At this moment, there are exactly 604,844 registered corporations in the USA. The story of why I know that is pretty fascinating.
The act of computationally creating an answer via cognitive computing or conceptual reasoning rather than searching for it with text curiously gets described in so many ways, but nobody ever seems to talk about it directly, its always a talked about in terms of how it is done. I propose we call it “answer synthesis”. Let’s dig deeper.
Ubiquitous Computing, as a term, has been around for quite some time now. It refers to a state of computing in which there is a presence of data, interfaces, computing, etc, that is essentially omnipresent and is available for interaction in a wide variety of forms for a wide array of purposes. In essence, when people talk about the Internet of Things, they usually are describing what others refer to as ubiquitous computing. One of the aspects of this paradigm that makes it ubiquitous is a somehow-universal interoperability between all things connected.
Also, separate from that, there should be a sense of ambient intelligence that persists around all of these interacting agents. Obviously, interoperability, intelligence, high-availability, access, security, communication, data interoperability, data analysis, prediction, etc, are all under the umbrella of the term. However, is all of this really needing to be solved in order to have the user experience of having interoperability and ambient intelligence? I think not. Either way, there are lots of things to think about when it comes to putting your finger on what the real problems are that are left to solve in this space.
Partial updates are somewhat problematic in the world of RESTful applications. Currently, we use POST and PUT to write data or update it, but on sub-properties of data updates, it actually can get somewhat hard to code for when you get into the more subtle application logic and error management, let alone on datasets that are very large or have very deeply nested data structures in a single JSON object, for example.
But, regardless, PUT and POST have done a satisfactory job up until now, and nobody really needs to use PATCH in a relational context. But therein lies an interesting point: data is getting bigger, and naturally, semantic data is starting to become much more prevalent, and its URI-based. It logically follows that if data continues to become more semantic, and you’re dealing more often in deeply nested structures, you’ll need a URI-based updating method that can be more flexible than PUT and POST. But you don’t have to take my word for it, lets ask an expert.
When discussing triplestores and big data, people often ask me what the difference is between it and a traditional relational database. The following is a collection of everything I think you’d need to know or want to know in order to talk intelligently about the subject.
I was once told by one of my college professors, who coincidently had a PHD in Organic Chemistry from Harvard, that most of innovation and discovery is a matter of using something you already have to do things that it wasn’t meant to do. While there are a lot of examples of pure innovation and discovery that don’t necessarily follow that narrative, I do see what he means, and I tend to agree with him.