faw inst. für anwendungsorientierte wissensverarbeitung earthquake engineering workshop in escience...

FAW

Inst. für Anwendungsorientierte Wissensverarbeitung

Earthquake Engineering

Workshop in eScienceApplications for SeismologyMarch 7-9 2011, Edinburgh

On finding Links between Information Systems and Knowledge Based Systems

in Civil Engineering and Seismology / Earthquake Engineering

a.Univ.-Prof. Dr. Josef Küng

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Facts and Figures

FAW About the Institute

History- 1990 founded as a research institute- 1991 first year in Hagenberg- 1997 regularly institute of JKU- 2005 foundation of FAW-GmbH

- 2005 EU-FP6-Project SAFEPIPES- 2008 EU-FP7-Project IRIS- 2010 EU-FP7-Project NERA

Team (FAW-Institut)- currently 15 persons in research and development

R&D- more than 100 successful finished projects and co-operations- among others currently we are coordinating (together with Dr. Wenzel, VCE) the large EU-FP7 project IRIS (Integrated European Industrial Risk Reduction System)

(c) FAW – Johannes Kepler Universität|

Information and Knowledge

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Information

FAW Current Research Domains

Information ModelingAdaptive modeling toolModeling dynamic aspects of processes

Information-IntegrationSemantic data integration (in the grid)

DatawarehousesLoading Processes (e.g. automatic regression tests)

Information-ExtractionIntelligent (semantic and rule based) extractionof structured information out of unstructured web pages


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Knowledge

Semantic Technologies, Ontologies Using Topic Maps and Ontologies to supportqueries and decisions

Ontology Enineering

Case Based ReasoningSimilarity queries in Case Based Reasoning

Application of Case Based ReasoningStructural Health Monitoring

Application of Case Based Reasoningin passive and active Decision Support


FAW Current Research Domains

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our famous example: tiscover [1]

FAW Past Research Work

Introduction

Web Based Destination-Management-System

Access to complete and up-to-date information about Tourism Holiday Destinations

Booking Functions

System Provider:Tiscover AG Innsbruck

Development:FAW-HagenbergTiscover AG Hagenberg


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our famous example: tiscover [2]

FAW Past Research Work

tiscover is more than a web page


Public Terminal(AccessPoint)

Reservation &

CallCenter

CustomizedBooking Engine

Internethome/office

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ad Information: AMMI [1]

Meta Modeling Tool (Adaptive Modeling tool for Meta models and it Instances)


FAW Current Research Work

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Instance Modeling View



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Administration Module



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ad Knowledge: EU-Project IRIS [1]


Introduction

IRIS – Integrated European Industrial Risk Reduction System– Oct. 2008 – Mar 2012, about 40 Partners, mainly form civil engineering domain,

4 partners from IT-Domain, one associated partner form Japan (University of Tokyo ) and US (Drexel University, Stanford University)

Motivation– Within Current practices in risk assessment and management for industrial systems are characterized by its

methodical diversity and fragmented approaches. Integration is needed. – The large collaborative project IRIS is proposed to identify, quantify and mitigate existing and emerging risks to create

societal cost-benefits, to increase industrial safety and to reduce impact on human health and environment.

Basic Concept– The basic concept is to focus on diverse industrial sector’s main safety problems as well as to transform its specific

requirements into integrated and knowledge-based safety technologies, standards and services.

WP7: Monitoring, Assessment, Early Warning, Decision Support– FAW has its main task in this work package – setting up the decision support system.


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GeneralStructure

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Overall Goal – Find the early warning point




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Decision Support System

Passive Decision Support– Providing the right information at the right time to the decision maker

in order to support him/her. (i.e. via Data Warehouses or via good organized (good accessible/searchable) document bases

Active Decision Support– A system, that uses some AI (Artificial Intelligence) methods

to elaborate a proposal to the decision maker or to do a decision autonomously.

(data mining, neural networks, support vector machines, decision trees, case based reasoning, ... )

-> Within IRIS we work in both directions – Active Decision Support -> Case Based Reasoning– Passive Decision Support -> Semantic Networks




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Active Decision Support System

Case-based Decision Support(Example: Assessment of Simple Structures (Lamp Posts)

Data– Design (Type, Height, Material, ... )– Measurement (Set of selected eigenfrequencies ,

vibration measured after a stimulation)– Visual Inspection (Condition of post and stand,

Scratches, oxidation, condition of concrete)

Task– Classification of lamp post’s condition




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Active Decision Support System

Results– Currently case base consists of 800 measurements of different lamp posts – Above 90% “correct” classifications– Improvement of results:

