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Nanohousing and Relationship Folding: A Theoretical Step in the Journey of Nanohousing

Dan E. Linstedt

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DNA molecules are capable of folding on themselves. In fact, it happens every time DNA strands replicate. After replication they begin to "fold" and "bend" at specific points in the chain, making the molecules much "smaller" in 3D space than before. Nanohousing is the use of Nanotechnology and DNA computing, as it applies to data integration and business intelligence. As we follow the nature of Nanohousing, we attempt to stick with the "wet" or "real-world" models where the concepts were founded. There is more about the physical modeling of the nanohouse on our Data Vault modeling architecture.

We can begin using 3D modeling tools and concepts to break new ground. After using this model, it quickly becomes apparent that a) the business can now "see" how their data affects their business; b) the business can compare (visually) which relationships it "thinks are important" versus which the data set "shows" is strong. This turns data modeling intobusiness modeling.

What are we looking at today?

Again, today's data models are frequently represented in only two dimensions. As shown below, flat planes of information are linked together and limited by our ability to visualize a different view.

Figure 1: 2D Model representation: SQLServer Northwind DB

In this representation we are far from the wet or natural world models. Briefly imagine that the DNA molecule represents different data sets, and the instructions housed within the DNA (that are encoded or decoded) are the algorithms that tell us what context this data resides. Imagine that the relationships in the DNA strand are relationships through our context within our data model. Now we realize that our information modeling concepts arefar far away (Shrek 2) from reality.

Where do we need to go?

As I stated earlier, the 2D modeling we currently use is a good start. But for true information understanding and a better opportunity to understand the cognitive thought process, I suggest we use 3D modeling tools and concepts. An example (albeit a poor man’s drawing of what a 3D model might look like) is given below:

Figure 2: 3D Example Model of Northwind Data Vault

This 3D model is a result of nature – basic chemistry. However, it is applied as a data model. When using components of the Data Vault architecture we can clearly see the "spheres" that represent business keys and their descriptors. Similarly, the "links" represent links across business keys (relationships, interactions, etc). The width of the linkage can represent the strength within the context. Color can represent the business's overall interest or importance of the data to the business.

Okay, but how does this apply to business?

When using this model, it quickly becomes apparent that the business can now "see" how their data affects their business. This model also shows that the business can (visually) compare which relationships it "thinks are important" versus which the data set "shows are strong." This turns data modeling into business modeling. It turns business modeling into providing additional value to the business, and it furthers the efforts to understand relationships within information across the business.

Wait a minute! Why can't I have this today?

Because the data modeling engines are currently STUCK in the 2D world, 3D models take time to implement. But if you see the value-added proposition to your business, push your data modeling vendors to start thinking this way. After such efforts, 3D modeling might start occurring.

It is absolutely required for the Nanohouse to succeed, mostly because the Nanohouse is based on DNA sequencing and atomic layer understanding. The Nanohouse must be modeled this way. Once it's connected to a DNA sequencing machine (nanotech programming), the Nanohouse can begin disseminating data sets accordingly.

I thought this was about relationship folding, how does that work?

This works quite easily. To get there, however, you must begin thinking in 3D modeling terms. Once we understand that concept, we can progress to relationship folding. I wrote an entry on the Data Vault Modeling site about this. But here's the gist: Once we have the model in three dimensions, we can begin "spinning" or rotating it. We can begin assigning colors to significance, size and weight. We can apply "context" to the model as it relates to our business as well.

Imagine thinking in a whole new paradigm. What if you could add value to your business through relationship discovery based on the knowledge you’ve already collected? That idea relates back to neural science and learning capacity. However, we're all smart individuals; we can apply our own thoughts and understanding to begin seeing "new connections" through the data sets. In other words, we can traverse the relationships that span the model. Simply ask yourself if there is any benefit to "short-cutting," or short-circuiting, the entire loop of connections?

Are there any patterns that you can spot that would allow the data to be inter-connected? Can the machine "build rules around" to generalize on a higher level grain? If so, you've just completed a "system generated" component to fold the relationships between two "spread-out" elements in business. Once this relationship is established, it is entirely possible that a) your business can use it as differentiation; b) it will multiply the value of your data set by 2x, 3x, or 4x; c) it may shorten your businesses processes (cycle time reduction) and create a new way of thinking.

 

Conclusion

We've mixed many notions and concepts in this article; however this is a journey, not a destination. Nanohousing is nothing more than a "thought experiment." It is about the hypothetical use of nanotech as it applies to data, business and integration. Although there are many easily applicable techniques mentioned here, Nanohousing has yet to mature.

As you build your Master Data Management system, or your single version of the facts, briefly consider the nature and impact of 3D modeling, as well as relationship folding. This should also be of interest if you are integrating your enterprise, metadata or information. I think you might be pleasantly surprised at the returned value.

Whether you are a researcher or just curious about something, feel free to contact me anytime. I'd love to hear your thoughts, comments and feedback on this article—whether they are critical disagreements or thoughtful perspectives. This is a research area of mine.

  • Dan Linstedt 

    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/.

 


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