Towards an Ubiquitous Pragmatic Web

Dr. Adrian Paschke
Director RuleML Inc.
and
Biotec Center,
Technical University Dresden
Adrian.Paschke AT gmx.de
Pragmatic
Web
Semantic
Web
Towards an Ubiquitous
Pragmatic Web

2 SIWN Congress 2008, Glasgow, UK, 22-24 July 2008 © Dr. Adrian Paschke Keynote: Towards an Ubiquitous Pragmatic Web
RuleML
RuleML Inc. (http://www.ruleml.com)
Founded in Canada
Umbrella organization of the RuleML Initiative
Acts world-wide (locations Canada, Germany, ...)
Rule Markup and Modeling Initiative (RuleML)
(www.ruleml.org)
Goal: Promotion of modern and future generations of web-based rule
technologies
Members from research, standardization, industry and government
Collaboration with W3C, OMG, OASIS, EPTS, DARPA-DAML, XBRL, and
many other standardization- and governmental bodies

Biotec Center Dresden – TU Dresden
Biotechnology Innovation Center
http://www.biotec.tu-dresden.de/
Interdisciplinary Excellence Center of the Technical University
Dresden
 Research groups for Bioinformatics, Genomics, Proteomics, Biophysics,
Cellular Machines, and Tissue Engineering
Biotec
 150 Researcher from over 10 countries with
background from Computer Science,
Engineering, Biology, Medicine, Physics,
Chemistry
 Bioinformatics group is member of International
Competence Centre for Computational Logic
 International Master Program in Molecular Bio-
engineering
 20 Biotec companies with ~200 employees
 60.000.000 EUR Center for Regenerative
Therapies Dresden (CRTD).

Agenda
Towards a Dynamic
Value Web of Services
and Things
 IT-Service-Management and
Technology for an Internet of
Services and Things
Enabling Technologies
 Complex Event Processing
Technologies
 Semantic Web Technologies
 Towards an Ubiquitous
Pragmatic Web
 A Vision
Virtualization Layer /
SAN, Grid Computing
Smart Things
Agents /
Services
Blades, Cluster
Load Balancer
UDDI
Storage with
virt. Images
Network,
Switches
Semantic Web

Business Services
Horizontal and Vertical App. Services,
Web-/Grid Serv., SOA
ManagementServices,ServiceMgt
Blades, Cluster
Load Balancer
UDDI
Storage with
virt. Images
Network,
Switches
dd
Self-Managing Systems /
Autonomic Computing
dd
SAN, Grid Computing
Towards a Dynamic Value
Web for IT and Business
Services
IT-Service-Technology and –Management
for an Internet of Services and Things
Bichler, M., Diernhofer, N., Fay, F., König, C., MacWilliams, A., Paschke, A., Setzer, T., Völk, G.: "Dynamic Value Webs for IT-
Services - IT-Service Technologies and Management, Siemens SBS / TUM research study, 10/2004.

Self-Managing Systems and Virtualization Layer
Business Services
Web-/Grid Serv., SOA
Blades, Cluster
Load Balancer
UDDI
Storage with
virt. Images
Network,
Switches
dd
Autonomic Computing
dd
SAN, Grid Computing
Virtual
Server
Load
Balancing
Server Farm, Grids
Virtual
Servers
Server Virtualization
SAN
Virtual Storage
Reduced operating costs
- Reduced number of manual tasks
- Automated operations
LPAR,
etc.
Scalable Hardware
Blades
Autonomic Computing
Grid Computing

Hardware Approaches
Scalable Hardware
Blade Server / HighPerformance-Server
Server Farms/ Server Cluster
Load Balancing (software-/hardware-based)
Server Virtualization
Storage Virtualization
Storage Area Networks
Virtual
Server
Load
Balancing
Virtual
Server
Server Farm
Server Virtualization
Fibre Channel
Switch
SAN

Autonomic Computing
Goal: Reduce running costs by reducing manual
interventions, simplified and automated system operation
Example: Reaction of a living organism
Chop-Cycle:
Self-configuring
Self-healing
Self-optimizing
Self-protecting
Autonomic Systems
Autonomic Infrastructures

