This book describes the next step of software evolution
which could likely offer a solution to many problems. According to an ambitious
author’s claim, this technology can even help fixing society and corporation.
The goal of the book is to answer a million dollar question "how can we get
The gap between the realities of the current enterprise and
Semantic Cloud Architecture seems so huge that most companies are very cautious
in approaching this cliff. But in order for a company to benefit from new
technologies a critical mass of linked knowledge must be collected through the
transitioning of many different companies and industries to said new
technologies. While this "catch-22" may seem inescapable, this book offers a
transitioning process with practical "baby steps" and minimum investment. The
book shares the methods and tools needed to plant the seeds of Big Data and
Semantic technology in the current business ground, enabling the next
Background: The author
developed, patented, and promoted new methods to simplify IT while providing
unprecedented power of knowledge understood by people and machines.
Cambridge University Press published his first book sharing initial ideas on Integrated Software and
From Book Reviews:"This
is the new road map for a new generation of students and specialists
dedicated to the field of IT and information systems"
- Dr. V. Genin, International Academy
of Higher Education, the UN
"An impressive attempt to
re-define software and knowledge engineering for the post-dotcom
– V. Kaptelinin, Ph. D., Ume University, Sweden
"The book is a brilliant synergy of
theory and experience."
– A. Nozik, General Director, SZMA International
From the Editor:
We are pleased to present this unusual book to our readers from business, IT management, architecture, and development circles.
IT of the future: Big Data and Cognitive Computing
with Semantic Cloud Architecture promises to save more than 50% of the
current IT budget for big companies, while unleashing the power of knowledge.
The book starts with the fundamentals and provides the
conceptual ground for the audience, then continues with the practical transitioning
steps, including the architecture and code samples. The readers will be
pleasantly surprised by "The Message from 2040", a science-fiction story
built-in the book. This projection of the nearest future shows unexpected turns
of evolving economy and society with their conflicts between competitive and
collaborative forces. The book ends with "the key to the future", providing
access to helpful tools and enabling learning and doing business with new
About the author: Jeff (Yefim) Zhuk worked for Boeing, Intelligent Software Solutions, and
Sallie Mae; consulted government agencies and corporations in SOA and
knowledge engineering; shared his expertise at Java One, Semantic Technologies, Oracle and
Boeing Conferences. In the publications and multiple patents he described a
new field of Integrated
Software and Knowledge Engineering.
Through his work and in this new book he promoted the ideas and
practical steps towards semantic enterprise with greatly simplified and
If you like the magic of web
and mobile development and would like to become a magician -
Join us at ITU
Introduction: Fixing Education and Corporation
A problem: education inequity
Job market in the US is changing much faster than in the
most countries. Simpler jobs disappear while new and exciting opportunities
appear on the horizon. Unfortunately, it is much easier to lose an existing job
that to jump on a new fast moving wagon. The jumpers, who can catch up with the
changes, are our educational elite.
There are many reasons for education inequity. Here is just
When I was training a small team of DoD software architects
and developers, one of the men stood out the most to me because he was always
able to expand on any subject and had great questions. However, when he told me
his story, I was surprised to learn that his elementary, middle and high
schools considered him retarded, a "lost case". Just before college, he
developed his own way of learning and later became a successful student in
college and then a team-lead at work.
How many "lost cases" do we have in schools, colleges or in
the workplace? How many students have trouble following along with the
mainstream methods of education and vocational training? What is the percentage
of students with learning differences or detrimental gaps in their education?
I was a corporate trainer, university and college
instructor, taught in public and private schools, consulted and mentored
business and technical teams. Usually less than half of the group can sync with
the basic flow of materials and can understand a bigger picture of the
developmental process. Going above and beyond, the best mentors can recognize
those who are ready for advanced material and those that need more time or
special attention. The most effective teachers develop ways to approach
individual learning differences, fill in the gaps in their knowledge, keep the
students engaged, and minimize the "lost cases".
We can enormously increase these efforts by utilizing the
latest technology. By focusing on ending education inequity we will also solve
the related program of the growing economic inequity in our society. "We" does
not necessary mean the government or only the government. Private companies
would also see a great payoff from an investment in resolving education
inequity by enlarging the pool of skilled workers available. Furthermore,
publicity of a company’s efforts in education would also serve to build up the
company’s public image. And from a moral point of view, what could be a better
payoff than fixing society’s problems?
The best teachers in this country have already developed
individual approaches, while selecting the style and materials based on
individual learning differences. This is especially important for early
childhood education. Conversational approach in education is crucial to finding
individual differences and consistently engaging a student.
We do not have enough teachers, especially good teachers to
converse one-o-one with the students. But today, we can computerize their methods
and greatly expand the scale of the operation. The solution is a combination of
conversational approach with semantic technologies.
The system will have access to a big variety of educational
materials and will use a conversational approach to recognize the learning
differences and optimize the ways of learning for each individual. The system
must be smart enough to measure the level of engagement while trying different
styles and materials. The "smart" part will come with Conversational Semantic
Decision Support. Later in the book I write in details about this method and a
system. Here is a brief definition (read more in the book).
