Beyond the tremendous level of activity around big data (data science, machine learning, data analytics…take your pick of the term) in research circles, I wanted to peek into some of the use cases for its adoption in the industries that deal with physical things, as opposed to digital objects, and draw some inferences about what conditions help adoption of the research that we do in academic circles.
The convergence of Internet of Things (IoT) and big data is not surprising at all. Industries with lots of small assets (think pallets on a factory floor) or several large assets (think jet engines) have been putting many many sensors on them. These sensors generate unending streams of data, thus satisfying two of the three V's of big data right there: velocity and volume. Next time you are on a plane and are lucky to be next to the wings, look underneath the wings and you will see an engine — if it is Rolls Royce or GE, it may even have been designed or manufactured in our backyard in Indiana. Engines like these are generating 10 GB/s of data that is being fed back in real time to some onboard storage or more futuristically streamed to the vendor's private cloud. This is one piece of the IoT-big data puzzle, the data generation and transmission. This is the more mature part of the adoption story. The more evolving part of the big data story is the analysis on all this data to make actionable decisions, and that, too, in double-quick time.
The second part of this story is in the analysis of all this data to generate actionable information. Talking to my industrial colleagues, there are five major use cases for such analysis:
Academia has been agog about this field of big data for, well, …seems like forever. We academics thirst for real use cases and real data and this field exemplifies this more than most. We need to be able to demonstrate that our algorithm and its instantiation in a working software system delivers value to some application domain. How do we do that? There is, of course, a lot of pavement pounding and trying to convince our industrial colleagues. Again talking to a spectrum, some factors seem to recur frequently. These are not universal across application domains, not by a long shot, but they are not one-off, either.
I think the domains of big data and IoT are destined to mutually propel each other. The first makes the latter appear smarter, even when the IoT system is built out of lots of small dumb devices. The latter provides the former with fruitful challenging technical problems. Big data algorithms here have to become small, run with a small footprint, a gentle giant in the land of many, many devices.
Saurabh Bagchi is a professor of electrical and computer engineering, and of computer science, at Purdue University, where he leads a university-wide center on resilience called CRISP. His research interests are in distributed systems and dependable computing, while he and his group have the most fun making and breaking large-scale usable software systems for the greater good.
No entries found