• End-user can adjust parameters (attribute weights, predefined distances) – results are improving• Identify and exclude “unrepresentative cases” (where connection (parameter values classification result) is

irreproducible)• In some ways the inspection process could be adapted (e.g. less “free-text” attributes)

In contrast to complex structures like e.g. bridges, an automated assessment of more simple structures, as lamp posts are, looks very promising




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Passive Decision Support System

Combining Semantic Nets and Search Engines [1] (Example: VCDECIS)

– This system builds a basic level of a wide scoped passive Decision Support System

– Organization/management of an institution‘s content (documents) to enable easier retrieval of knowledge




17(c) FAW – Johannes Kepler Universität|



Passive Decision Support

Combining Semantic Nets and Search Engines [2] (Example: VCEDEIS )

Components– Search engine– Topic Map (3 layer), currently transferred to OWL– Web Portal

• Document upload platform• Topic Map navigator incl. full-text search

Content Topics

Topics

Content

18(c) FAW – Johannes Kepler Universität|



Decentralized Approach

– Each group can operate its own Knowledge Base (KB) and Decision Support Systems

– IRIS Knowledge Base provides interface to partner KBs– Web Portal to access and administrate IRIS KB– Decision support (data assessment)

mainly relies on local measurement data and on local background information (KB)

– OWL will be the languageKnowledge Representation(at higher level)

IRIS Ontology Landscape

IT-Framework, Current Big Picture

FAW EU-FP7-Project IRIS

19| (c) FAW – Johannes Kepler Universität

CBR-Cycle (Aamodt&Plaza1994):

• Case Base: General knowledge (knowledge base, e.g. models, reports, rules …) and already known cases

• Retrieve: Search– Retrieve the most similar case or cases

• Reuse: Adaptation– Reuse the information and knowledge

in that case to solve the problem• Revise: Verification

– Revise the proposed solution• Retain: Learn

– Retain the parts of this experience likely to be useful for future problem solving

Case Based Reasoning in General

Case Based Decision Support [1]



CBR for IRIS• Adopted to IRIS-Demands

• More flexible (to be used in different Domains)

Our new CBR-Framework for IRIS

Case Based Decision Support [1]



General Statements on Cloud Computing

Classical Computing

• Buy & Own: Hardware, System Software, Applications (often to meet peak needs)

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• Install, Configure,Test, Verify, Evaluate

• Manage:

• . . .

• Finally, use it

• €€€€€ - high Cost

Cloud Computing

Subscribe

Use

€ - pay for what you use, based on QoS (Quality of Service)

eve

ry 1

8 M

on

th?

Long Term Vision ‘The IRIS Cloud’ [1]



General Statements on Cloud ComputingDefinition 1

A Cloud is a type of parallel and distributed system consisting of a collection of inter-connected and virtualised computers that are dynamically provisioned and presented as one or more unified computing resources based on service-level agreements established through negotiation between the service provider and consumers.

Cloud Services• Software as a Service (e.g. Google Mail, … )

• Platform as a Service (e.g. Google App Engine, Microsoft Azure, … )

• Infrastructure as a Service (e.g. Amazon.com, … )

Ownership and Exposure• Public/Internet Clouds (3rd party Cloud Infrastructure and services, available on subscription basis)

• Private/Enterprise Clouds (Cloud runs within a company’s data center, for internal and/or partners use)

• Hybrid/Mixed Clouds (mixed usage of private and public clouds)

1 Rajkumar Buyya, Cloud Computing and Distributed Systems (CLOUDS) Lab, Dept. of Computer Science and Software Engineering, The University of Melbourne, Australia




IRIS Private Cloud




IRIS Private Cloud and Mediator




IRIS Private Cloud and Consumption




• Decision Support (WP7)

- State: Enhanced Case Based Reasoning Framework is in an implementation stage Work on Active Decision Support is promising

- Plan: Continue on CBR, Active Decision Support Knowledge Base and Prototypes (Proof of Concepts)

• Data / Knowledge Integration (WP6) and Risk Informed Design (WP8)

- State: IRIS System Landscape is in a stable version Work on Integration Ontologies is ‘well on track’ (e.g. Bride Ontology is almost finished) - Plan: Continue on Ontologies, keep integration in mind, (if time, think and work more on the IRIS-Cloud)

State, Plan for Next Steps



faw inst. für anwendungsorientierte wissensverarbeitung earthquake engineering workshop in escience...

Documents

ad information

information systems

knowledge slide

foundation of faw

research institute

uptodate information

fawpast research work

euproject iris