Wednesday, September 25, 2013

Data format required for streaming platform alpha launch

In response to several recent questions, here's a quick update on the data structure our system can handle for now.  

Input.  What the data should look like.  Each piece of data represents a point in time.  Your sensor is gathering data and then emitting (should use this term) it.  You don't specifically mark the time on each piece of data but as they are created they are sent to the system. 

Each piece of data (or if you want to call it an event) has some properties associated with it.  It depends on what your sensor is designed to do.  For example for a gyroscope sensor.  It will emit rotation of the x, y, and z axis.  For every sample that the sensor emit it might look like gyro.x = -84.2, gyro.y = -132.2, gyro.z = -80.  All of that data represents 1 event.  You can send as many of these "events" to as you want.

Most sensors can emit at a very high frequency.  You can try to send all that data into (which it can handle) but is your internet connection good enough to send all that data?  Or do you need that high of a sample rate for your application?  What is the right sample rate?  Thats another topic for another blog.

Once you have all this time series data streamed in you can do some very cool stuff with it.  For our users, the most important thing to do with the data is classify it WITHOUT having to pre-define the device state.  What that means, is being able to tell from the raw data what's going on, not requiring pre-set parameters that define the events ("right now I am running, all data coming in is related to running") for a specific period of time.

We'll have more on this in our support FAQ.

Friday, September 13, 2013 Now Providing Machine Learning For Streaming Data

The team is excited to announce that we will now be providing a version of our machine learning platform specifically for streaming data. We will be supporting flat data from any type of connected device.
This offering comes as a result of customer demand from the past several months. We had initially planned to launch our streaming platform in Q1 of 2014. However the number of requests we’ve received for streaming support convinced us that the time was right now to bring our Beta offering to market.
So, what are the top three problems this platform solves for our customers?

First, turning sensor data into useful information is not easy. Many of our customers have limited data science resources on their teams. Developing and moving machine learning algorithms into production is a challenge. Our catalog of machine learning algorithms provides a complete data science solution out of the box. This is especially useful for startups looking to raise capital as our platform provides a Big Data and Machine Learning story for pitches.

Second, time to market. The wearables market is exploding, and being able to provide valuable apps to users based on device data is critical. Our platform provides all the infrastructure needed to ingest, store, predictively model and return results that power consumer web and mobile apps. This has eliminated months of development time which allow our customer to stay focused on delighting their customers and building awesome developer communities.

Third, scaleability. It’s one thing to build out the infrastructure, it’s another to scale it across thousands of devices and millions of events. Our cross-cloud architecture automatically scales with our customers business. It leverages multiple geographies across multiple cloud providers to ensure service is never interrupted.

For our technical friends, the platform includes:
  • Web Sockets For True Streaming
  • Multiple Classification Algorithms
  • Time-Series Data Storage
  • Streaming Visualizations
  • API for Developers
You can find more information on the platform here:
We’re now accepting new users for our Beta. If interested in joining the beta, please email me at andy(at)