UBIWARE Summary

1
UBIWARE
Smart Semantic Middleware for the Internet of
Things
Expert
Device
Service

Vagan Terziyan
ICINCO-2008 13 May 2008, Funchal, Madeira,
Portugal
2
Authors

• Artem Katasonov
• Olena Kaykova
• Oleksiy Khriyenko
• Sergiy Nikitin
• Vagan Terziyan

URL http//www.cs.jyu.fi/ai/OntoGroup
University of Jyväskylä
3
UBIWARE Team Industrial Ontologies Group
2008-2009

• Researchers
• Vagan Terziyan (Head)
• Olena Kaykova
• Artem Katasonov
• Oleksiy Khriyenko
• Sergiy Nikitin

• Contact Person
• Timo Tiihonen
• e-mails tiihonen_at_it.jyu.fi
• vagan_at_cc.jyu.fi
• phone [phone]

• Michal Szydlowski
• Joonas Kesäniemi
• Michal Nagy
• Arnim Bleier
• Nikos Mouchtaris

URL http//www.cs.jyu.fi/ai/OntoGroup
4
Three alternative trends of Web development
Applications, services, agents
Machines, devices, computers
Human Communities
Facilitates Machine-to-Machine interaction
Facilitates Software-to-Software interaction
Facilitates Human-to-Human interaction
Semantic Web
Web of Things
Metadata
Ubiquitous Computing
Web 2.0
Smart Spaces
Ontologies
Wikis
RFID
Web Services
Blogs
Embedded Systems
Agents
Mashups
Sensor Networks
EAI Portals
Community Portals
Web
5
New integral trend of Web development
Web of intelligent entities (intelligence
services), browseable, searchable, composable,
configurable, reusable, dynamic, mobile
Involvement of various mathematical models to
be service components results to more general Web
of Abstraction
Facilitates Intelligence-to-Intelligence (also
model-to-model) interaction
Distributed AI
MAS
Data and Web Mining
Machine Learning
Knowledge Discovery
"Smart Services"
Web
6
Semantic Wave (Web X.0)
We may add here Web 5.0 will come finally and
it is about connecting models in a Global
Understanding Environment (GUN), which will be
such proactive, self-managed evolutionary
Semantic Web of Things, People and Abstractions
where all kinds of entities can understand,
interact, serve, develop and learn from each
other. Vagan Terziyan
The semantic wave embraces four stages of
internet growth Web 1.0, was about connecting
information ... Web 2.0 is about connecting
people. Web 3.0, is starting now and it is
about connecting knowledge Web 4.0 will come
later and it is about connecting intelligences
in a ubiquitous web where both people and things
can reason and communicate together. Semantic
Wave 2008 , Mills Davis
7
Beyond Web 5.0 ?
Human v2.0 ?!
Ray Kurzweil
2029
Singularity
Semantic Wave
Nanotech
http//www.youtube.com/watch?vBywCMkbG-Jg
8
Overall Goal

• Based on combination of Semantic and Agent
  technologies, we aim at designing a new
  generation middleware platform (UBIWARE) which
  will support and essentially simplify design,
  implementation and operation of self-managed,
  complex, flexible and extendable industrial
  systems (e.g. information-, business
  intelligence-, expert-, condition monitoring-,
  diagnostics-, maintenance-, etc. systems)
  consisting of mobile, distributed, heterogeneous,
  self-descriptive, self-aware, shared, reusable
  and configurable components of different nature.

