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Semantic Cloud Architecture
Introduction of a Semantic Layer of the Enterprise Architecture open many new opportunities.
Semantic Monitoring and Semantic Listener
In a semantically rich environment, there is no need for complex monitoring tools. The service names and descriptions as well as application messages are self-explanatory and directly tied to the semantic execution model.
Application messages can describe as many properties as necessary with the idea that each property is defined in the semantic model. The messages can tell the story about WHEN (time), WHAT (description of the event), WHERE (system or/and service name), HOW Serious (type), HOW to fix (recovery action), and WHO should be notified.
A relatively simple semantic listener program can understand and act upon these messages.
This approach, when it is consistently used across the company and industry, will create smaller, smarter, and inexpensive semantic-sensitive tools to monitor and manage service operations. The same message will become a valuable record in the root cause analysis and recovery processes.
Such records can be RDF-formatted. These RDF-formatted records-messages can represent the situational awareness factors.
The next step of software evolution offers new opportunities in many areas. One thing is clear: with the volume of information doubling every year, and with increasingly interconnected departments and corporations, semantic technology, the cool new kid on the block (who also happens to be pretty darn smart) is well on its way in.
In the future, new classes called Knowledge Engineering and Semantic Cloud Architecture will be introduced in every school along with the subject of Critical Thinking. Modeling tools that have Business and Development views today will add an Ontology view tab to the front page. This is happening as you read these lines.
Semantic technology helps computers to better understand unstructured text, not just our commands. Then computer programs greatly increase their ability to partner with people on decision-making processes.
Stop dreaming of Artificial Intelligence. We are very close and need to learn new communication skills. Computers can help us more when we can help computers. This is about a conversational approach, when a program is not necessary smart enough for complete understanding, but as a child can ask a clarifying question.
This is about a new generation of systems built with knowledge-driven architecture. [4].
Sample A good example would be adaptive robotic systems that can learn by conversing with people and store new skills as orchestrations of services.
A fundamental problem of current robotics is their limited set of skills that hard to expand. This is related to the current development methods that require multiple translations from natural language of task requirements to compiled and integrated working systems. Current robots are programmed to perform relatively simple, well defined and predictable tasks.
Adaptive robot system [6] with knowledge-driven architecture includes a built-in conversational mechanism to translate on-the fly changeable situational requirements into close to natural language but more precise terms. Each successful translation introduces another rule or even a situational scenario, adds a service, and increases the system power.
The integration of software and knowledge engineering is arriving on the scene in much the same way that object-oriented programming did when it replaced structural programming.
Similar to that time, 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.
Business Architecture Sandbox for Enterprise (BASE) was designed to minimize this pain and to plant the seeds of Big Data and Semantic technology in the current business ground, enabling the next technology revolution.
BASE runs as a Web Application integrated with Mule, ESB [7] and Apache ActiveMQ [8]. This integrated system is configured as a cluster with multiple servers, providing high availability and fail-over.
BASE allows developers and subject matter experts describe and create business processes and workflows based on the REST API created on-the-fly on the top of business ontology.
These basic SOA standardization provide the ground for service orchestration, reducing tight coupling of applications, and decreasing production problems and maintenance efforts.
To play with the prototype online just use the descriptions, the link and the key in the book online http://itofthefuture.com.
Was it clear so far?
Collaboration of Services and Transformation of "tribal knowledge"
Collaboration between people and groups seems to be a thing with a positive sign, although we know how difficult this can be. Distributed knowledge and process systems [9] allows involved parties, people and companies, negotiate multiple forms of collaboration online while sharing data and services.
What is the need for collaboration for services?
Collaborative security of service groups is different from a single service security.
Simultaneous activity of many services, working on a common task, requires collaborative decision making. Think of a situation with multiple transportation services on the ground and in the air, when their interaction and collaboration is the must.
How can computer services optimize their behavior, when many of them simultaneously perform different and sometimes conflicting tasks, interfere with external events and weather, trying to adapt to a quickly changing situation?
Collaborative Security and Decision Making in SOA environment [10], answers this question and turns this beautiful idea into a working system.
One of the keys, is a multi-dimensional system of rules driving service behavior. Another key, similar to people?s collaboration, is the ability of system services to converse, understand, and adapt to the changes by adding or updating the rules. The difficult part is the mixture of business and technical slang in expressing events and situations.
Generally speaking, business prefers natural language, while technical language is XML and web services standards. Necessity of the semantic bridge is obvious. The bridge is coming especially handy when Subject Matter Experts must intervene in an unexpected situational scenario.
What is the source of rules and how to establish correct rules for a selected rules engine?
Current practice answer this question by calling consultants. This is not only expensive. The biggest problem is that consultants do not know specifics of the business, the knowledge domain that is essential for creating the rules.
Some knowledge can be retrieved via published resources, corporate regulations and policies. But the research shows that about 70% of information is so called ?tribal knowledge?, never computerized experience of subject matter experts.
The Rules Collector system [11] helps capturing the expertise of an individual in a formalized manner as a set of rules for a selected rules engine. The transformation happens over a long process initiated by a program to retrieve a complete information from a subject matter expert, sufficient enough to be formalized as a rule.
Yes, a computer conducts an interrogation of a Subject Matter Expert (SME), clarifying ambiguous expressions and connecting the dots, word by word.
At this time of massive retirement of the ?baby boomers? in various industries, capturing their ?tribal? knowledge becomes one of our most important tasks.
Capturing corporate knowledge in a computerized form is a pre-requisite for the next step in the development process, when the ?know how? will belong to the computers.
Less technical translations and translators will be needed, and many more developers will come up with creative ideas for this exciting development stage.
In the beginning was the Word?
Assignments A). Check the References:
1. Cycorp combines an unparalleled common sense ontology and knowledge base with a powerful reasoning engine and natural language interfaces, http://cyc.com
2. Financial Industry Business Ontology (FIBO) standard, http://www.edmcouncil.org/financialbusiness
3. Conversational Semantic Service Map, Yefim (Jeff) Zhuk, The system for collaborative design, assembly on-the-fly, execution, benchmarking, and negotiation of computer services and applications by developers and subject matter experts, US Patent Pending.
4. Knowledge-Driven Architecture, Yefim Zhuk, Streamlining development and driving applications with business rules & scenarios, US Patent, http://www.google.com/patents/US7774751
5. The book online, ?IT of the future?, http://ITofTheFuture.com, focuses on practical steps to transition the current IT of competing applications to a unified Semantic Cloud Architecture and describes Business Architecture Sandbox for Enterprise.
6. Adaptive Robot System with Knowledge-Driven Architecture, Yefim Zhuk, On-the-fly translations of situational requirements into adaptive robot skills, US Patent, http://www.google.com/patents/US7966093
7. MuleSoft Enterprise Service Buse (ESB), https://www.mulesoft.com/
8. Apache ActiveMQ, http://activemq.apache.org/
9. Distributed Knowledge and Process system, Yefim Zhuk, The system allows negotiate multiple forms of collaboration, and contains sufficiently flexible levels of data security for online collaboration, US Patent, http://www.google.com.sv/patents/US7032006
10. Collaborative Security and Decision Making, Yefim Zhuk, transforming a beautiful idea of collaborative security decision making into a working system, US Patent, http://serviceconnect.org/
11. Rules Collector system, Yefim Zhuk, Transforming ?tribal knowledge? into formal rules to drive applications and business processes, US Patent, http://captureknowledge.org/
