![]() ![]() ![]() Off the top of my head I could name 20 to 30 databases spread out across the different centres. One of my guys is working on enhancing the Lessons Learned database here to try and spread it across the various Lessons Learned databases we have. My group supports the entire centre here at Johnson Space Centre and to a degree NASA and some of the other centres so anybody, from any of the centres, can come and look at the information we have.Īn engineer working on Orion can come and look at the Lessons Learned database along with the engineers from any of the other projects. I understand you are using this with Project Orion. I do try to share information with organisations and I do, at least, try and share my publicly available data. So I collaborate with them and I also work with, and share information with, outside groups, Elder Research, BASF and Exxon Mobil. So I work with them a lot to look at different ways of doing things - to share my knowledge and get some knowledge from them. ![]() They aim to come up with different techniques on how we can analyse it, share it and visualise it. I've worked with people like the US Census Bureau, the Federal Reserve and we have a group of individuals here that we call the NASA Datanauts - a group of non-NASA individuals that are asked to join this group once a year for a collaboration exploring NASA Open Data. I collaborate with other organisations in order to look at information and kind of figure out new techniques and incorporate those techniques within the knowledge architecture framework. So I incorporated data science into that model and built upon that model to be able to expand upon it and build a mechanism that allows anybody, depending on what data they have, to utilise these concepts and get the information out of that data.Īre you working with other people or organisations on this? I thought that we had knowledge management and informatics but we still did not have a good way of extracting that knowledge from that data. I kind of liked that concept, but as I was looking through it, I realised that there was something missing and that was the data science piece. I did come across a gentleman, Tom Reamy, who talked about knowledge management from the scope of knowledge management and informatics. On Knowledge Architecture - if you do a search on it - you will find some things but people define it differently. It is something that I had read about but I have never come across anybody that uses these same three things. Is this Knowledge Architecture something that you developed yourself? I use those three things together to get that information which, in turn, allows me in the case of Neo4j to be a bit more robust in how I manage to present that information to my end users. I use correlation analysis to show documents that were similar to each other and to be able to get that correlation across different topics. I deal with the topic modelling that I apply to those lessons to be able to help users find the answers a lot faster. In the case of those lessons learned I apply topic modelling. The data science is how I group and cluster and identify the documents and actually get that knowledge out of there. ![]() The Informatics piece is the framework of the applications that I utilise to transmit the data to my end-users, in this case Neo4j. The Knowledge Management piece is the strategy - the different types of techniques and methods of how I store, create and identify my data. It is the convergence of those three things together that allows me to extract knowledge from my data. This is a combination of Knowledge Management, Informatics and Data Science. Priem in January 1993 and is headquartered in Santa Clara, CA.Meza: "Knowledge architecture is a combination of knowledge management, informatics and data science." Photo: Neo4j The company was founded by Jen Hsun Huang, Chris A. The All Other segment refers to the stock-based compensation expense, corporate infrastructure and support costs, acquisition-related costs, legal settlement costs, and other non-recurring charges. The Tegra Processor segment integrates an entire computer onto a single chip, and incorporates GPUs and multi-core CPUs to drive supercomputing for autonomous robots, drones, and cars, as well as for consoles and mobile gaming and entertainment devices. The GPU segment comprises of product brands, which aims specialized markets including GeForce for gamers Quadro for designers Tesla and DGX for AI data scientists and big data researchers and GRID for cloud-based visual computing users. It operates through the following segments: Graphics Processing Unit (GPU), Tegra Processor, and All Other. NVIDIA Corp engages in the design and manufacture of computer graphics processors, chipsets, and related multimedia software. ![]()
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