9
What is UBIWARE ? (1)

• UBIWARE is a generic, domain independent
  middleware platform, which is meant to be able to
  provide the following support
• integration
• interoperability
• proactivity
• communication, observation, negotiation,
  coordination and collaboration
• automation, design and installation
• lifecycle management, execution monitoring,
  diagnostics, maintenance
• self-descriptiveness, semantic search, discovery,
  sharing, reuse
• planning and decision-making
• adaptation
• learning, mining, knowledge discovery
• context-awareness
• self-management including self-configuration
• security, privacy and trust
• etc...
• for (see next slide)

10
What is UBIWARE ? (2)

• for the following resources, systems and
  components (including their groups)
• data information and knowledge data, metadata,
  knowledge, logic, ontologies
• software and services software components,
  software agents, software and information
  systems, services including Web-services
• humans users, operators, experts,
  administration, customers, patients, doctors,
  etc
• hardware machines, devices, networks, embedded
  electronics, RFID
• organizations
• intangibles human and organizational capital,
  innovations, property rights, trust and
  reputation, brand recognition, etc.
• processes behaviors, technologies and business
  models
• interfaces
• intelligence reasoning, inference, planning,
  learning, data-mining, knowledge discovery, etc
  engines
• ecosystems environments, smart spaces, other
  middleware and CSCW tools
• abstractions and mathematical models
• etc.

11
What is UBIWARE ? (3)

• Due to heterogeneity of provided services and
  supported components, UBIWARE is based on
  integration of several technologies Semantic
  Web, Distributed Artificial Intelligence and
  Agent Technologies, Ubiquitous Computing, SOA
  (Service-Oriented Architecture), Web X.0, P2P and
  related concepts.
• The research and design on UBIWARE is started by
  Industrial Ontologies Group within UBIWARE
  project Smart Semantic Middleware for
  Ubiquitous Computing (June 2007 May 2010)
  funded by Tekes and industrial companies.
• Project web page http//www.cs.jyu.fi/ai/OntoGrou
  p/UBIWARE_details.htm

12
What is UBIWARE (in short)

• UBIWARE is a tool to support
• design and installation of,
• autonomic operation of and
• interoperability among
• complex, heterogeneous, open, dynamic and
  self-configurable distributed industrial
  systems
• and to provide following services for system
  components
• adaptation
• automation
• centralized or P2P organization
• coordination, collaboration, interoperability and
  negotiation
• self-awareness, communication and observation
• data and process integration
• (semantic) discovery, sharing and reuse.

13
Why Semantic Web? (Ora Lassila)
Semantic Web is important for UBIWARE just
because UBIWARE is meant to handle also new
problems, which may appear later
14
Why Agents?

• Growing complexity of computer systems and
  networks used in industry ? need for new
  approaches to manage and control them
• IBM vision Autonomic computing Self-Management
  (includes self-configuration, self-optimization,
  self-protection, self-healing)
• Ubiquitous computing, Internet of Things ? huge
  numbers of heterogeneous devices are
  interconnected
• nightmare of pervasive computing when almost
  impossible to centrally manage the complexity of
  interactions, neither even to anticipate and
  design it.
• We believe that self-manageability of a complex
  system requires its components to be autonomous
  themselves, i.e. be realised as agents.
• Agent-based approach to SE is also considered to
  be facilitating the design of complex systems

15
GUN Concept
GUN Global Understanding eNvironment
GUN Global Environment Global Understanding
Proactive Self-Managed Semantic Web of
Things (we believe) Killer Application for
Semantic Web Technology
16
GUN and Ubiquitous Society
GUN can be considered as a kind of Ubiquitous
Eco-System for Ubiquitous Society the world in
which people and other intelligent entities
(ubiquitous devices, agents, etc) live together
and have equal opportunities (specified by
policies) in mutual understanding, mutual service
provisioning and mutual usability.
17
Challenge 1 General Adaptation Framework
RDF-Based Semantic Agent Programming Language
Universal reusable semantically-configurable
adapters
18
Challenge 2 General Proactivity Framework
Role Feeder description
Role SCADA description
Role Maintenance worker description
Universal reusable semantically-configurable
behaviors
19
Challenge 3 General Networking Framework
Scenario Predictive maintenance description
Scenario Data integration description
Universal reusable semantically-configurable
scenarios for business processes
20
UBIWARE Subgoals

• Core DAI platform design (UbiCore)
• Policy-Based Control of MAS (PBC)
• Managing Distributed Resource Histories
  (UbiBlog)
• Self-Management, Configurability and Integration
  (COIN)
• Smart Interfaces Context-aware GUI for
  Integrated Data (4i technology)
• Industrial cases and appropriate prototypes.

