How NASA is using knowledge graphs to find talent

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One of NASA’s biggest challenges is identifying where data science skills reside within the organization. Not only is data science a new discipline – it’s also a fast-evolving one. Knowledge for each role is constantly shifting due to technological and business demands.

That’s where David Meza, acting branch chief of people analytics and senior data scientist at NASA, believes graph technology can help. His team is building a talent mapping database using Neo4j technology to build a knowledge graph to show the relationships between people, skills, and projects.

Meza and his team are currently working on the implementation phase of the project. They eventually plan to formalize the end user application and create an interface to help people in NASA search for talent and job opportunities. Meza told VentureBeat more about the project.

VentureBeat: What’s the broad aim of this data led project?

David Meza: It’s about taking a look at how we can identify the skills, knowledge and abilities, tasks, and technology within an occupation or a work role. How do we translate that to an employee? How do we connect it to their training? And how do we connect that back to projects and programs? All of that work is a relationship issue that can be connected via certain elements that associate all of them together – and that’s where the graph comes in.

VentureBeat: Why did you decide to go with Neo4j rather than develop internally?

Meza: I think there was really nothing out there that provided what we were looking for, so that’s part of it. The other part of the process is that we have specific information that we’re looking for. It’s not very general. And so we needed to build something that was more geared towards our concepts, our thoughts, and our needs for very specific things that we do at NASA around spaceflights, operations, and things like that.

VentureBeat: What’s the timeline for the introduction of Neo4j?

Meza: We’re still in the implementation phase. The first six to eight months was about research and development and making sure we had the right access to the data. Like any other project, that’s probably our most difficult task – making sure we have the right access, the right information and thinking about how everything is related. While we were looking at that, we also worked in parallel on other issues: what’s the model going to look like, what algorithms are we going to use, and how are we going to train these models? We’ve got the data in the graph system now and we’re starting to produce a beta phase of an application. This summer through the end of the year, we’re looking towards formalizing that application to make it more of an interface that an end user can use.

VentureBeat: What’s been the technical process behind the implementation of Neo4j?

Meza: The first part was trying to think about what’s going to be our occupational taxonomy. We looked at: “How do we identify an occupation? What is the DNA of an occupation?” And similarly, we looked at that from an employee perspective, from a training perspective, and from a program or project perspective. So simply put, we broke everything down into three different categories for each occupation: a piece of knowledge, a skill, and a task.

VentureBeat: How are you using those categories to build a data model?

Meza: If you can start identifying people that have great knowledge in natural language processing, for example, and the skills they need to do a task, then from an occupation standpoint you can say that specific workers need particular skills and abilities. Fortunately, there’s a database from the Department of Labor called O*NET, which has details on hundreds of occupations and their elements. Those elements consist of knowledge, skills, abilities, tasks, workforce characteristics, licensing, and education. So that was the basis for our Neo4j graph database. We then did the same thing with training. Within training, you’re going to learn a piece of knowledge; to learn that piece of knowledge, you’re going to get a skill; and to get that skill, you’re going to do exercises or tasks to get proficient in those skills. And it’s similar for programs: we can connect back to what knowledge, skills, and tasks a person needs for each project.

VentureBeat: How will you train the model over time?

Meza: We’ve started looking at NASA-specific competencies and work roles to assign those to employees. Our next phase is to have employees validate and verify that the associated case — around knowledge, skills, abilities, tasks, and technologies — that what we infer based on the model is either correct or incorrect. Then, we’ll use that feedback to train the model so it can do a little bit better. That’s what we’re hoping to do over the next few months.

VentureBeat: What will this approach mean for identifying talent at NASA?

Meza: I think it will give the employees an opportunity to see what’s out there that may interest them to further their career. If they want to do a career change, for example, they can see where they are in that process. But I also think it will help us align our people better across our organization, and we will help track and maybe predict where we might be losing skills, where we maybe need to modify skills based on the shifting of our programs and the shifting of our mission due to administration changes. So I think it’ll make us a little bit more agile and it will be easier to move our workforce.

VentureBeat: Do you have any other best practice lessons for implementing Neo4j?

Meza: I guess the biggest lesson that I’ve learned over this time is to identify as many data sources that can help you provide some of the information. Start small – you don’t need to know everything right away. When I look at knowledge graphs and graph databases, the beauty is that you can add and remove information fairly easily compared to a relational database system, where you have to know the schema upfront. Within a graph database or knowledge graph, you can easily add information as you get it without messing up your schema or your data model. Adding more information just enhances your model. So start small, but think big in terms of what you’re trying to do. Look at how you can develop relationships, and try to identify even latent relationships across your graphs based on the information you have about those data sources.


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Tech News

Don’t be dull, NASA — let us explore some strange space moons

It’s been 30 years since Nasa last visited Venus, with the Magellan orbiter in 1990. Now, two new missions have been selected to explore the deadly atmosphere, crushing pressures, and volcanic landscape.

The process dates back to February 2020, when Nasa announced that four missions were to undergo a nine-month peer-review process for feasibility. They were all part of the Discovery program, started by Nasa in 1992 to bring together scientists and engineers to create exciting, groundbreaking missions. Set aside from the flagship missions – such as Curiosity and Perseverance – the missions operating under Discovery have taken unique and innovative approaches to exploring the solar system.

The two winning Venus missions, Davinci and Veritas, have been awarded US$500 million (£354 million) and will be launched some time between 2028 and 2030. But the competition was tough from the two losing missions, which would have gone to Io and Triton, respectively moons of Jupiter and Neptune. So what are we missing out on as a result?

