Charlie Bess left a comment on my recent post that responded to his post in the EDS Fellows' Next Big Thing Blog regarding the abundance of data and the rarity of context. His comment needed a response, and my response got out of control, so it has turned into a post. His comment deserved wider visibility anyway, making a very good point, so I'm repeating it here. (Excuse me if I get tuna salad on your monitor; I'm writing this as I eat lunch.)
I think we do agree.
Physical proximity is the perfect example of meta data. This is data about the data we have on the word (or object if location is provided by RFID).
Although machines don’t have minds, they can have algorithms that handle “normal” situations, and should be able to recognize when it is not normal and bring this to people’s attention. People excel at pattern recognition and making decisions with insufficient information. So even if the system provides the context that it is not a normal situation and why, it should reduce the latency associated with the decision making process. Of course removing the noise of normal, should also be a tremendous benefit.
Charlie, I agree that we agree, with your clarification. My "disagreement" was
only with the use of the term "metadata", which for many people is
limited to explicit descriptions of the data, e.g., the data dictionary
in a relational database; the title, description, keywords, and Dublin
Core annotations found in HTML META tags; the DTD of an SGML or XML
document (or XML Schema document in the latter case); or the WSDL and
other descriptive annotations on Web Services. Your definition is clearly wider than that.
I think you have correctly stated what computers are good for at the
moment, beyond numeric data processing and symbolic computation
problems - to monitor aspects of the real world and to let us know when
something is outside the bounds of normality. It made me wonder how far we can go in teaching computers how to become (or rather to simulate being) contextually aware. I think it's possible, and here's why.