21
1 Core DAI Platform Design (UbiCore)

• The core platform should provide means for
  building systems that are flexible and consist of
  heterogeneous autonomous components, yet
  predictable in operation.
• A major challenge a semantic/ontological
  approach to coordination - to enable the
  components to communicate their intentions with
  respect to future activities and resource
  utilization and to reason about the actions,
  plans, and knowledge of each other, in real time.

22
UBIWARE Platform Architecture
Behavior Engine
.class
Script Configuration Settings
Assign Settings Activity
Beliefs Log Storage
Script Policy Constraints
Live Activity
Script Role-Based Behavior Rules
.class
Activity
Activity
Activity
Activity
Activity
Activity
Activity
Activity
Everything is Belief !!!
23
Layered Agent Architecture
24
Soul-Mind-Body-Genome-Ontonut Agent Architecture
Added 2 September 2008 by Terziyan Vagan
SoftSoul
Life Behavior
HardSoul
Meta-Beliefs (preferences)
Shared Meta-Beliefs
SoftMind
RBE Reusable Behavior Engine
Shared RBEs
RBE
RBE
RBE
RBE
HardMind
Beliefs (facts, rules, policies, plans)
Configuration (GENOME)
Ontobilities
Shared Beliefs
SoftBody
RAB Reusable Atomic Behavior
Shared RABs
RAB
RAB
RAB
RAB
HardBody
Hardware
Shared Hardware
Visible to other agents
Environment
May be an agent
25
What is Environment ?
Added 2 September 2008 by Terziyan Vagan
Environment

• Environment of an agent is the remaining part of
  the UBIWARE-supported world (physical or virtual)
  if to exclude the agent itself (with its soul,
  mind, body, etc).
• Main groups of entities in the environment of the
  agent
• Entities to which the agent provides services
• Entities from which the agent consumes services
• Conflicting entities, or other agents, which
  share entities of described above categories the
  same service providers or service consumers
• Any combination of the above.

26
Environment Service Consumers
Added 2 September 2008 by Terziyan Vagan
SC
Environment Service Consumers

• Service consumers (SC) within agent environment
  are the entities to which the agent provides
  services.
• Main groups of service consumers of the agent
• Entities (devices, humans, software, other
  agents, etc.) under the agent monitoring
  (supervision, control, diagnostics, maintenance,
  etc.)
• Entities (devices, humans, software, other
  agents, etc.) to which the agent provides needed
  information (pull or push)
• Entities (devices, humans, software, other
  agents, etc.) to which the agent provides
  assistance in their operation (pull or push)
• Entities (devices, humans, software, other
  agents, etc.) to which the agent committed to
  serve like an instrument (slave) in their
  operation and can be anytime configured and fully
  controlled by them
• Any combination of the above.

Monitoring Information Assistance Instrument
27
Environment Service Providers
Added 2 September 2008 by Terziyan Vagan
SP
Environment Service Providers

• Service providers (SP) within agent environment
  are the entities, which provide services to the
  agent.
• Main groups of service providers of the agent
• Entities (humans or other agents) which monitor
  the behavior of the agent (supervision,
  policy-based control, reconfiguration, etc.)
• Entities (devices, humans, software, databases,
  other agents, etc.), which provide needed
  information (pull or push) to the agent
• Entities (devices, humans, software, other
  agents, etc.) which provide assistance to the
  agent in its operation (pull or push)
• Entities (devices, software, other agents, etc.)
  which the agent can use as instruments in service
  provisioning (pull) and which are fully committed
  for that (aka slaves) and are under full
  control by the agent while used
• Any combination of the above.

