"data as a service" entries

Editor’s note: this is an excerpt from our new report Data: Emerging Trends and Technologies, by Alistair Croll. Download the free report here.

Here’s a look at some options in the evolving, maturing marketplace of big data components that are making the new applications and interactions we’ve been looking at possible.

Graph theory

First used in social network analysis, graph theory is finding more and more homes in research and business. Machine learning systems can scale up fast with tools like Parameter Server, and the TitanDB project means developers have a robust set of tools to use.

Are graphs poised to take their place alongside relational database management systems (RDBMS), object storage, and other fundamental data building blocks? What are the new applications for such tools?

Inside the black box of algorithms: whither regulation?

It’s possible for a machine to create an algorithm no human can understand. Evolutionary approaches to algorithmic optimization can result in inscrutable, yet demonstrably better, computational solutions.

If you’re a regulated bank, you need to share your algorithms with regulators. But if you’re a private trader, you’re under no such constraints. And having to explain your algorithms limits how you can generate them.

As more and more of our lives are governed by code that decides what’s best for us, replacing laws, actuarial tables, personal trainers, and personal shoppers, oversight means opening up the black box of algorithms so they can be regulated.

Years ago, Orbitz was shown to be charging web visitors who owned Apple devices more money than those visiting via other platforms, such as the PC. Only that’s not the whole story: Orbitz’s machine learning algorithms, which optimized revenue per customer, learned that the visitor’s browser was a predictor of their willingness to pay more. Read more…