Example: Self-optimizing and configuring SAP System
1. User wants to use a SAP service
2. Control instance detects
unavailable service
3. (Blade) server will be started
automatically with Linux and
starts a SAP service
4. Operation systems and SAP
software are loaded from NAS
system
User
Control
instance
Application Server
(Linux)
Database Server
(e.g. RISC-Unix)
Net Attached
Storage

Grid Computing
Goal: Transparent usage of heterogenous IT resources as Grid Services
Solution: Virtualization by open standards and protocols
Grid Computing
 Information Grids
 Ressourcen Grids
 Service Grids
Features:
 Local autonomy
 Scalability
 Heterogenous Resources
 Dynamic and adaptive
GGF, OGSA, OGSI, Globus Toolkit, EGA, RTI
Market perspectives:
 On the move from test to real production systems
 $12 Milliarden in 2008
 Commercial Usage since 2008
 EU ICT Grid Projects

Business Services
Business Services
Web-/Grid Serv., SOA, SCA, ASP, SaaS, BSN
Blades, Cluster
Load Balancer
UDDI
Storage with
virt. Images
Network,
Switches
dd
Autonomic Computing
dd
SAN, Grid Computing
Firewall
TCP/IP
Travel
Services
WSDL
Car Rental
Billing Server
Event
Reservations
Hotel Reservation
Insurance
Banking
WSDLWSDL
UDDI
WSDL
GPS/Galileo
WeatherFirewall
WSDL
WSDL
WSDL
Business Services Network / Service Supply Chain
Internet of
Services
Internet of
Things
Smart Things

Service Oriented Computing
M. P. Papazoglou and D. Georgakopoulos. Service-oriented
computing. Communications of the ACM, 46:2528, 2003.
Business
Processes
Application
Components
Services
Hardware
Service Component ArchitectureService Oriented Architecture
Description and Basic Services
Composition
Management
Basic Services
Composite Services
Managed Services
Publication
Discovery
Selection
Binding
Coordination
Conformance
Monitoring
QoS
Certification
Rating
SLAs
Support

Business Process Standards
BPMN 2.0
2008
BPDM
Source: Martin Bartonitz/Saperion

Next Generation Media – A New Thing Dimension
EU ICT – New Thing Dimension
 Focus “Internet of Things”
EU IST technology paradigm
“Ambient Intelligence”
 Ubiquitous Computing
 Pervasive Computing
Example „Smart Shelf“
Source: ITU
Source: O. Etzion, 6th
CEP/BAM/BPM Expert
Meeting, Regensburg,
2008

Service and Business Management
Business Services
Web-/Grid Serv., SOA, SCA, SaaS, BPM
Blades, Cluster
Load Balancer
UDDI
Storage with
virt. Images
Network,
Switches
dd
Autonomic Computing
dd
SAN, Grid Computing
Description and Basic Operations
Publication
Discovery
Selection
Binding
Composition
Coordination
Conformance
Monitoring
QoS, Security
SLA
Rating
Certification
Revenue Mgt.
Management

IT Service Management (ITSM) and Business Process Mgt. (BPM)ManagementServices,ServiceMgt
Technical View
EndEnd
IT Infrastructure
Management
IT Service
Management
Business Prozess
Management
to
IT Governance / IT Business
Value Management
Operational
Tactical
Management
Dimensions
Business View

Service Management (ITIL, BS15000/ISO20000)ManagementServices,ServiceMgt
Network,
Systems, DB
& Operations
Management
Customer
Relationship
Management
Release
Management
Security
Management
Service
Desk
Service Delivery
Configuration
Management
Incident
Management
Problem
Management
Availability
ManagementChange
Management
Business, Customers, Users
IT Service
Continuity Mgt.
Service-Level
Management
Capacity
Management
Financial
Management
ISO 20000
Source: ITIL

Service Level Management

SLM and BAM
monitoring of time-critical
bottleneck factors
and transaction control
transparency over
integrated represented
processes
representation of the
effects of system
availabilities and
-disturbances
process and system-
linked emergency and
disturbance management
monitoring of
SLA-compliance
Workflow
Management
Business
Process
Monitoring
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
100%
97%
95%
SLA
2 3Measures: 41
Business
Activity
Monitoring
Service Level
Monitoring
SLA Life Cycle