Monitoring Information Assistance Instrument
28
On Ontonuts in UBIWAREOntonuts as agent-driven
proactive service capabilities(Ontobilities)

• Added by Vagan Terziyan 17 September, 2008

29
UBIWARE Agent Possible Future Architecture
RBE Reusable Behavior Engine
SoftSoul
Life Behavior
HardSoul
RAB Reusable Atomic Behavior
Meta-Beliefs (preferences)
Shared Meta-Beliefs
SoftMind
Ontobility is self-contained, self-described,
semantically marked-up proactive agent capability
(agent-driven ontonut), which can be seen,
discovered, exchanged, composed and executed
(internally or remotely) across the agent
platform in a task-driven way and which can
perform social utility-based behavior
Shared RBEs
RBE
RBE
RBE
RBE
HardMind
Beliefs (facts, rules, policies, plans)
Configuration (GENOME)
Ontobilities
Shared Beliefs
SoftBody
Shared RABs
RAB
RAB
RAB
RAB
HardBody
Hardware
Shared Hardware
Genome is part of semantically marked-up agent
configuration settings, which can serve as a tool
for agent evolution inheritance crossover and
mutation
Visible to other agents through observation
Environment
May be an agent
30
Ontonuts Competence Profile of an Agent as a
service provider (what can I do and what can I
answer) and appropriate service plan (how I do
or answer )
You can ask me for

1. action
2. information

ontonut
31
External view to ontonuts Shared Competence
Specification
You can ask me for
External
Internal

1. I can open the door 456
2. I can fly
3. I can use knifes
4. I can build house from wood
5. I can visualize maps
6. I can grant access to folder 444

1. I know everything about Mary
2. I know everything about cats
3. I know what time it is now
4. I know all lovers of John
5. I know grades on chemistry of all pupils from 4-B

We consider ONTONUTS to be shared S-APL
specifications of these competences
32
Internal view to ontonuts Action or Query Plans
You can ask me for
External
Internal

• I can open the door 456
• S-APL plan of opening the door 456

• I know everything about Mary
• S-APL plan of querying either own beliefs or
  external database about Mary

We consider ONTONUTS to be also an internal plans
to execute competences
33
Possible general rule of ontonut appearance
You can ask me for
External
Internal
IF I have the plan how to perform certain
complex or simple action or the plan how to
answer complex or simple query AND
time-to-time execution of the plan is part of my
duty according to my role (commitment) OR I am
often asked by others to execute action or query
according to this plan THEN I will create
ONTONUT which will make my competence on this
plan explicit and visible to others
34
Example (1) Atomic Ontonut 1
I can answer any queries on mental diseases of
citizens of X
007
003
Give me the list of women from X with mental
diseases diagnosed after 2006
1
I know how appropriate database is organized, I
have access rights and I am able to query it
SQL
City X Central Hospital Relational Database
35
Example (2) Atomic Ontonut 2
I can answer any queries on loans in Nordea bank
007
005
Give me the list of Nordea clients with loans of
more than 100 000 EURO
2
I know how appropriate database is organized, I
have access rights and I am able to query it
XQuery
Nordea XML Database
36
Example (3) Complex Ontonut 3
I know how to split query to two components I
know to whom I can send component queries (I have
contracts with them) and I know how to integrate
outcomes of these queries
I can answer any queries on mental diseases and
loans of Nordea bank clients from X
001
007
Give me the list of Nordea clients from X with
loans of more than 200 000 EURO and who has more
than 2 mental disorders during last 5 years
3
37
Summary Ontonut is something similar to OWL-S
Semantic Web Service description (i.e.
combination of Profile, Model, Grounding)
OWL-S

• A Service is a kind-of Resource in the Web, i.e.
  some Web resources provide services.
• What does the service require of the user, or
  other agents, and provides for them? The
  answer to this question is in ServiceProfile
• How does it work?
• The answer to this question is in ServiceModel
• How is it used?
• The answer to this question is in
  ServiceGrounding.

