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Dan Linstedt

Bill Inmon has given me this wonderful opportunity to blog on his behalf. I like to cover everything from DW2.0 to integration to data modeling, including ETL/ELT, SOA, Master Data Management, Unstructured Data, DW and BI. Currently I am working on ways to create dynamic data warehouses, push-button architectures, and automated generation of common data models. You can find me at Denver University where I participate on an academic advisory board for Masters Students in I.T. I can't wait to hear from you in the comments of my blog entries. Thank-you, and all the best; Dan Linstedt http://www.COBICC.com, danL@danLinstedt.com

About the author >

Cofounder of Genesee Academy, RapidACE, and BetterDataModel.com, Daniel Linstedt is an internationally known expert in data warehousing, business intelligence, analytics, very large data warehousing (VLDW), OLTP and performance and tuning. He has been the lead technical architect on enterprise-wide data warehouse projects and refinements for many Fortune 500 companies. Linstedt is an instructor of The Data Warehousing Institute and a featured speaker at industry events. He is a Certified DW2.0 Architect. He has worked with companies including: IBM, Informatica, Ipedo, X-Aware, Netezza, Microsoft, Oracle, Silver Creek Systems, and Teradata.  He is trained in SEI / CMMi Level 5, and is the inventor of The Matrix Methodology, and the Data Vault Data modeling architecture. He has built expert training courses, and trained hundreds of industry professionals, and is the voice of Bill Inmons' Blog on http://www.b-eye-network.com/blogs/linstedt/.

That's right, Terminator as in T2 Eyeballs. Well, not really that advanced (yet). I just read in May's issue of Scientific American about nanomorphing silicon implants that take the place of damaged light recognition cells in the back of the eye, basically allowing a blind person to "see" images and outlines. They admit the resolution isn't that hot yet, but it will advance like everything else.

This article will explore Form and Function, and discuss the nature of adaptable neural models, and what it means to build a system that could potentially mimic the human brain.

According to the article, the brain can operate at 10 billion synapse firings per second. Who's Synapse? What's a Synapse? and Why does it Fire? For answers to those questions and more, see your local brain surgeon. (just kidding).

Here's the poor mans definition: imagine for a minute a series of interlinked spider webs. Got the picture? Ok. Now, imagine the spider on the center of each of the web. Each center of the web represents a term called a neuron. Each part of the web spanning outward, let's call that a synapse. Where one web attaches to another, let's call that a dendrite (receptor).

A spider catches prey by first, having a sticky web - second by feeling the vibrations caused on the web when something gets stuck there. Now imagine the neuron (center of the web) building up a charge and sending that charge down one or more synapses (all at once). Once the charge gets' high enough, it fires across the inhibitors to the dendrite receptors on the other side. In other words, capable of shaking another spider web with a directed charge.

Now imagine 15 layers of these webs, each interconnected with the other, and each layer responsible for a "part" of coverage. The inter connectivity can provide a feedback loop to build up a charge, or to "morph" it's neural structure and learn things - or in this case, focus on what's important like edges, highlights.

Nanomorphing is changing the hardware layers to suit the needs of the situation, rather than changing the software layers. The nanotech part of this allows different chemical bonds to be "favored" and "unfavored" depending on the electrical current and stimulation, thus changing the configuration at "run-time".

This is an example of just how important it is to bind form and function closely together - the more specific and targeted the functions are, the more compact they can be, the more efficient they can be. The more bound the form is to that function, the more adaptable the form can be - thus more resilient, and quicker to respond or adapt to it's environment. Also, surprisingly - the more standard, fault-tolerant and redundant the architecture gets which by the way, leads to adapted efficiencies during run-time.

This eye piece (according to the article) is made up of transistors modeled in a neural net fashion, with nanotechnology components, layered 5 layers thick. Each layer provides feedback loops to the last, to allow a charge to build up in a specific area, and "fire" a nerve ending in the back of the eye to the brain, resulting in a perceived image.

Note to self: Where's the ACTIVE feedback loop in our Data Warehouses? Are we still in the cave-man stage here?

Sorry about that... Moving on. You think this stuff is too far out? Hasn't happened yet? too difficult to build? Think again, there's a company "in my back-yard" in Boulder, CO called Genobyte... Check them out: http://www.genobyte.com/ They are already building adaptable hardware, and quite surprisingly, have been doing this since 1997.

Anyhow, my point (that seems to take so long to get to) is this: CONVERGENCE IS EVERYTHING, when it comes to nanotechnology, and nanohousing (nano data warehousing of the future), we will be forced to combine form and function in order to build adaptable systems with virtually unlimited scalability.

If we can build a system of nanomorphing hardware, and compensating software with encapsulated dynamic feedback loops, we may have the beginning of something interesting.

Would love to hear your comments and thoughts or questions.

Cheers,
Dan L


Posted May 3, 2005 4:24 PM
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