Fixed
Infrastructure
Business
Utility
IT
Utility
The Emergence of the Utility Concept
Web-based
Business Service
Solutions
Web-based IT
Infrastructure
Custom IT
Infrastructure
Web-based IT
Management
Services
1990 2010
Custom
Infrastructure
Common
Web-based
Infrastructure
2008
The IT utility is here
… the business utility
is coming
Custom IT
Management
Services
Custom Business
Services
Small Business
Customer
Service Provider
(e.g. Portfolio Mgt.)
Utility Provider
Hosting, Bandwidth,
Storage,..
Small Subscription Pricing
Stock purchase transaction price = €5 / € 1000 trans. vol.
Stock purchase limit price = € 3 per transaction
Response time violation price = € 2 per 0,01% deviation
Throughput violation price = € 1 per 1% deviation
Utility Provider Pricing
Base Price = € 10 per month
Storage Price=2GB average per day
Network bandwith price= € 3 per MB/h
Internet connectivity price = € 10 per GB vol. per month
Internet connectivity exceeded price = € 1 per 10 MB vol.
Data throughput violation price = € 1 per 0,8% deviation
On-Demand Rule-based,
autonomous exec. of SLAs
Pay-per-use

Utility Computing

IT-Infrastructure
(Storage, CPU,...)
eBanking
Service
Application Provider Utility Service Provider
Consumption-based usage
and accounting
(pay-per-use, price discrimination, …)
Flexible
IT Infrastructures
and Enterprise
Service Networks
(Grid Computing,
SAN, SOA ...)
SLA
Functionality,
Quality of Service, Pricing,
Contract Penalties, ...
P1  P2 => P3
P3  P4  P5 => P6
...
Rule-based
SLA Management
Ressourcen
C1
C2
C3
Service Requests
D1 D2 D3
Pricing and
Revenue Management

Rule Based Service Level Agreement (RBSLA)
http://rbsla.ruleml.org

Business
Process
Business
Services
Infrastructure
Process
Architect
Service
Integrator
Infrastructure
Provider
= New Roles
Business
Strategy
Solution
Architect
The “Foundation”
The “Appliances”
The “Frameworks”
The “Blue Print”
New Roles in the Business Services Value Chain
Vertical Expertise
Business Best Practices
The “Builders”
Business Solution Aggregator
EventModeller

Emerging Enabling Technologies

Complex Event Processing - Why do we need?
Event Cloud
Travel
Services Car Rental
Billing Server
Event
Reservations
Hotel Reservation
Insurance
Banking
GPS/Galileo
Weather
passwdchange
new auto pay
account login
account login
deposit
activity history
withdrawal
logout
account login
account balance
transfer
deposit
new auto pay
enquiry
enquiry
logout
passwdchange
new auto pay
account login
account login
deposit
activity history
withdrawal
logout
account login
transfer
deposit new auto pay
enquiry
enquiry
logout
book
request
Networked Service Supply Chain
Detection, prediction and mastery of complex events
and situations are crucial to the competitiveness of
networked businesses and the efficiency of dynamic
distributed service infrastructures
Challenge for Complex Event Processing
incident

Complex Event Processing – What is it?
(CEP) is a discipline that
deals with event-driven
behavior
 Selection, aggregation, and
event abstraction for
generating higher level
complex events of interest
Business View
Technical View
ComplexEventProcessingMedia
3. Event
Selection
6. Event
Consumption2. Event
Definition
4. Event Aggregation
5. Event
Handling
1. Event Production
CEP
Media

Complex Event Processing
CEP
Media
Detect
Decide
Respond
Discrete event simulation
Distributed Event-based
Computer networks
Active databases
Middleware
Rules
Workflows /
Process
Models

Event
Originator
Event
Processing
Media
Event
Consumer
Event
Modeler
Event Sources / Event Producer
Event Pattern Definition
Event Monitoring, Tracking, Discovery,
Selection
Event Consolidation, Composition,
Aggregation, Detection
Event
Definition
Event Selection
Event
Aggregation
Event Rating, Situation Detection,
Prediction
Event Handling
Operator, Management
Event
Production
Event
Consumption
Atomic
Events
Complex
Events
Business
Events
General CEP Reference Architecture