38
A Service as well as an Ontonut provides some
Function
G
X
Service Model
Service Grounding
F
Service Profile
Y
39
An Ontonut as well as a Service provides some
Function
Service Profile
External
Service Grounding
Internal
Service Model
40
Service Profile, Model and Grounding example
Cinema Cashier
x1 movie_name x2 time x3 number_of_tickets x
4 seats preference x5 money
G
X
1 takes x1, x2, x3, x4 2 checks availability
of x3 tickets for the x1 movie, at x2 time, which
suits x4 constraint 3 finds one_ticket_prise
from the price list 4 calculates price for x3
tickets price
one_seet_price x3 5 takes x5 6 calculates
y2 ( y2 x5 price ) 7 gives y1, y2.
F
1 cinema address 2 cinema movie schedule 3
cinema cash-desk location 4 nock to the
cash-desk window and, when it opens, make your
order (X)
Y
y1 movie tickets y2 change
41
On Ontonuts in UBIWAREOntonuts as distributed
querying capability
Added by Sergiy Nikitin 17 September, 2008
42
Original view on Ontonuts (distributed querying)
Data Service
Files
Ontonut Bindings
Ontonuts Role Script
DB/KB
agent-to-agent servicing
adaptation of external sources
43
Main components of Ontonuts architecture
Business Logic Script
Ontonuts bindings
Agent Beliefs (S-APL code)
Ontonuts triggering rule
Ontonuts Role Script
Query Planner
Query Plan Executor
SQLReader TextTableReader ExcelReader MessageSen
der MessageReceiver
Reusable Atomic Behaviors (Java code)
QueryAnalyser QueryPlanner

Data Service
44
Ontonuts example code

• DiaryCommentNut rdftype diDBOntoNut.
• DiaryCommentNut hasDataSource datasourceid.
• DiaryCommentNut hasSQLQuery
• "SELECT CommentID, EntryID, Title FROM
  dbo.Comment ".
• DiaryCommentNut hasMapping
• commentID dimapsTo "CommentID".
• entryId dimapsTo "EntryID".
• commentTitle dimapsTo "Title".
• DiaryCommentNut hasTransformationScript
• table ?rowid row
• CommentID column ?commentid.
• EntryID column ?entryid.
• Title column ?title
• gt
• DiaryComment instance
• ?rowid rdftype DiaryComment.
• ?rowid commentID ?commentid.
• ?rowid entryId ?entryid.

45
Querying usecases

• Event flow integration
• Time-based distributed query to different sources
  (extract events from different systems by
  filtering them with the same time frame)
• Additional information
• Get supplementary device configuration data for a
  particular event-based view
• Complex mining
• Collect information from a set of sources, where
  inputs of subqueries are dependent on outputs of
  the preceding subqueries
• Combination of all cases mentioned above

?diaryEvent hasCommentText ?ctext. ?diaryEvent
hasTime ?ctime. ?ctime gt ?timestart. ?ctime lt
?timeend. ?alarmHistorian hasAlarm ?alarm.
?alarm hasTime ?atime. ?atime gt ?timestart.
?atime lt ?timeend. ?timestart
2008.09.08T1200.00. ?timeend
2008.09.08T2359.00.
?diaryEvent hasCommentText ?ctext. ?diaryEvent
hasTime ?ctime. ?ctime gt 2008.09.08T1200.00.
?ctime lt 2008.09.08T2359.00. ?diaryEvent
hasTag ?eventtag. ?eventtag hasMappingTo
?node ?dpm hasNode ?node. ?node hasAlarmLimit
?alimit
?dpm hasNode ?node. ?node hasPerfIndex
?pindex. ?pindex lt 0.5 . ?node hasMappingTo
?eventTag. ?diaryEvent hasTag ?eventTag.
?diaryEvent hasComment ?comment
46
Query types

• Parallel
• subqueries can be executed independently from
  each other and results are merged
• IOMIO (sequential)
• Inputs are dependent on the outputs from
  subqueries to other sources
• Hybrid (a combination of two above)
• Possible cases
• Results of two or more parallel subqueries are
  merged and used as an input for a subsequent
  subquery
• Result of one subquery is used as an input to a
  set of parallel subqueries
• Result of a subquery is used as an input for both
  subsequent and non-susequent, parallel and
  non-parallel subqueries