Exploring Jupiter’s bizarre moon

Io is a strange moon – even among moons, which are strange, to begin with. As Jupiter’s innermost moon, orbiting a mere 350,000 km above the cloud tops, it gives Io an extreme heating mechanism that makes it the most volcanically active object in the solar system, sporting over four hundred volcanoes.

You might think, given we live on a planet with a fair share of volcanoes, that we’d have a good idea of where all this heat is coming from. In fact, according to Alfred McEwen, principal investigator on the proposed Io Volcanic Explorer or IVO mission, we’re still profoundly ignorant of how it actually works.

IVO was designed to perform multiple fly-bys of the moon and use a suite of instruments to map the activity on and below the surface. By collecting information on Io’s magnetic and gravitational fields, taking videos of the enormous lava eruptions and analyzing the gas and dust escaping from the moon, IVO would help scientists learn how Io’s heat is generated and lost.

All of this information is crucial – not just for awesome videos of space volcanoes – because this kind of extreme activity is believed to be an important aspect of planetary formation and evolution. By understanding the processes that drive change on Io, we can ultimately learn more about how planets and moons came to be.

The ice giants

The least explored and understood planets are Uranus and Neptune, and they are home to some of the most bizarre things in the solar system. Uranus has an axial tilt – the angle of its axis of rotation compared to the plane it orbits the Sun – so extreme that it spins on its side. This is thought to be the result of a giant collision in the solar system’s past.

Meanwhile, Neptune is home to the only large moon that orbits backward around its parent planet, the curious Triton. The peculiar orbital arrangement isn’t where the oddities end. The plane in which Triton orbits is offset by an extreme 23 degrees compared to Neptune’s, and it is believed to have moved to Neptune from the Kuiper Belt, the region beyond Neptune’s orbit filled with icy leftovers from the solar system’s formation.r, consider how safe they really are

Tech News

NASA better hurry up and put people on the Moon, bad space weather’s coming

Outer space is so often called a “cold void” that it’s easy to forget there’s weather up there too. You won’t see any thunderstorms or tornadoes in space, but our sun’s been cycling through various “seasons” for millions of years and scientists think they’ve figured out how to predict “extreme” weather events in our neck of the universe.

A team of researchers from the University of Reading today published research indicating the window for good weather might be closing for NASA’s plan to send a crewed spacecraft to the Moon in 2024.

Titled “Extreme Space-Weather Events and the Solar Cycle,” the team’s paper details a “seasonal” weather cycle involving the sun’s polar position and radiation bursts.

Per the research:

Here we use the 150-year aaH record of global geomagnetic activity with a number of probabilistic models of geomagnetic-storm occurrence to test a range of hypotheses.

We find that storms of all magnitudes occur more frequently during an active phase, centred on solar maximum, than during the quiet phase around solar minimum.

We also show that the available observations are consistent with the most extreme events occurring more frequently during large solar cycles than small cycles.

Finally, we report on the difference in extreme-event occurrence during odd- and even-numbered solar cycles, with events clustering earlier in even cycles and later in odd cycles.

In other words: the researchers determined that periods of extreme geomagnetic activity as measured on Earth can be attributed increased solar activity. By timing these “cycles,” the researchers hypothesize they can determine when extreme solar weather events are most likely to happen.

And, of course, the bad news is that we’re not far from entering one of these cycles.

The researchers say extreme activity tends to occur at the tail end of odd cycles and the beginning of even cycles – meaning, for example, there’s a higher likelihood of bad weather at the end of cycle 25 and the beginning of cycle 26.

Per a university press release:

The findings could have implications for the NASA-led Artemis mission, which plans to return humans to the moon in 2024, but which could be delayed to the late 2020s.

This could be problematic for NASA’s Artemis mission. The crewed Moon mission already exists on a truncated timeline and many experts believe 2024 is simply too soon for a viable, crewed mission.

One of the problems was that former president Donald Trump pushed NASA to move its original timeline of 2028 up to 2024 for apparently political reasons.

Luckily for NASA, the program appears to have survived the transition to the Joseph Biden presidency, but the 2024 date may be in jeopardy.

The problem: The scientists’ warning indicates extreme weather events are more likely from 2025 and on, until the next cycle starts toward the beginning of the 2030s. But this isn’t exactly a showstopper.

If you think about it like terrestrial “extreme weather,” it’s easier to understand. If there’s a storm in Cape Canaveral, we aren’t launching any space craft that day. And if there’s a hurricane, we might delay launch by days, weeks, or even months.

In space things are a bit different. We’re not so much worried about getting rained out as we are ensuring we’re taking all the environmental factors into consideration as we plan to, essentially, light a tiny piece of metal with people inside of it on fire and shoot it 384,400 kilometers into space. That’s the kind of thing you need to get the math just right for.

Quick take: This is absolutely no reason to speed up the launch. We’re not talking about moving your wedding date up a few days to avoid some cloudy days, we’re talking about risking the lives of humans in a mission that’s already been needlessly bumped up for PR purposes.

2028 is starting to sound like a more reasonable timeline again.

Repost: Original Source and Author Link

Tech News

NASA unveils plans for first-ever flight on Mars

When NASA’s Perseverance rover landed on Mars last month, it didn’t arrive alone.

Tucked under the belly of the buggy, a helicopter named Ingenuity had hitched a ride to the red planet. Its mission: to complete the first powered flight on another world.

Credit: NASA/JPL-Caltech/MSSS