Selected CEP Solution Segments – Business Value
BAM, ITSM Monitor and detect
exceptional IT
service and
business behavior
from occurred
events
RTE
Quick decisions,
and reactions to
threats and
opportunities
according to
events in business
transactions
Information
Dissemination
Valuable
Information at
the Right Time
to the Right
Recipient
CEP
Media
Detect
Decide
Respond
Diagnose and/or
proactively predict
problems based on
symptoms and
resolve them
Proactive
Diagnostics

Example
Across the 24 largest airlines more than 5.6
million bags went missing in 2006, this is an
average of 15.7 bags per 1,000 travelers. 15%
of the are never found.
BBC News, April 4, 2007
Orchestrate:
Passenger has been
routed to another
destination – send
the language
Orchestrate:
Bag has reached to
the wrong aircraft
Notify:
Bag has been
checked but did not
reach the ULD within
20 minutes
Notify:
Bag has been
checked but did not
reach the connecting
flight
Situation Awareness by CEPSource: O. Etzion, 6th CEP/BAM/BPM Expert
Meeting, Regensburg, 2008

Towards an Event Driven Internet of Services
Gartner Symposium Itxpo 2004 Yefim Natis, Cannes, France, 31 October - 4 November 2004
Service-Oriented Architecture: Composite Applications, Web Services and Multichannel Applications

Semantic Web
"The Semantic Web is an
extension of the current web in
which information is given well-
defined meaning, better
enabling computers and people
to work in cooperation."
Tim Berners-Lee, James Hendler, Ora
Lassila, The Semantic Web
„Make the Web understandable
for machines“
Principles (Original Semantic
Web Stack as of 2003)
 Development in layers – each layer
depends on the other
 Downwards compatible
 Up-wards: partial understanding

New Semantic Web Stack
W3C Semantic Web Stack as of 2008

Use Cases / Applications
Semantic-enriched Search
Content management
Knowledge management
Business intelligence
Collaborative user interfaces
Sensor-based services
Linking virtual communities
Grid infrastructure
Multimedia data management
Semantic Web Services
…
Employee
Management
Article
Database
Order
Management
Warehouse
CRM
Customer
RDF
Hub
 Enterprise Application
Integration
 „Semantic“ Web Services
 Knowledge Management
 Content Classification
in Portals…

Use Cases: GeneOntology
Proteinsequence and structures are annotated with the GeneOntology
19.000 Terms
Cellular Components
Biological Process
Molecular Function
GO concept
Is-a relationship
Parent

Many pages
nothing
useful
“Hardware
Store
Dresden”
“yellowPages:Store:Har
dware Region:Dresden”
Hardware
Shopping Mall
Store
Region City
Ontology
Relevant pages
which conform to
the ontology
<a onto=…>
Use Cases: Semantic Web Search
Ontologie-
based Search

GoPubMed – Semantic Enriched Search Engine
Go Pub Med
Semantic-enriched search engine on top of pubmed.org
-search results are ranked using ontologies
-related terms are highlighted, usable for further search

Semantic Web Tools / User Interfaces
Triple Stores
RDFStore, AllegroGraph, Tucana
RDF Gateway, Mulgara, SPASQL
Jena’s SDB, D2R Server, SOR
Virtuoso, Oracle11g
Sesame, OWLIM, Tallis Platform
…
Reasoners / Rule Engines
Prova, OO jDrew
Pellet, RacerPro, KAON2, FaCT++
Ontobroker, Ontotext
SHER, Oracle 11g, AllegroGraph
…
Converters
flickurl, TopBraid Composer
GRDDL, Triplr, jpeg2rdf
…
Search Engines
Falcon, Sindice, Swoogle
…
Middleware
Rule Responder, IODT, Open Anzo, DartGrid
Ontology Works, Ontoprise
Profium Semantic Information Router
Software AG’s EII
Thetus Publisher, Asio, SDS
…
Semantic Web Browsers
Disco, Tabulator, Zitgist, OpenLink Viewer
…
Development Tools
SemanticWorks, Protégé
Jena, Redland, RDFLib, RAP
Sesame, SWI-Prolog,
Prova Rule Manager
TopBraid Composer
DOME
…
Semantic Wiki systems
Semantic Media Wiki,
Platypus, Visual knowledge