47
Ontonuts a mechanism for provision of dynamic
information

• An analog of platform-embedded constructs like
• saplNow saplis ?time (gets current system time)
• But can be flexibly (re-)defined by user
• fingridCurrentVoltage saplis ?voltage
• metsoCurrentOilLevel saplis ?oillevel
• innowCurrentUsersOnline saplis ?usersonline
• The approach simplifies the implementation of the
  agents business logic by introducing computable
  elements. The values of these elements are
  computed on-demand (only when a query appears in
  agents beliefs)

48
Ontonuts a mechanism for provision of dynamic
information(2)

• When extended to more abstract level, computable
  values can be applied for
• counting statistics over dynamically updated data
  (e.g. average alarm rate per day, or number of
  students at the lecture now)
• collecting dynamic information about others (e.g.
  request what is Johns location at the moment
  would look like
• John currentLocation ?location)

49
Ontonuts vs. Softbody (Environment)

• An agent can seamlessly read the information from
  the environment in a similar manner as from the
  Ontonut, the observable properties in the
  environment can provide different kind of
  information, that directly goes to agents
  beliefs, however, environment represents common
  and most frequently used properties, whereas
  agent may need to perform its own specific
  calculations. The complexity of the calculations
  will be hidden behind Ontonut.

End of Sergiy Nikitin chapter
50
Soul-Mind-Body-Genome-Ontonut Use of S-APL
provides new opportunities
Added 2 September 2008 by Terziyan Vagan

• Mobility of (Souls, Minds, Bodies, Genomes,
  Ontonuts)
• Integration of (Souls, Minds, Bodies, Genomes,
  Ontonuts)
• Semantic Search and Querying of (Souls, Minds,
  Bodies, Genomes, Ontonuts)
• Reasoning based on (Souls, Minds, Bodies,
  Genomes, Ontonuts)
• Configurability of (Souls, Minds, Bodies,
  Genomes, Ontonuts)
• Inheritance, crossover, mutation, evolution, etc.
  of (Souls, Minds, Bodies, Genomes, Ontonuts)
• Learning of (Souls, Minds, Bodies, Genomes,
  Ontonuts)
• Compilation of Minds, Agents towards executable
  ones.

51
S-APL data model (example)
G
_C1 accordingTo John
_C1
_C2 implies _C3
_C2
_C3
Time is Summer
Sun is Shining
52
S-APL example beliefs

• Simple belief
• John Loves Mary
• Complex belief
• John Loves Mary accordingTo Bill
• Goal / desire
• I want John Loves Mary

53
New semantics of RDF Statement in S-APL (object -
executable resource)
Property_m
exe Resource_ j
Resource_i
executable resource
Semantics of such statement means that the value
of the Property_m of the Resource_i can be
obtained as a result of execution of the
procedure (query, service, function, etc.)
represented as Resource_ j
exRDF - Executable RDF
54
Service Call (something like)
Added 17 September 2008 by Terziyan Vagan
saplI saplCall saplI saplhave ?X
saplconfiguredAs ?X saplis
busTicket . ?X fromPlace Tampere .
?X toPlace Helsinki . ?X
hasPriseEuro ?Y . ?Y isLessThan 30
. saplI saplnegotiate ?Z
saplconfiguredAs ?Z saplMIN ?Y
55
2 Policies (organizational constrains put on top
of individual behavior rules)

• Instructions (e.g. drink at least 2 liters of
  water every day)
• Conditional Instructions (whenever hear alarm,
  call security)
• Commitments (e.g. promise to love forever)
• Conditional Commitments (e.g. promise to take
  care in case of illness)
• Restrictions (e.g. no smoking)
• Conditional Restrictions (do not use elevator in
  case of fire)

