the future for smart technology architects
Post on 21-Oct-2014
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The future of software and even hardware is based in ever more complex abilities to adapt to highly dynamic change and input. The Internet of Things brings with it input from billions of sources locally and around the globe and for intelligent architects this represents an opportunity to create deep competitive advantage and customer loyalty. The Japanese have used intelligent systems for years from cars to trains to vacuum cleaners and there will continue to be smarter and smarter systems. Architects around the world must include this thinking into their designs and strategies. Adaptive social networks, individually designed health care, just in time 3d printing are only some of the components of this coming era. How to include smart system thinking into designs How to get started with smart tools like inferencing, fuzzy, neural and other technologies When to think smart and when to avoid Possible outcomes to strive for today in preparing your architecture for the age of smart systemsTRANSCRIPT
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Smart
Cloud and the
Internet of T
hings
A Day in the Life
IASA is
a non-profit professional association
run by architects
for all IT architects
centrally governed and locally run
technology and vendor agnostic
The use, disclosure, reproduction, modification, transfer, or transmittal of this work without the written permission of IASA is strictly prohibited. © IASA 2009
Topics
What is Cloud and IoT?
What is the relationship between Cloud and IoT?
Where is the ‘Smart’ in Smart Cloud and Smart IoT?
What is valuable about Cloud and IoT?
How to include smart system thinking into designs
How to get started with smart tools like inferencing, fuzzy, neural and other technologies
When to think smart and when to avoid
Possible outcomes to strive for today in preparing your architecture for the age of smart systems.
Cloud and IoT
’
Cloud
The umbrella term for anything available over a network
Relevant attributes which typify and classify architectures include Public or private Virtualized or non-virtualized Service oriented or person oriented Hardware oriented or platform oriented or software oriented Organizationally oriented or personally oriented Secure or unsecure Paid or free Paid by quality attribute or paid by operational attribute Guaranteed or unguaranteed
Internet of Things
Identifying all physical and virtual objects on a network
Relevant attributes which will typify and classify architectures may include Type of IoT identity (hardware, network, software, service,
invoker, agent, intelligent agent, independent intelligent agent, provocateur)
Size or scope of object (molecular -> planetary) Data type/volume consumption/production Power consumption/production Location and Mobility Object interaction power in virtual, physical or both Intention and Autonomy
Proposed Hierarchy of IoT Identities Provocateur - Intelligent agent with intention (human level)
Independent Intelligent Agent - Intelligent agent acting without permission
Intelligent Agent – Agent with a degree of reasoning capacity
Agent – Invoker which changes addresses in some way
Invoker – Service which calls other services
Service – Software object which returns a complex response
Software – Network object which returns a simple response
Network – An object which is addressable over a network
Hardware – An object which is identifiable over a network
Concepts and Relationships
Cloud is the raw network access mechanism
IoT is the type of things accessible
Understanding these relationships requires a much more sophisticated ontology and series of reference points
Google AcquiresDeep Mind
Why Is Smart Required for IoT and Cloud?
How is Smart Implemented Now
Advanced Search – Genetic, Graph Theory
Inferencing (Deductive, Inductive)
Fuzzy Reasoning
Optimization
Learning
Interpreting and Language
Negotiation
Searching for Information
Our lives and companies are run with information
Information has to be constructed from data and context
There is more data and information in the world than we can process
Intelligent search is key to our ability to make use of information
Common applications: business intelligence, lifestyle optimization, interest optimization
The Rules We Live By
Most companies have large numbers of commonly modified rules
Inferencing allows us to deduce new information within context (forward-chaining) induce information from existing data (backward-chaining)
Common Applications: Insurance rates and converage, retail pricing and discounts, purchase decisions, lifestyle choices “If the train is late let me sleep in”
Fuzzy Reasoning and Controllers
Humans and business work on ‘fuzzy definitions’ which is simply that most things are both true and not true “It is cold in Sweden” may be true to a Texan but not an
Eskimo! “A cup is also a bowl” can be more or less true “That hotel is extremely expensive” for me but Bill Gates?
Allows our devices to be more precise and selective in decision making and reasoning “Pre-heat the car when it is very cold” “We buy very high quality business supplies”
Common Applications: Energy utilization, mechanical controllers, human definitional input
Optimization
Business processes, graph navigation, optimal path traversal, and business integration all involve process optimizations
Multi-processes integration beyond the simplicity of a single service (physical or virtual) control much of our lives
Utilization of embedded process engines and optimization allows for maximum flexibility of physical and virtual agents
Common Applications: multi-partner business transactions, automated delivery systems, personal travel itineraries, multi-device automation
Learning
More and more data and choice is available to system software
As automation and autonomy become ubiquitous training in desired outcomes is necessary for personal and business
The vast amount of data and information requires grouping, characterizing and classifying
Neural networks and decision trees
Common applications: Food, travel and personal preferences, natural language processing, optimal energy input/output, security threat detection
Thing to Thing Communication
Language, dialect, grammar, vocabulary and pronunciation are all relevant in IoT communications and configuration
Knowledge and language ontology and dictionary will be essential to self-configuration (and therefore adoption)
This may be the single most difficult task in the IoT Even humans struggle with this constantly ‘Molecular’ data element combinations are not solidified (what is
an address, a name, a birthday)
Common applications: Thing configuration and communication, business analytics, service orchestration, personal identity management (pay for use)
Negotiation
As systems begin to represent us there is more and more conflict “What is the best price we can get for pencils for
employees”
Using negotiation techniques to avoid conflict with game theory
Common applications: Device resource allocation and utilization, purchasing
Considering Value and Risk
Value to Who? Individuals Governments and NGOs Vendors and Service
Integrators For Profit – non-vendor
What type of Value Lifestyle|Social Value Financial Value Customer|Operational
Value Societal|Human Value
Risk to Who? Individual Corporation Governments
What type of Risk? Physical Financial Societal
How Smart Becomes Value
There is a world of ‘new’ objects to sell to the world
There is an unlimited number of ways to incorporate new inventions into multiple channels, services and ‘products’ Learning about your customers and partners Dynamically allocating resources and processes Optimized pathing Planning and forecasting Configuration management and ease of use Human interaction and reasoning
Architecture Value
• Profitability• Constituent Value• Reuse• Grow Market Size• Grow Market Quality
What is “creates value”?What is Good?
suitable or efficient for a purposebeneficial or advantageous
Value Questions
Financial Value How do our customers buy from us? When does a person ‘have’ to be involved? How do our partners supply us? When do our customers have to think? When do our employees have to use a best guess or
experience? Are there times we ‘diagnose’ a problem? How can our systems interact on long-lasting complex
transactions?
What does Smart Mean Tomorrow
We must begin to consider systems as more than software services Autonomy – the degree to which systems can act without
permission Power (to influence) – the amount of influence or size of
outcomes a system can achieve Resources (to command and use) – the size and makeup of
objects a system may use Motivation – as systems gain more power and autonomy we
will need to understand Combat – when systems with autonomy, power and
resources disagree about outcomes
Resources
Books Designing the Internet of Things Practical Artificial Intelligence Programming with Java Rethinking the Internet of Things IoT – Global Technological and Societal Trends
Tools/Frameworks Drools Weka JFuzzyLogic Fuzzylite Gambit
Skill Taxonomy
The use, disclosure, reproduction, modification, transfer, or transmittal of this work without the written permission of IASA is strictly prohibited. © IASA 2009
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