Web Rules Technology
Users employ rules to express what they want, the
responsibility to interpret this and to decide on how to do it is
delegated to an interpreter
Represent knowledge in a
way that is understandable
by ‘the business’, but also
executable by rule engines,
thus bridging the gap
between business and
technology

Example: Logic Derivation Rules on the Web
Prolog-like formalization:
''The discount for a customer buying a product is 5.0 percent
if the customer is premium and the product is regular.''
''The discount for a customer buying a product is 7.5 percent
if the customer is premium and the product is luxury.''
Business Rules:

Example Bonus Malus SLA Policy
Bonus-Malus System
Quality of Service (QoS) Average Availability (quantitative) Bonus/Malus Discount
High 100 % + 5%
Normal 98-100 % + 0%
Low <98 % - 5%
Below average <95 % 1000 $ penalty
Predicate VariablePredicate
qos Service=
Complex Term
availability(Service)
Constant
100%
Predicate Variable
qos Service
Predicate
discount
Variable
Service
Constant
5%
If QoS is high then provide a bonus of 5% on the base price.
Body Head
Body Head
If average availability is 100 % then QoS is high.
Constant
high
Constant
high

Example: Rule Based Service Level Agreements
CIM
PIM
PSM
Web Service
Application
Service Using
Application
WSDL
Contract / SLA
Management
Contract / SLA
Management
SLA
Response Time
Delay / Availability
Resource Utilization
Functionality
Guarantees
Pricing / Penalties
Service
Customer/User
Service Provider
Knowledge Representation
for SLAs / Policies
RBSLA Project
http://rbsla.ruleml.org

Web Rule Languages
ILog
IRL
Blaze
SRL
Prova
PRR RuleML RIF
SBVR
Platform
Specific
Platform
Independent
Computational
Independent

RuleML
ILog
IRL
Blaze
SRL
Prova
PRR RuleML RIF
SBVR

RuleML
Rule Markup and Modeling Initiative (RuleML)
(www.ruleml.org)
representatives from academia, industry and government
promotion of the modern and future generations of Web rule
technology
RuleML is currently the de facto open language
standard for Web Rules
W3C Rule Interchange Format in preparation
Collaborating with W3C (RIF), OMG (PRR,
SBVR), OASIS, DARPA and other
standards/gov'nt bodies

RuleML Enables ...
Rule
modelling
markup
translation
interchange
execution
publication
archiving
in
UML
RDF
XML
ASCII

Rule Interchange
RuleML
(e.g. OWL, RDFS)

RuleML Language Family
RuleML
Derivation
Rules
Reaction
Rules
Integrity
Constraints
Transformation
Rules
Derivation RuleML
Integrity
RuleML
Reaction RuleML
RuleML
Translators
Layered Approach

Schema Modularization
RuleML is specified by a
set of modular XSDs
XML Schema + EBNF
Syntax
Full RDF compatibility via
type and role tags (akin to
triple syntax);
XML Schema
Modularization: Layered
and uniform design

RuleML Language Family – Derivation RuleML
Modules
Languages
/ Dialects

RuleML 0.91 – Striped Syntax
''The discount for a customer buying a product is 5.0
percent
if the customer is premium and the product is regular.''
<Implies>
<head>
<Atom>
<op><Rel>discount</Rel></op>
<Var>customer</Var>
<Var>product</Var>
<Ind>5.0 percent</Ind>
</Atom>
</head>
<body>
<And>
<Atom>
<op><Rel>premium</Rel></op>
<Var>customer</Var>
</Atom>
<Atom>
<op><Rel>regular</Rel></op>
<Var>product</Var>
</Atom>
</And>
</body>
</Implies>
Implies
head
Atom
op Rel discount
Var customer
Var product
Ind 5.0 percent
body
And
Atom
op Rel premium
Var customer
Atom
opr Rel regular
Var product
Type Tag
Role Tag

Scope of Reaction RuleML
* Transient Events
* ECA Paradigm
* Global Active Rules
* Trigger (EA Rules)
* Complex Event Algebra
Active Databases
Reaction RuleML
Production Rule
Systems
Rule-Based Event Notification
Systems / Distributed Complex
Event Processing
KR Event / Action /
Transition / Process
Logic Systems
* Implicit Sequence of
Knowledge Updates
* CA Rules
* Event / Action Messages
- Inbound / Outbound
- Enterprise Service Bus
* (Agent) Conversation
- Protocols
- Performatives (e.g. FIPA ACL)
* Event / Action Axioms
* Reasoning on Effects /
Transitions
- fluents / states / processes
- akin to e.g. state machines,
petri-nets or pi-calculus

