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Scenarios with IBROW

Scenarios with IBROW. Spin-off applications. Spin-off Applications. Spin-off Applications. 1 st phase demo. Flow diagram. Small demo: execution. Spin-off Applications. Scientific Component finding, selection, integration/ configuration Distributive Adaptation Bridges, refiners

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Scenarios with IBROW

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  1. Scenarios with IBROW Spin-off applications

  2. Spin-off Applications

  3. Spin-off Applications

  4. 1st phase demo

  5. Flow diagram

  6. Small demo: execution

  7. Spin-off Applications

  8. Scientific Component finding, selection, integration/ configuration Distributive Adaptation Bridges, refiners Interoperability UPML experience Practical Useful in practice Strong involvement industrial partner Prototype after six months Nature of components KM components/PSMs Objectives Web Workbench

  9. Knowledge management problem • Introduce KM in departments • What is KM problem at department? • Many tools and techniques available • How to find and select them? • How to combine them? Problem  tasks to achieve  enablers  KM plan

  10. KM problem characterization

  11. KM ontology

  12. Goals of KM tasks • Identify needed knowledge needed(K) • Search needed knowledge located(K) • Acquire or develop knowledge acquired(K) • Store knowledge stored(K) • Distribute and share knowledge available_at_end_user(K) • Control knowledge quality(K) • Foster use of knowledge exploited(K)

  13. Problems related to goals

  14. Enablers Altavista Google BP Win (Business Process for Windows) Brains   DDS (Digital City Amsterdam) Lotus Notes Netscape Messenger Internet Explorer EudoraMail Brown paper session (Erica Jones tool) Card sort Paradigm Plus Visual Basic Aion Rational Rose MS Visual Modeller Cu-SeeMe ICQ AOL Instant messenger Checkland diagrams CommonKADS MS Access Oracle Panagon Decision Manager (KISS-OO) Perl ASP Java Javascript Flow chart Gantt chart (in MS Project) Hyperknowledge IDEF Inspiration KADS Tool Knowledge editor Meta Pack Mindman/MindManager Mindmapper RelaxPlus Decision Pad BrainBox IdeaFisher Smart Draw Paradigm Plus PC Pack Pert charts (in MS Project) QFD designer QUIDS Rethink / Ithink Rich picture SAS decision tree tool State transition tool MS Word VisiMap Xpertrule FreeThought Plus STRAD

  15. Examples of enablers (class) Simulation tools Workshops Interviews Questionnaires Books Magazines Alerting services Document retrieval Search engines Brainstorming Knowledge acquisition tools Creativity techniques Information mapping Datamining Textmining Databases Organizational dictionary Ontologies Decision trees Communities of practice Virtual communities Groupware Video-conferencing Corporate portals Mailing lists E-mail Instant messaging Incentive systems

  16. Enablers

  17. Characterization of enablers DC UPML Extension for KM Applicationspecific

  18. Brokering

  19. KM Ontology Broker UI Matcher Checker Configurer Enabler KB Problem Characterization Architecture

  20. From the final user viewpoint • Browse and shop • Browsing an ontology • Browsing a library • Search for components • Problem oriented • Goal oriented • Enablers oriented • Manipulate components

  21. Order set of enablers along various criteria Notification of sub goals of a KM task Diagnosis KM problem: culprit task Generate (partial) order plan based on current selection of enablers Generate possible completions of KM plan that satisfy criteria Check consistency of selected set of enablers Suggest alternative enablers for same KM goal Other functionalities

  22. Implementation issues • Programming language: Java2 Platform • Modeling language: UPML • Modeling editor: Protege2000 • User interface • Java Applet • HTML + Javascript

  23. UI: Browsing

  24. UI: Brokering

  25. Spin-off Applications

  26. Automating access to information resources in molecular biology • Molecular biologist seek to exchange • Primary data • Software tools that can analyze their data, • Results of processing their data with such tools, • Experience that they have learned from applying the tools.

