RC. Working as a geodetic engineer from 1973 to 1978, participated in converting an inertial navigation platform (off a cruise missile) to an inertial survey platform, a huge real-time data crunching machine.
From 1978 to 1983, I tackled an internet service provider (ISP) and global positioning system (GPS) for the first private firm to use GPS, and retrieved massive amounts of data to determine an object’s exact position.
From 1983 to 1989, I served as CEO of a geographic information system (GIS) and computer-aided design (CAD) software company, where there were many complex indexing systems involved, but with a group of 70 programmers, I solved major data management issues for utilities and government clients.
From 1990 to 1997, I worked on satellite imagery processing and sonar pattern recognition of massive unstructured data containing intelligence. I was able to find that intelligence and develop a Semantic Intelligence (SI) Engine.
I then gathered several data scientists to develop our Knowledge Generation Engine to be applied to text—the most underrated unstructured piece of data in all our databases. I partnered with Mark Hurst, chief technology officer (CTO) of Cirilab, who has a background in science and artificial intelligence. Mark is currently focused on text analytics; associative, contextual, and analogical modeling; collaborative analysis; and organization and discovery.
Enterprise wise (risk management) for governance and the belief that analyzing will help, though there is very little return on investment (ROI) and the analysis is associated with huge expenses.
For a consumer, therein lies the “knowledge nugget.” The question is, “How do I find it?” After searching, the consumer expects and deserves to find some information that is of relevance.
We have a series of products (semantic tools) and services that provide a means for a “self-describing source,” a Knowledge signature (Ksig) of a document, or a Knowledge map (Kmap) of several documents. These tools allow you to get to a level where you can garner “meta-knowledge,” and be able to carry out knowledge management. You can then process that meta-knowledge to achieve “meta-cognition” and build an intelligence infrastructure. (Don’t worry, I come down to earth in the next few questions.)
For a consumer, the focus is to save time, assist him or her in managing a knowledge base, and filling in the knowledge gaps. For an enterprise, it comes down to benefiting from those individuals using the system. There is a focus on the value of a “corporate memory” garnered from analyzing a knowledge base. The goal is to make use of collaborative efforts (Kmap of blog posts, chats, e-mail, documents, etc.) to facilitate access to knowledge within minutes, and then to save and share that knowledge.
The main difference is the focus on SI tools—for an individual user and his ROI, a subject very dear to most knowledge workers (other than information management [IM] and information technology [IT]) —and on the unstructured data, the place where each document is allowed to express itself. The document is the algorithm!
They need to organize and analyze large amounts of data in a short time. That may be 10 documents (100 pages each) for tomorrow morning, 108,000 chats for next week, or a profile of 1,500,000 documents for a round of litigation. With the soft-as-a-service (SAAS) model, the customers first register, but then only pay when they use the system.
In the early 1990s, we bid, won, and delivered a project on large engineering and unstructured data management. I was leading a swat team of data scientists to deliver that project at 20% of budget and 25% of the allotted time. The solution and savings came from a multidimensional index (GIS type) and from processing only 10% of the data and modeling all knowledge nuggets based on an aerial photo. The results were more than adequate and at great savings.
The present trends in business intelligence (BI) and SI are similar and very promising with so much data being produced today.
It should be one user, one folder at a time, with the prime design parameters being “relevance” and “timeliness.” The key to a semantic index is to identify what may be useful in a future discovery. You will satisfy 80% of the current needs at 20% of the costs—and still have the ability to drill down to satisfy more details should you choose to process more.
Saving time in reaching a decision is both valuable to the individual and the enterprise. A simple speed-read function summarizes 40 pages in seconds and gives ‘corporate memory’—a knowledge tag of things that are happening.
Star Trek—I think DATA had our software in his head.
We had a barn full of both on a farm I used to have. I do love my grandkids!!!