stock market trading and retail sales to QSAR of drug design and tidal wave currents, xINvisionQ can be used by anyone for business, academia, or personal use. Essentially xINvisionQ can be your own personal "AI machine economist", "AI machine trader", "AI machine sales analyst", or "AI machine scientist", one that puts anyone at the cutting edge forefront of revolutionary technology.
xINvisionQ's answer
xINvisionQ's simple to use interace allows anyone to load in a CSV or TXT file, run the program, and get informative results in a range of minutes to hours - all from their own laptop or phone. No coding required, no need to prepare a huge amount of data, or sit in front of your computer for hours on end - xINvisionQ stands out from the crowd in many ways. All you need is data, domain knowledge, and a laptop and you're ready to INvision the future with xINvisionQ!
Based on our record, Google Cloud Dataflow seems to be more popular. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 2 years ago
This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: over 2 years ago
I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: over 2 years ago
You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: over 2 years ago
It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / about 3 years ago
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