  27. Molecular biologists have to • Analyze large amounts of data by standard algorithms • Predict 3D structure of molecules given DNA sequence • Choosing the right algorithm is hard • Each algorithm has own characteristics (form) • Not possible to easily try out all

  28. Objective • Assemble library of algorithms for 3D structure • Take away (software engineering) burden to work with algorithms • Capture feedback of use with algorithms

  29. Many web sites for molecular biologists • They offer access to libraries of algorithms for gene-sequence analysis or protein-structure prediction. • Web-based software resources typically do not make explicit essential information, such as • the functions that the Web sites provide • the data that they can produce • the parameters that they require.

  30. How it works • Biologists submit experimental data as web-forms • They receive results concerning matching DNA or amino-acid sequences, protein structural information, or organism identification. • These tools include database search and complex algorithmic processing for matching, prediction or constraint satisfaction.

  31. IBROW approach • Annotate websites (algorithms and DB) with UPML • Website’s functionality is seen as PSM • KB already interoperable with PSM • Accessible to broker

  32. Query Configure Web Monitor Map Agent-based portal for problem-solving Record User community Problem request Problem-solving methods Problem-solving methods Problem-solving methods Data UPML-Data PSM Configuration Brokering agents Result report Data Data Data Data An agent-based Web portal for intelligent problem-solving Typical situation in which a community of users interacts with our service. Users submit problem-solving requests along with sets of data grounded in a shared ontology. The brokering agents search the Web for problem-solving methods capable of processing the submitted data, according to their PSM annotations.

  33. Agents that support a portal by mediating access to PSMs that meet users’ needs • PSM query agent: search and retrieve PSMs based on user’s request features • Domain-PSM mapping agent: map PSM requirements to user’s domain ontology • Experience-recording agent: markup cases of PSM use and mapping, for reuse and suggestion • Configuration agent: combine complementary PSM components together, compare equivalent PSMs • Monitoring agent: invoke and supervise PSM execution, user interaction, runtime data query, etc.

  34. Applications • Addresses U.S. Department of Defense’s pervasive interest in IT to address bio-terrorism • Bio-informatics test bed portal for emergency characterization of bio-terrorist agents

  35. Spin-off Applications

  36. Web Information Mediator • User: medical Professionals • Have several interests • Each interest is realized by a series of tasks • Task: Proactive Information Search&Organize • Automating Runtime Actions • WIM is designed with a series of tasks to further the user’s interests • Learning • about habits, interests, preferences, and resources

  37. WIM • Accessible from a website • User defines Interests • Automatic, dynamic configuration • Interests are defined by tasks and schedules • WIM configures a “special agent” for a task • WIM executes tasks by coordinating the components

  38. WIM services • Broker • User interaction in professional terms • Domain knowledge used by components to rank and select relevant information • For each task • Configures dynamically the components • Library has wrapper components for other resources • Results • Integrated, organized, and visualized for the user

  39. Keywords Bibliographical filters Quality assessment Presentation type Application type Analysis type

  40. Ontology User’s View - User terms are verified to pertain to the ontology used - User can explore related terms in ontology - If term is unknown alternative terms are suggested

  41. Results Dynamic Ranking User can select relevant dimensions to dynamically change the ranking, and view the results from several perspectives

  42. WIM provides a mediation service between the user’s information interests and the available information resources WIM is architecturally designed as a chain of mediator agents: Wrappers for content Ontology servers Query agents Info. Fusion agents Info. Analysis agents Learning agents Mediation

  43. BST Select sources Retrieval Source Data Fusion Result Browsing Query Model Ontology Translation Source- specific Query Results Assessment & Ranking Bibliographical search task

  44. Top WIM: Configuration PSM Ontology User Specialized PSM Web UI Broker Wrapped Content Providers Medical Ontology Task Ontology Server Database Medical Ontologies Medical Ontologies Medical Ontologies Configuration

  45. Special Agents Broker Task Ontology Ontologies Configuration Wrappers Server Database Resources Bottom WIM: Execution

  46. Configuration & Plan Special Agents Plan Executor Execution: Central Plan + RPC 1) Plan as in info-agents planning 2) Plan as a program in “Service Combinators” (Cardelli)

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