Reaction RuleML
Reaction RuleML (http://ibis.in.tum.de/research/ReactionRuleML/)
Quasi-Standard for Reactive Web Rules
Production rules, ECA rules and variants such as Trigger (EA), intelligent rule-
based CEP, KR Event/Action Logics, Process Algebras, …
Application Domains:
Event Processing Networks
Event Driven Architectures (EDAs)
Reactive, rule-based Service-Oriented Architectures (SOAs)
Active Semantic Web Applications
Real-Time Enterprise (RTE)
Business Activity Management (BAM)
Business Performance Management (BPM)
Service Level Management (SLM) with active monitoring and enforcing of Service
Level Agreements (SLAs) or e-Contracts
Supply Chain Event Management
Policies
Web-based Workflow Systems
…

General Concepts (1)
General (reaction) rule form that can be specialized
as needed
Three general execution styles:
Active: 'actively' polls/detects occurred events in global ECA style, e.g.
by a ping on a service/system or a query on an internal or external event
database
Messaging: Waits for incoming complex event message
Reasoning: KR event/action logic reasoning and transitions (as e.g. in
Event Calculus, Situation Calculus, TAL formalizations)
Appearance
Global: ‘globally’ defined reaction rule
Local: ‘locally’ defined (inline) reaction rule nested in an outer rule
General Concepts

General Syntax for Reaction Rules (Reaction RuleML 0.2)
<Rule style="active" eval="strong">
<on>

</on>
<if>

</if>
<do>

</do>
<ifPost>

</ifPost>
<doAlternative>

</doAlternative>
</Rule>

Reaction RuleML – Rule Type Examples
Derivation Rule: <Rule style="reasoning">
<if>...</if>
<then>...</then>
</Rule>
Production Rule: <Rule style="active">
<if>...</if>
<do>...</do>
</Rule>
ECA Rule: <Rule style="active">
<on>...</on>
<if>...</if>
<do>...</do>
</Rule>

Messages in Reaction RuleML
<Message mode="outbound" directive="ACL:inform">
<oid>  </oid>
<protocol>  </protocol>
<sender>  </sender>
<content>  </content>
</Message>
@mode = inbound|outbound – attribute defining the type of a message
@directive – attribute defining the pragmatic context of the message, e.g. a
FIPA ACL performative
< oid > – the conversation id used to distinguish multiple conversations and
conversation states
< protocol > – a transport protocol such as HTTP, JMS, SOAP, Jade,
Enterprise Service Bus (ESB) ...
< sender >< receiver > – the sender/receiver agent/service of the message
< content > – message payload transporting a RuleML / Reaction RuleML
query, answer or rule base

Selected Reaction RuleML Extended Features
Support for Event / Action algebras
Support for different selection and consumption policies
Support for intervals (Time, Event)
Support for situations (States, Fluents)
Support for external event query languages
Support for external vocabularies, e.g. Common Base
Event (CBE)
Support for external action execution / procedural
attachments
...

Towards a Ubiquitous
Pragmatic Web
A View into the Crystal Ball

Pragmatic Web
Vision: Ubiquitous Pragmatic Web 4.0
Monolithic
Systems Era
Desktop Computing
Deskto
p
World Wide Web 1.0
Connects Information
Syntactic Web
Semantic Web 2.0
Connects Knowledge
Social Semantic Web 3.0,
Web of Services & Things,
Corporate Semantic Web
Connects People, Services and Things
Ubiquitous Pragmatic Web 4.0
Connects Intelligent Agents and Smart
Things
Semantic Web
Ubiquitous autonomic
Smart Services and
Things
Pragmatic Agent
Ecosystems
Machine
Understanding
Ubiquitous Next Generation Agents and Social Connections
Syntac
tic
Web
Semanti
c Web
Pragmatic
Web
HTML
XML
RDF
Smart
Agents
Content
Producer
Passive Active
Consumer

