Stephen Wolfram (Mathematica) wrote to let us know that Wolfram Research is releasing a new service, called Wolfram Data Drop. He thinks it could be a great tool for drone data, and indeed a cool demo is coming soon. In the meantime, you can see his son demonstrating an earlier use of Mathematica for drone data at Maker Faire above (more details on that here). 

Here's his blog post explaining it:

So what is the Wolfram Data Drop? At a functional level, it’s a universal accumulator of data, set up to get—and organize—data coming from sensors, devices, programs, or for that matter, humans or anything else. And to store this data in the cloud in a way that makes it completely seamless to compute with.

Data Drop data can come from anywhere

Our goal is to make it incredibly straightforward to get data into the Wolfram Data Drop from anywhere. You can use things like a web APIemailTwitterweb formArduinoRaspberry Pi, etc. And we’re going to be progressively adding more and more ways to connect to other hardware and software data collection systems. But wherever the data comes from, the idea is that the Wolfram Data Drop stores it in a standardized way, in a “databin”, with a definite ID.

Here’s an example of how this works. On my desk right now I have this little device:

This device records the humidity, light, pressure, and temperature at my desk, and sends it to a Data Drop databin. The cable is power; the pen is there to show scale.

Every 30 seconds it gets data from the tiny sensors on the far right, and sends the data via wifi and a web API to a Wolfram Data Drop databin, whose unique ID happens to be “3pw3N73Q”. Like all databins, this databin has a homepage on the web:

The homepage is an administrative point of presence that lets you do things like download raw data. But what’s much more interesting is that the databin is fundamentally integrated right into the Wolfram Language. A core concept of the Wolfram Language is that it’s knowledge based—and has lots of knowledge about computation and about the world built in.

For example, the Wolfram Language knows in real time about stock prices and earthquakes and lots more. But now it can also know about things like environmental conditions on my desk—courtesy of the Wolfram Data Drop, and in this case, of the little device shown above.

Here’s how this works. There’s a symbolic object in the Wolfram Language that represents the databin:

Databin representation in the Wolfram Language

And one can do operations on it. For instance, here are plots of the time series of data in the databin:

Time series from the databin of condition data from my desk: humidity, light, pressure, and temperature

And here are histograms of the values:

Histograms of the same humidity, light, pressure, and temperature data from my desk

And here’s the raw data presented as a dataset:

Raw data records for each of the four types of desktop atmospheric data I collected to the Data Drop

What’s really nice is that the databin—which could contain data from anywhere—is just part of the language. And we can compute with it just like we would compute with anything else.

So here for example are the minimum and maximum temperatures recorded at my desk:
(for aficionados: MinMax is a new Wolfram Language function)

Minimum and maximum temperatures collected by my desktop device

We can convert those to other units (% stands for the previous result):

Converting the minimum and maximum collected temperatures to Fahrenheit

Let’s pull out the pressure as a function of time. Here it is:

It's easy to examine any individual part of the data—here pressure as a function of time

Of course, the Wolfram Knowledgebase has historical weather data. So in the Wolfram Language we can just ask it the pressure at my current location for the time period covered by the databin—and the result is encouragingly similar:

The official weather data on pressure for my location nicely parallels the pressures recorded at my desk

If we wanted, we could do all sorts of fancy time series analysismachine learningmodeling, or whatever, with the data. Or we could do elaborate visualizations of it. Or we could set up structuredor natural language queries on it.

Here’s an important thing: notice that when we got data from the databin, it came with unitsattached. That’s an example of a crucial feature of the Wolfram Data Drop: it doesn’t just store raw data, it stores data that has real meaning attached to it, so it can be unambiguously understood wherever it’s going to be used.

We’re using a big piece of technology to do this: our Wolfram Data Framework (WDF). Developed originally in connection with Wolfram|Alpha, it’s our standardized symbolic representation of real-world data. And every databin in the Wolfram Data Drop can use WDF to define a “data semantics signature” that specifies how its data should be interpreted—and also how our automatic importingand natural language understanding system should process new raw data that comes in.

The beauty of all this is that once data is in the Wolfram Data Drop, it becomes both universally interpretable and universally accessible, to the Wolfram Language and to any system that uses the language. So, for example, any public databin in the Wolfram Data Drop can immediately be accessed by Wolfram|Alpha, as well as by the various intelligent assistants that use Wolfram|Alpha. Tell Wolfram|Alpha the name of a databin, and it’ll automatically generate an analysis and a report about the data that’s in it:

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Comment by Greg Dronsky on March 9, 2015 at 2:42pm

Nice. A bit clumsy but still great for a kid his age. Have to check it out , maybe i could use Mathematica in my segway robot project...


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