FAW Inst. für Anwendungsorientierte Wissensverarbeitung Earthquake Engineering Workshop in eScience Applications for Seismology March , Edinburgh On finding Links between Information Systems and Knowledge Based Systems in Civil Engineering and Seismology / Earthquake Engineering a.Univ.-Prof. Dr. Josef Küng

2 Facts and Figures FAWAbout the Institute  History founded as a research institute first year in Hagenberg regularly institute of JKU foundation of FAW-GmbH EU-FP6-Project SAFEPIPES EU-FP7-Project IRIS 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

3 Information FAWCurrent Research Domains  Information Modeling Adaptive modeling tool Modeling dynamic aspects of processes  Information-Integration Semantic data integration (in the grid)  Datawarehouses Loading Processes (e.g. automatic regression tests)  Information-Extraction Intelligent (semantic and rule based) extraction of structured information out of unstructured web pages (c) FAW – Johannes Kepler Universität |

4 Knowledge  Semantic Technologies, Ontologies Using Topic Maps and Ontologies to support queries and decisions Ontology Enineering  Case Based Reasoning Similarity queries in Case Based Reasoning Application of Case Based Reasoning Structural Health Monitoring Application of Case Based Reasoning in passive and active Decision Support (c) FAW – Johannes Kepler Universität | FAWCurrent Research Domains

5 our famous example: tiscover [1] FAWPast 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-Hagenberg Tiscover AG Hagenberg (c) FAW – Johannes Kepler Universität |

6 our famous example: tiscover [2] FAWPast Research Work tiscover is more than a web page (c) FAW – Johannes Kepler Universität | Public Terminal (AccessPoint) Reservation & CallCenter Customized Booking Engine Internet home/office

7 ad Information: AMMI [1] Meta Modeling Tool (Adaptive Modeling tool for Meta models and it Instances) (c) FAW – Johannes Kepler Universität | FAWCurrent Research Work

8 ad Information: AMMI [2] Instance Modeling View (c) FAW – Johannes Kepler Universität | FAWCurrent Research Work

9 ad Information: AMMI [3] Administration Module (c) FAW – Johannes Kepler Universität | FAWCurrent Research Work

10 ad Knowledge: EU-Project IRIS [1] FAWCurrent Research Work Introduction  IRIS – Integrated European Industrial Risk Reduction System – Oct – 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. (c) FAW – Johannes Kepler Universität |

11 ad Knowledge: EU-Project IRIS [2] FAWCurrent Research Work (c) FAW – Johannes Kepler Universität | General Structure

12 Overall Goal – Find the early warning point (c) FAW – Johannes Kepler Universität | ad Knowledge: EU-Project IRIS [2] FAWCurrent Research Work

13 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 (c) FAW – Johannes Kepler Universität | ad Knowledge: EU-Project IRIS [3] FAWCurrent Research Work

14 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 (c) FAW – Johannes Kepler Universität | FAWCurrent Research Work ad Knowledge: EU-Project IRIS [4]

15 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 (c) FAW – Johannes Kepler Universität | FAWCurrent Research Work ad Knowledge: EU-Project IRIS [5]

16 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 (c) FAW – Johannes Kepler Universität | FAWCurrent Research Work ad Knowledge: EU-Project IRIS [6]

17(c) FAW – Johannes Kepler Universität | FAWCurrent Research Work ad Knowledge: EU-Project IRIS [7] 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 | FAWCurrent Research Work ad Knowledge: EU-Project IRIS [8]  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 language Knowledge Representation (at higher level)

IRIS Ontology Landscape IT-Framework, Current Big Picture FAWEU-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] FAWEU-FP7-Project IRIS 20 | (c) FAW – Johannes Kepler Universität

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] FAWEU-FP7-Project IRIS 21 | (c) FAW – Johannes Kepler Universität

General Statements on Cloud Computing Classical Computing Buy & Own: Hardware, System Software, Applications (often to meet peak needs) 5 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) every 18 Month? Long Term Vision ‘The IRIS Cloud’ [1] FAWEU-FP7-Project IRIS 22 | (c) FAW – Johannes Kepler Universität

General Statements on Cloud Computing Definition 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 (3 rd 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 Long Term Vision ‘The IRIS Cloud’ [2] FAWEU-FP7-Project IRIS 23 | (c) FAW – Johannes Kepler Universität

IRIS Private Cloud Long Term Vision ‘The IRIS Cloud’ [3] FAWEU-FP7-Project IRIS 24 | (c) FAW – Johannes Kepler Universität

IRIS Private Cloud and Mediator Long Term Vision ‘The IRIS Cloud’ [4] FAWEU-FP7-Project IRIS 25 | (c) FAW – Johannes Kepler Universität

IRIS Private Cloud and Consumption Long Term Vision ‘The IRIS Cloud’ [5] FAWEU-FP7-Project IRIS 26 | (c) FAW – Johannes Kepler Universität

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 FAWEU-FP7-Project IRIS 27 | (c) FAW – Johannes Kepler Universität