Blog: Dan E. Linstedt

« Data Modeling may also be stuck in 1985! | Main | Checking in on the Nanohouse Computer »

Nanotech lessons for Data Modelers

We could learn A LOT about information modeling from the nano molecular levels if we only paid attention. Self-assembly at the nanoscale provides many clues about how we should model our information systems as they grow. This blog entry highlights self assembly and its attributes: repeatable, consistent, and reliable.

"Although man's understanding of how to build and control molecular machines is still at an early stage, nanoscale science and engineering could have a life-enhancing impact on human society comparable in extent to that of electricity, the steam engine, the transistor and the Internet." -- Professor David Leigh, Edinburgh University

ComputerWorld reports that self assembly and mixed silicon circuits are 5 to 7 years off. However they do present some very interesting findings from the lead laboratories in the nation. Here we explore impacts, and apply the ideas to a cross-field: data modeling.

"The neat thing about SAMs is they're very well ordered," McGimpsey says. A field of these SAMs protrudes from the substrate at a well-defined angle—like a small patch of thick, well-tended grass—and can perform several duties, such as improving conductivity or increasing surface area. Such order, McGimpsey says, "means predictability of structure, and thus of properties." (from the ComputerWorld article mentioned earlier)

The order means predictable structure and properties - shouldn't we be taking our data modeling queues from nature? Our current data modeling efforts inside RDBMS engines is ancient history, 3rd normal form has only a few ties to natural structure. Our data models must reflect the natural models at the nanoscale. They need to be repeatable, predictable, and redundant. This is a foundation of the Nanohouse. See my web site for more information.

What does Order bring to the table?
Redundancy, fault-tolerance, control, scalability, repeatability are all attributes of order. If we can provide an ordered data model for our information systems (one that resonates with natural models) we can begin predicting how it will act under certain circumstances. We can also begin producing (automatically) the models that will house our data.

No matter how large the data sets grow, we can always predict exactly how it will perform – especially through the use of Fourier transforms and mathematical formulas. "Natural systems form nano-scale structures," Natural systems also provide accurate accounts of form and function. Why then do we in IT insist on creating artificial modeling elements in a 2 dimensional world to house our data? We should be solely focused on 3D modeling capabilities with repeatable and redundant design (ordered systems).

With IT moving toward SOA, we should also be focusing on the data model behind the scenes – can it self-assemble in the future? Can self-assembly mean self-maintaining data models? Can data models proactively change according to newly arriving stimuli? Can we teach our modeling systems (in the information industry) like chemical experiments? When will our data modelers finally learn that it’s about the FORM and FUNCTION, not just the data itself?

For now, focusing on the biological aspects of nano self-assembly can bring tremendous gains to the data modeling world – if for nothing else, housing huge quantities of information in an itsy-bitsy space and an ordered and repeatable fashion.

Do you believe Data Modeling needs an overhaul? Sound off!

Share: