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

http://www.ruleml.com/

http://www.ruleml.org/


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).

http://www.biotec.tu-dresden.de/




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

Managem

ent

Serv

ices,

Serv

ice M

gt

Blades, Cluster

Load Balancer

UDDI

Storage with

virt. Images

Network,

Switches

dd

Self-Managing Systems / Autonomic Computing

dd

Virtualization Layer / 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


Web-/Grid Serv., SOA

Managem

ent

Serv

ices,

Serv

ice M

gt

Blades, Cluster

Load Balancer

UDDI

Storage with

virt. Images

Network,

Switches

dd


dd


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)


Storage Virtualization Storage Area Networks

Virtual

Server

Load

Balancing

Virtual

Server

Server Farm


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


Web-/Grid Serv., SOA, SCA, ASP, SaaS, BSN

Managem

ent

Serv

ices,

Serv

ice M

gt

Blades, Cluster

Load Balancer

UDDI

Storage with

virt. Images

Network,

Switches

dd


dd


Firewall

TCP/IP

Travel

Services

WS

DL

Car Rental

Billing Server

Event

Reservations

Hotel Reservation

Insurance

Banking

WS

DL

W

SD

L

UDDI

WS

DL

GPS/Galileo

Weather Firewall

WS

DL

WS

DL

WS

DL

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 Architecture Service 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 Horizontal and Vertical App. Services, Web-/Grid Serv., SOA, SCA, SaaS, BPM

Managem

ent

Serv

ices,

Serv

ice M

gt

Blades, Cluster

Load Balancer

UDDI

Storage with

virt. Images

Network,

Switches

dd


dd


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) M

anagem

ent

Serv

ices,

Serv

ice M

gt

Technical View

End End

IT Infrastructure Management

IT Service Management

Business Prozess Management

to

IT Governance / IT Business Value Management

Operational

Ta

cti

ca

l Management

Dimensions

Business View


Service Management (ITIL, BS15000/ISO20000) M

anagem

ent

Serv

ices,

Serv

ice M

gt

Network,

Systems, DB

& Operations

Management

Customer

Relationship

Management

Release

Management

Security

Management

Service

Desk

Service Delivery

Configuration

Management

Incident

Management

Problem

Management

Availability

Management Change

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 3 Measures: 4 1

Business

Activity

Monitoring

Service Level

Monitoring

SLA Life Cycle

http://www.scientific.de/produkte/opnet/itguru.html


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

Cu

sto

m

Infr

astr

uctu

re

Co

mm

on

Web

-based

Infr

astr

uctu

re

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

Ressourc

en

C1

C2

C3

Service Requests

D1 D2 D3

Pricing and

Revenue Management

Rule Based Service Level Agreement (RBSLA)

http://rbsla.ruleml.org

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

Ev

ent

Mo

del

ler


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?

Complex Event Processing (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 Com

ple

x E

vent

Pro

cess

ing M

edia

3. Event Selection

6. Event Consumption 2. 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

http://www.scientific.de/produkte/opnet/itguru.html


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 CEP Source: 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

http://www.scientificamerican.com/2001/0501issue/0501berners-lee.html


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:

http://www.dfki.de/ruleml/exa/0.8/discount.ruleml.txt


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 Variable Predicate

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

http://www.ruleml.org/

http://www.w3.org/2005/rules/wg.html


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

…

http://ibis.in.tum.de/research/ReactionRuleML/


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

http://images.google.de/imgres?imgurl=http://www.net4low.com/kunden/avalon/media/kristallkugel-zwei.jpg&imgrefurl=http://www.net4low.com/kunden/avalon/de/dept_211.html&h=300&w=219&sz=28&tbnid=mn_moLjMjgQJ:&tbnh=110&tbnw=81&start=3&prev=/images%3Fq%3DKristallkugel%26hl%3Dde%26lr%3D%26ie%3DUTF-8


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

Mach

ine

Un

ders

tan

din

g

Ubiquitous Next Generation Agents and Social Connections

Syntactic

Web

Semantic Web

Pragmatic Web

HT

ML

XM

L

RD

F

Sm

art

Ag

en

ts

Co

nte

nt

Pro

du

ce

r

Passive Active

Co

ns

um

er


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

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.”

http://www.prova.ws/

http://xdn.xcalia.com/xdn/docs/files/XcaliaCore/4.3.0/documents/html/introduction-4.htm



http://www.w3.org/2005/rules/wg/wiki/List_of_Rule_Systems

http://ibis.in.tum.de/projects/rbsla/index.php

http://ibis.in.tum.de/projects/paw/


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 formats GoPubMed

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


Use Case: Virtual Organization (4) Meeting Scheduling


Use Case: Virtual Organization (5) Review Allocation


Use Case: Virtual Organization (6) 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

http://responder.ruleml.org/

http://reaction.ruleml.org/

http://www.prova.ws/

http://www.prova.ws/csp/?q=taxonomy/term/11



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

Complex Event Processing

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