56
Policy-based agreement and policy commitment
Agreement Manager
Agreementj
hasPolicy
Policyi (R1, R2, R3)
(R1 Vagan, R2Oleksiy, R3Artem)
Policy commitment
isMemberOf (hasRoleR3)
isMemberOf (hasRoleR2)
isMemberOf (hasRoleR1)
Artem
Vagan
Oleksiy
57
3 Managing Distributed Resource Histories
(UniBlog)

• Managing Distributed Resource Histories is going
  to be a set of tools, which will support each
  ubiquitous resource to collect and semantically
  markup own history during the life-cycle, to
  query when needed own history or external
  distributed histories of other entities, to
  integrate the histories, to make mining
  (utilizing intelligent data mining and machine
  learning techniques) of the histories to discover
  knowledge, and finally to manage acquired
  distributed knowledge.

58
Industrial Resource Lifecycle and History
Condition Monitoring
States
Symptoms
Fault detection, alarms
Measurement
Predictive Measurement
Predictive Monitoring
Data Warehousing
Conditions Warehousing
History
Industrial Resource
Diagnostics
Diagnoses Warehousing
Predictive Diagnostics
Predictive Maintenance
Maintenance
Plan Warehousing
Fault identification, localization
Fault isolation
Maintenance Planning
Diagnoses
Maintenance Plan
59
Nature of Distributed content (1)
Resource
Domain
Resource agent observes the target resource and
other resources in its environment, collects
information about target resource throughout its
history and takes care of it
Agents of other resources may observe foreign
resource if needed, collect information about it
and communicate this information with others if
asked
60
4 Self-Management, Configurability and
Integration (COIN)

• Self-management, Configurability and Integration
  will cover the aspects of evolutionary and
  temporal changes on the platform. In the dynamic
  environment every resource may modify its own
  characteristics due to adequate reaction
  (self-awareness) on the surrounding environment
  or internal changes. Changes in a resources
  behavior may influence business process chains,
  in which the resource is involved. We target a
  well-defined resource configurability framework
  which will define clear mechanisms of
  configuration including contracting,
  re-negotiation and re-composition taking into
  account agent-driven proactivity and dynamics.

61
Resource Configuration Example
ID3
Locomotive (ID3)
hasConfiguration (ID1,ID2)
ID1
ID6
hasColor (ID3, Muticolor)
hasBehind (ID3, ID4)
ID4
Car (ID4)
hasColor (ID4, Beige)
Train (ID1)
ID7
hasBehind (ID4, ID5)
hasPart (ID1,ID3)
hasAhead (ID4, ID3)
hasPart (ID1, ID4)
hasPart (ID1, ID5)
ID2
hasDestinationTo (ID1, Paris)
ID5
hasDestinationFrom (ID1, Amsterdam)
Car (ID5)
hasConfiguration (ID3,ID6)
hasColor (ID5, Red)
ID8
hasConfiguration (ID4, ID7)
hasAhead (ID5, ID4)
hasConfiguratioin (ID5, ID8)
62
Configuration Components
Object of configuration
Object of configuration
ID1
Content of configuration
Content of configuration
hasConfiguration (ID1,ID2)
ID2
Class of the resource
Train (ID1)
hasPart (ID1,ID3)
Structure of the resource
hasPart (ID1, ID4)
hasPart (ID1, ID5)
hasDestinationTo (ID1, Paris)
Parameters values of the resource
hasDestinationFrom (ID1, Amsterdam)
hasConfiguration (ID3,ID6)
Configuration of structural components
hasConfiguration (ID4, ID7)
hasConfiguratioin (ID5, ID8)
63
Reconfiguration
ID1
ID1
hasConfiguration (ID1,ID8)
hasConfiguration (ID1,ID2)
ID8
ID2
Train (ID1)
Train (ID1)
hasPart (ID1,ID3)
hasPart (ID1,ID3)
hasPart (ID1, ID4)
hasPart (ID1, ID4)
hasPart (ID1, ID5)
hasPart (ID1, ID5)
hasDestinationTo (ID1, Paris)
hasDestinationTo (ID1, Paris)
hasDestinationFrom (ID1, Amsterdam)
hasDestinationFrom (ID1, Amsterdam)
hasConfiguration (ID3,ID9)
hasConfiguration (ID3,ID6)
hasConfiguration (ID4, ID10)
hasConfiguration (ID4, ID7)
hasConfiguratioin (ID5, ID11)
hasConfiguratioin (ID5, ID8)
64
Reconfiguration behavior (option 1 reordering)
ID1
ID1
ID3
hasConfiguration (ID1,ID8)
Locomotive (ID3)
ID8
ID9
hasColor (ID3, Muticolor)
hasBehind (ID3, ID5)
Train (ID1)
hasPart (ID1,ID3)
ID4
Car (ID4)
hasPart (ID1, ID4)
ID10
hasColor (ID4, Beige)
hasPart (ID1, ID5)
hasAhead (ID4, ID5)
hasDestinationTo (ID1, Paris)
hasDestinationFrom (ID1, Amsterdam)
ID5
Car (ID5)
hasConfiguration (ID3,ID9)
hasColor (ID5, Red)
ID11
hasConfiguration (ID4, ID10)
hasAhead (ID5, ID3)
hasConfiguratioin (ID5, ID11)
hasBehind (ID5, ID4)
65
Reconfiguration behavior (option 2 recolor)
ID1
ID1
ID3
hasConfiguration (ID1,ID12)
Locomotive (ID3)
ID12
ID6
hasColor (ID3, Muticolor)
hasBehind (ID3, ID4)
Train (ID1)
ID4
hasPart (ID1,ID3)
Car (ID4)
hasPart (ID1, ID4)
hasColor (ID4, Red)
ID13
hasPart (ID1, ID5)
hasAhead (ID4, ID3)
hasDestinationTo (ID1, Paris)
hasBehind (ID4, ID5)
hasDestinationFrom (ID1, Amsterdam)
ID5
Car (ID5)
hasConfiguration (ID3,ID6)
ID14
hasColor (ID5, Beige)
hasConfiguration (ID4, ID13)
hasAhead (ID5, ID4)
hasConfiguratioin (ID5, ID14)
66
5 Smart Interfaces and 4i-Technology

• Smart Interfaces will be developed to support
  dynamic context-aware A2R (Agent-to-Resource)
  interfaces. Such interfaces will be able not only
  to translate one data format for another one but
  also intelligently select relevant features of
  the content to be sent from a sender to a
  receiver depending on current context.
• Additional requirement to smart interfaces (which
  is smart visualization) appears when the resource
  in A2R abbreviation is human, i.e. A2H
  (Agent-to-Human). We are using our 4i technology
  (FOR EYE technology) to deal with that
  requirement. 4i is an ensemble of Platform
  Intelligent GUI Shell and visualization modules
  that provide context-dependent representation
  view of a resource data.

67
4i-page in html as semantic mash-up
Power Plant
ARBUZ Inc.
Golden Bay
St.Peter Church
Food Market
Open 800
Closed 2200
Service 1000 1800
Telephone [phone]
What is now Wedding ceremony
68
7 Industrial Cases and Appropriate Prototypes

• Industrial Cases and Appropriate Prototypes has
  been developed and being constantly developed to
  prove the research concepts and find fast ways of
  its industrial utilization.

69
Conclusion

• UBIWARE offers to industry intelligent software
  tools and a platform for the global EAI,
  information integration, secure, autonomous and
  self-configurable flexible architectures and
  services
• UBIWARE is based on three key factors for
  competitive ICT research Proactivity, Semantics,
  Intelligence
• UBIWARE is making intelligence (not just data or
  applications) available, self-descriptive,
  interoperable, reusable, proactive, self-managed
  
• UBIWARE has shown applicability in several domain
  areas
• We believe that UBIWARE seems to be in a right
  trend and in a right time

This presentation http//www.cs.jyu.fi/ai/ICINCO-
2008.ppt