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The big challenge for spatial computing is that there is no spatial computer science, and casual observers overlook this fact. As in, there is a dearth of compu

The big challenge for spatial computing is that there is no spatial computer science, and casual observers overlook this fact. As in, there is a dearth of computer science -- basic data structures and algorithms -- for efficiently reasoning about spatiotemporal relationships of the type common to multimodal sensor data models at scale. Neither Amazon nor Microsoft, companies mentioned in the article, are addressing this technology gap in a meaningful way (I have some contact with their programs). They are solving very narrow cases that don't generalize or scale usefully. A question I ask when doing due diligence on "spatial computing" technology is what computer science problems did you solve to create the technology? The common answer is "none" and most people dabbling in the space aren't even aware hard computer science problems exist as prerequisites to building such technology unless they've been banging their head against it for a while. That's the state of the spatial computing market today. We also know that it is (currently) impossible to build such a thing by sprinkling some magic spatial computing dust on open source technology stacks -- they can't express the necessary mechanics. You'll be writing a lot of code from first principles, it isn't a business execution problem. We've had petabytes of this type of sensor data for decades, and the vision and desire to build digital twins with that data, that part isn't new. There are hardcore technical reasons this doesn't exist as a real thing, it isn't that no one has ever thought of spatial computing before. To make matters worse, most of the companies with the expertise and desire to use such a platform lack the expertise and execution to create it. Creating such a platform has to come from somewhere else. It is possible to build a spatial computing platform today that actually works, the computer science challenges have solutions, but the combination of domain expertise and computer science skillset to execute it is extremely rare at the moment. That said, spatial computing will be a trillion dollar platform business once someone pulls it off.

waihtis 6y ago
Would you be willing to expand a bit more on the hard problems and their relation to spatial computing?
- jandrewrogers 6y ago
  The elementary operations for spatial computing are a mixture of complex constraint/event processing and ad hoc graph search, frequently on data models with types that are not representable as an integer and therefore lack the characteristic properties of integers like order and trivial partitioning. Three of the above are notoriously difficult to scale individually. For spatial computing we need to scale all three simultaneously in a single computational construct. And if that wasn't enough, these are real-time systems that need to support extremely high write throughput while all of those operations are happening -- offline batch reorganization isn't an option. These are petabyte scale systems in the trivial case (tens of TB in the most minimalist data models), so you aren't going to be building a bunch of indexes or similar. It is a vexing computer science and architecture problem. And even if you solved that elementary computing infrastructure problem, there are several more hard/interesting computer science problems related to topological types and conflict resolution that you'll likely need to solve before you have a useful system. It is headaches all the way down.
  - prox 6y ago
    Is this is a solution looking for a problem? As you mention something as such in your first comment? Personally I don’t see a need to this “maximizing efficiency” paradigm for spatial computing, which seems to be mostly driven by industry (the potential for making billions of dollars) not as an inborn desire to better this world. It’s not that I decline it, but the need seems somewhat artificial in a way.
    - birdyrooster 6y ago
      Needing new data structures? What would that even look like to create a generic data structure which was specifically useful to spatial computing? Why not use existing data structures and algorithms as has been done already?
      - electricshampo1 6y ago
        Try take a look at Spatial Sieve Tree (Inventor J. Andrew Rogers) https://patents.google.com/patent/US7734714B2/en https://patents.google.com/patent/US7734714B2/en https://www.jandrewrogers.com/2015/03/02/geospatial-databases-are-hard/ https://www.jandrewrogers.com/2015/03/02/geospatial-database... https://www.jandrewrogers.com/2015/10/08/spacecurve/ https://www.jandrewrogers.com/2015/10/08/spacecurve/ These might give a hint at why cs101 might not be enough for the scale and kind of systems he is talking about.
academia_hack 6y ago
I'm not convinced that scientific formalism is always necessary or valuable. "Computer Science" is an anachronism that arose due to the sort of people who built the first computers and the sort of reasoning they valued. It would have been bizarre to complain about how pointillism in art lacked fundamental theory when artists first embraced it. If people want to play around with a new approach to computing and spend some money doing so, I don't think having them sit down and prove theorems about optimal data structures is strictly necessary. Maybe it won't be mathematically perfect on the first go around, but who cares?
qPM9l3XJrF 6y ago
"That said, spatial computing will be a trillion dollar platform business once someone pulls it off." I'm skeptical. Seems like a solution in search of a problem. Who is willing to pay money for this?
- dmd 6y ago
  What could anyone possibly do with more than a few computers in the world? I mean, how many tables of sines and cosines can you possibly make?
  - qPM9l3XJrF 6y ago
    That doesn't answer my question.
zeckalpha 6y ago
> no spatial computer science It happens to be called something a bit different: https://en.m.wikipedia.org/wiki/Geographic_information_science https://en.m.wikipedia.org/wiki/Geographic_information_scien...