Towards a Pragmatic Web
1. Explicit Meta-data
 vCard, PICS, Dublin
Core, RDF, IEEE LOM
(Learning Objects
Metadata), Micro
Formats, FOAF, SIOC …
2. Ontologies
 RDFS, OWL Lite|DL|Full
3. Logic and Inference
 e.g. Logic Programming
Rule/Inference Engines
4. Software Agents and
Web Services
 FIPA, Semantic Web
Services, RBSLA, …

Pragmatic Agent Web
Interaction

Pragmatic Web
Semiotics, the study of sign processes, includes the study of how meaning is
constructed and understood.
„Pragmatics, a basic field of linguistics today, originally had its roots in Morris's
idea of a division of signs concerned with "the relations of signs to their
interpreters" or users. “
Eugen Halton, http://www.nd.edu/~ehalton/Morrisbio.htm, 1992
Pragmatic Web
 Utilize the heterogenous Semantic Web resources, meta data and meaning
representations with intelligent agents and web-based services with the
ability to understand the others intended meaning (pragmatic
competence)
Collaborate in a communicative conversation-based process where
content and context is interchanged in terms of messages (relation of signs)
between senders and receivers (interpreters/users).
Pragmatic layer/wrapper around semantic/content e.g. by KQML / ACL like
speech-act primitives (e.g. assert(content), retract(content), query(kb))
Model, negotiate and control shared and invividual meanings
 requires learning and knowledge adaption / updates

Pragmatic
Agent Web
Rule Responder Project:
http://responder.ruleml.org

ILog
IRL
Blaze
SRL
Prova XCML
PRR RuleML RIF OCL
SBVR
PROVA
Distributed Semantic Web Rule Engine
http://prova.ws

What is Prova?
Sourceforge Open-Source Project (http://www.prova.ws/)
Activity 86.69% ; Downloads 28.529; 10 new requests per month, 254
registered users
Use Cases e.g. :
 Xalia Core for Services - Resource / Service Allocation: Prova used for efficiently
computing global execution plans
http://xdn.xcalia.com/xdn/docs/files/XcaliaCore/4.3.0/documents/html/introduction-
4.htm
 W3C Semantic Web Rule Interchange Format (W3C RIF):
http://www.w3.org/2005/rules/wg/wiki/List_of_Rule_Systems
 Rule-based IT Service and Contract Management: RBSLA Projekt
http://ibis.in.tum.de/projects/rbsla/index.php
 Rule Responder Pragmatic Agent Web
http://ibis.in.tum.de/projects/paw/
Distributed Semantic Web Rule Engine and Agent/Service-
Oriented Architecture:
“Prova follows the spirit and design of the recent W3C Semantic Web
initiative and combines declarative rules, ontologies and inference with
dynamic object-oriented programming and access to external data
sources via query language built-ins.”

Prova – Selected Expressive Features
 External Data and Object Integration + Query Built-Ins
Java Integration
 XML Integration
SQL Integration
RDF Integration
 External Type Systems: Order-Sorted Polymorphic Typed Logic
Java Class Hierarchies
Semantic Web Ontologies
 Input/Output Mode Declarations
 Module Import and Integration: Order Modularized Logic Programs
 Meta Data Labels and Scopes (constructive views)
 Integrity Constraints and Test Cases for Verification and Validation
 Backward-reasoning Derivation rules + ECA-style rules
 Messaging Reaction Rules
 Dynamic Transactional Updates

Conversation Based Event Messaging and
Processing
process_join() :-
iam(Me),
init_join(XID,join_1,[c(_),b(_)]),
fork_a_b(Me,XID).
fork_a_b(Me,XID) :-
rcvMsg(XID,self,Me,reply,a(1)),
fork_c_d(Me,XID).
fork_a_b(Me,XID) :-
rcvMsg(XID,self,Me,reply,b(1)),
join(Me,XID,join_1,b(1)).
fork_c_d(Me,XID) :-
rcvMsg(XID,self,Me,reply,c(1)),
% Tell the join join_1 that a new pattern is ready
join(Me,XID,join_1,c(1)).
% The following rule is invoked by join once all the inputs are assembled.
join_1(Me,XID,Inputs) :-
println(["Joined for XID=",XID," with inputs: ",Inputs]).
% Prints
% Joined for XID=agent@hostname001 with inputs [[b,1],[c,1]]
- Semantics a la Petri nets and pi-calculus
- Workflow patterns