The Conversational Semantic Decision Support system includes
a set of pre-scripted scenarios describing decision trees, a set of
pre-scripted questions leading to a specific branch in a specific decision tree
and a semantic engine with the models of related knowledge domains. The engine
uses the knowledge domains to understand the meaning of the answers to the
questions and to map the answers to one of the existing decision trees and
branches. It is expected that the answers are not in the expected terms and
will require clarification questions, produced by the system. In the case of
multiple failures to understand and properly map human responses, the system
can access a subject matter expert for help. Each failure case will lead to
system expansion with more branches and scenarios providing a learning path.
Let us come back to our educational agenda. The set of
materials in each knowledge domain will be a mix of well-illustrated
media-based definitions and examples as well as pre-defined questions and
tasks. The most important task will be creating more questions. This individual
component of education will complement the existing group-based approach. This
individualized system will work under the umbrella of a collaborative system.
The answers and new questions will be visible (without the student names) for
evaluation to all students and will be ranked. Highest rank questions and
answers will expand teaching materials for the proper knowledge domains. The
value of questions is higher in student evaluations as they better reflect
student’s engagement and also serve as engagement tools for other students.
I described the system that I’ve built with my students
while teaching the Java Programming course at Community College of Denver. Our
system had a limited set of materials and did not have an important part,
Conversational Semantic Decision Support. Collecting best teaching materials on
multiple subjects and adding Conversational Semantic Decision Support, both are
not easy tasks, but both are doable. The teachers will still control the
process but the power of the conversational knowledge-based system will greatly
scale up their efforts.
A problem: "tribal knowledge"
Product development often starts
with a simple idea. In most cases it does not go too far because it requires
too much support from many areas of knowledge. It is like a camp fire that is
hard to start in a windy day, but very easy to stop. Someone has to be lucky
enough to be in a position to discover multiple "know how" that would provide
the necessary layers of translation into a product.
Corporate knowledge or "know how" can be split into three
categories: structured data – in relational databases, unstructured data – text
documents in folders and web sites, and the biggest portion of information that
is used daily in business routine and has never been captured. It is so-called
"Tribal Knowledge". My conservative estimate of the ratio between structured,
unstructured and "tribal" knowledge is 10%, 20% and 70%. Gartner Group
estimated that knowledge workers (almost everyone) spend 60 percent of their
time with the search, phone calls and meetings, while looking for information
to do their work. We would significantly improve business productivity across
the board if we could capture this information. By retiring "baby boomers" or
replacing "experienced and expensive" with "young and cheap" corporations actively
lose huge portions of tribal knowledge. Sooner or later the business will feel
the pain, especially the companies dealing with the long life products
surrounded by the monstrous flow of related rules and regulations.
What would happen if we can establish the process of
capturing "tacit" information and make it cheap and available? This is a long
shot, but we can start today.
The conversational approach to knowledge acquisition
combines the power of Big Data and Semantic Technologies with the human
intuition. This combination is optimal for retrieving the "tribal knowledge",
including the important "know how" necessary to move step by step from the
conceptual idea to its implementation.
We will establish a new type of the development and manufacturing
process and will make it available to a non-technical person who has initial
ideas have no knowledge about the "know how". What we call today design and
development will be transitioned into a direct conversation between a person
and a computer program, which can be called "a modeling and manufacturing
factory". Initiated by a person and supported by the conversational semantic
system with collected "know how", these conversations will help to clarify the
initial ideas, will model and manufacture the desired implementation.
This is the optimal combination of human’s ability to
suggest new approaches with computerized translation of these ideas into
properly formatted executable instructions for modeling and manufacturing
systems. The conversational system will search all available knowledge domains
and in the difficult cases come back to a SME with clarifying questions.
Eventually, they (SME and the system) will be able to successfully model and
implement the idea into a product and manufacture with 3D-printers.
A new wave of business developments may overpass the wave
initiated by the Internet.
Knowledge-Driven Architecture | US Patent | Yefim Zhuk | Driving applications with business scenarios
- Traditional control systems are first designed by subject matter experts that create business rules and scenarios. Then, the systems are developed by system developers, people who translate business rules and scenarios into technology. The invention allows business rules and scenarios to be directly included in a control system and directly drive the controlling services, providing for knowledge-driven architecture control systems. These systems can easily adjust its controlling behavior, improving flexibility to a variety of control systems including but not limited to video and audio systems, distributed networks and their combinations for medical, military and transportation applications.
Adaptive Mobile Robot System | US Patent | Yefim Zhuk | Integrating software and knowledge engineering with robotic technologies
- The invention integrates software and knowledge engineering with robotics technology to improve robot-to-robot and robot-to-human conversational interface and provide on-the-fly translations of situational requirements into adaptive behavior models and further down to service scenarios for a collaborative robot teams.
Collaborative security and decision making in service-oriented environment |
US and 15 European countries, Patent | Yefim Zhuk/Boeing | Turning a beautiful idea of collaborative decision into a system
- The invention provides collaborative security and collaborative decision making in a service-oriented environment.
Rules Collector System and Method | US Patent | Yefim Zhuk/Boeing | Formalizing expert knowledge into rules, which can be used for solving the next problem in the expert-computer brainstorming
- The system and method enables a process of capturing an expertise of an individual in a formalized manner, and which may update rules and knowledge databases with information based on the interaction with the individual.