waihtis 6y ago

Would you be willing to expand a bit more on the hard problems and their relation to spatial computing?

jandrewrogers 6y ago
The elementary operations for spatial computing are a mixture of complex constraint/event processing and ad hoc graph search, frequently on data models with types that are not representable as an integer and therefore lack the characteristic properties of integers like order and trivial partitioning. Three of the above are notoriously difficult to scale individually. For spatial computing we need to scale all three simultaneously in a single computational construct. And if that wasn't enough, these are real-time systems that need to support extremely high write throughput while all of those operations are happening -- offline batch reorganization isn't an option. These are petabyte scale systems in the trivial case (tens of TB in the most minimalist data models), so you aren't going to be building a bunch of indexes or similar. It is a vexing computer science and architecture problem. And even if you solved that elementary computing infrastructure problem, there are several more hard/interesting computer science problems related to topological types and conflict resolution that you'll likely need to solve before you have a useful system. It is headaches all the way down.
- prox 6y ago
  Is this is a solution looking for a problem? As you mention something as such in your first comment? Personally I don’t see a need to this “maximizing efficiency” paradigm for spatial computing, which seems to be mostly driven by industry (the potential for making billions of dollars) not as an inborn desire to better this world. It’s not that I decline it, but the need seems somewhat artificial in a way.
  - birdyrooster 6y ago
    Needing new data structures? What would that even look like to create a generic data structure which was specifically useful to spatial computing? Why not use existing data structures and algorithms as has been done already?
    - electricshampo1 6y ago
      Try take a look at Spatial Sieve Tree (Inventor J. Andrew Rogers) https://patents.google.com/patent/US7734714B2/en https://patents.google.com/patent/US7734714B2/en https://www.jandrewrogers.com/2015/03/02/geospatial-databases-are-hard/ https://www.jandrewrogers.com/2015/03/02/geospatial-database... https://www.jandrewrogers.com/2015/10/08/spacecurve/ https://www.jandrewrogers.com/2015/10/08/spacecurve/ These might give a hint at why cs101 might not be enough for the scale and kind of systems he is talking about.

academia_hack 6y ago

I'm not convinced that scientific formalism is always necessary or valuable. "Computer Science" is an anachronism that arose due to the sort of people who built the first computers and the sort of reasoning they valued. It would have been bizarre to complain about how pointillism in art lacked fundamental theory when artists first embraced it. If people want to play around with a new approach to computing and spend some money doing so, I don't think having them sit down and prove theorems about optimal data structures is strictly necessary. Maybe it won't be mathematically perfect on the first go around, but who cares?

qPM9l3XJrF 6y ago

"That said, spatial computing will be a trillion dollar platform business once someone pulls it off." I'm skeptical. Seems like a solution in search of a problem. Who is willing to pay money for this?

dmd 6y ago
What could anyone possibly do with more than a few computers in the world? I mean, how many tables of sines and cosines can you possibly make?
- qPM9l3XJrF 6y ago
  That doesn't answer my question.

zeckalpha 6y ago

> no spatial computer science It happens to be called something a bit different: https://en.m.wikipedia.org/wiki/Geographic_information_science https://en.m.wikipedia.org/wiki/Geographic_information_scien...