Mule Enterprise Service Bus
Mule ESB Open Source
Message Platform and
distributed Object Broker
Staged Event Driven
Architecture (SEDA)
> 30 Protocols (JMS, HTTP,
SOAP …)
Synchronous and
Asynchronous
Communication
Complex Message-driven
Event Processing (CEP)

W3C Sematic Web eScience Infrastructure
GoPubMed

Emerging eScience Services in Health Care and Life
Sciences
 Emerging web-based
eScience services and data
sources
Publications, Patents,
Ontologies, Hypothesis,
Genome, Pathways, Molecular
Properties, Disease, etc.
 Heterogenous distributed
tools, services and data
sources
 Millions of data
e.g. PubMed 2000-5000 new
publications every day
Large ontologies (e.g. Gene
Ontology); ~22000 of concepts
 Different knowledge
representation formatsGoPubMed
Ask Queries / Derive Knowledge
Get Decision Support
Submit Tasks
Trigger Actions
Get Reactions

Example Use Case

Example Use Case (Human View)
SWAN W3C HCLS KB
GoPubMed
GoPubMed Statistics
EMBL Patent Abstracts
1
2
3
4

Example Use Case (Human View)
SWAN W3C HCLS KB
GoPubMed
GoPubMed Statistics
EMBL Patent Abstracts
"IF a Person has most publications in
the Field and one or more Patents in the
field THEN the Person is an expert for
this Field".

Example Use Case (Rule Responder Solution)

Use Case: Virtual Organization
(Symposium Organization)

Responsibility Assignment Matrix
Role Model and
RAM in OWL

Use Case: Virtual Organization (3)
Request / Response Conversation

Meeting Scheduling

Review Allocation

Registration Process

Rule Responder Applications
Applications of the Pragmatic Agent Web and the Rule
Responder middleware in different projects
Rule Responder: http://responder.ruleml.org/
Reaction RuleML: http://reaction.ruleml.org
Prova Agent Architecture: http://www.prova.ws/
Prova Workflow Patterns: http://www.prova.ws/csp/?q=taxonomy/term/11
Rule Based Service Level Agreements: http://rbsla.ruleml.org
 Current Work
Integration of further rule engines into the middleware (Rule Responder project)
Extending the Pragmatic Foundations of Rule Responder
Syntactical standardization issues (Reaction RuleML Technical Group, W3C
Rule Interchange Format TG, Event Processing Technical Society)
Running projects:
 Corporate Rule Responder; Semantic Web-based enterprise infrastructures
 DILP (Distributed Inductive Logic Programming) for Mining Multi-Relation Data in Bioinformatics
 DoReMoPat: Domain-specific Reference Models for Complex Event Patterns
 Rule Responder Expert Finder; finding experts in virtual organisations
 Rule Responder HCLS eScience Infrastructure; W3C HCLS use case

Challenges
1. IT Virtualization, Resource Management, and Dynamic Service Orchestration
and Choreography
2. From System to Business Services Management
 Importance of non-functional properties and SLAs, e.g. 100% availability and security
 Operational, tactical and strategic management of business services
3. New On-Demand and Utility Computing Models
 New revenue models and planning models
4. New levels of Situation-awareness and Real-time / Proactive Reaction
 Intelligent Rule-based Situation Management, Decision Logic, and Behavioral Logic
5. Heterogeneous Data, Service and System Integration
 Semantic Web Technologies
 Enterprise Application / Service Integration Technologies
6. Universal Intelligent Ubiquitous Information Access and Collaboration
 Pragmatic Agent Web Technologies
 Human – Thing – Service Collaboration Interfaces
7. Design Theory and Engineering Issues

Thank You for Your Attention
http://responder.ruleml.org
Sourceforge SVN:
https://mandarax.svn.sourceforge.net/
svnroot/mandarax.
Sub-project "pragmatic-agent-web"
Smart Things
Agents /
Services
Blades, Cluster
Load Balancer
UDDI
Storage with
virt. Images
Network,
Switches
Semantic Web

Towards an Ubiquitous Pragmatic Web

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