Technology for Superimposed Information

1. Technology for SuperimposedInformation Lois Delcambre with Shawn Bowers, David Maier, Mat Weaver Database and Object Technology Lab Computer Science and Engineering Department Oregon Graduate Institute

2. Outline • introduction to superimposed information • a superimposed application: SLIMPad (DLI2 Project) • model-based representation and transformation of information • harvesting information to sustain our forests (NSF Digital Government project)

3. What is Superimposed Information? data “placed over” existing information sources to: • highlight • annotate • elaborate • select • collect • organize • connect • reuse information elements often to support new applications, beyond the original

4. Examples of Superimposed Information • Non-electronic examples: • Commentaries on religious texts, law, literature • Concordances, citation indexes • Electronic examples: • Your bookmark file in your web browser • RDF metadata

5. Why work on it now? • Broadening range of digital information • Easier to overlay than “hard copy” forms • More and more sources of base information • Accessibility/addressability to base information • Reference (e.g., URL) can be resolved quickly • Addressing at various levels of granularity • Emerging Standards: RDF, Topic Maps, XLink

6. The superimposed and base layers with marks Superimposed Layer marks Base Layer Information Source1 Information Source2 Information Sourcen …

7. Outline • introduction to superimposed information • a superimposed application: SLIMPad (DLI2 Project) • model-based representation and transformation of information • harvesting information to sustain our forests (NSF Digital Government project)

8. Paul Gorman, MD Lois Delcambre, PhD David Maier, PhD

9. Bundles in the wild……….. Observational team:Paul GormanJoan AshMary LavelleJason Lyman …………..Bundles in captivity Computer science team:Lois DelcambreDave MaierShawn BowersLongxing DengMathew Weaver

10. Let’s take a trip to the ICU

11. (Wild) Bundles

12. (Wild) Bundles

13. (Wild) Bundles

14. (Wild) Bundles • manage information for diverse, complex tasks • contain selected, collected, structured, annotated • are often used in settings with: • high uncertainty • low predictability • potentially grave outcomes • time & attention are highly constrained

15. (Wild) Bundles • There is benefit in creating (active processing of information) • There is benefit in reusing (trigger memory) • There is benefit in sharing (establish collective, situated awareness)

16. Given…. • bundles are everywhere! • access to bundles provides access to important information • information in bundles is often copied from other information sources • we can keep copied/referenced information linked through the use of marks

17. (Captive) Bundles • SLIMPad - a scratchpad application to create bundles but….with referenced information connected to the underlying source data • helping us explore architectural issues for building superimposed applications • motivating definition of a metamodel to represent information with mappings to transform • inspired by the observational work (but not focused on a specific medical task)

18. SLIMPad demo

19. Superimposed Layer Information Manager (SLIM) Architecture:Contributions • Mark Management - to create/resolve marks • SLIM API - for the application developer • TRIM store - for generic storage of superimposed information

22. SLIM API: as seen by application

23. What’s Next for this Project? • Validation - cardiologists, ICU nurses, … • Extend the informational model of SLIMPad • Extend SLIMPad to suit a selected medical task • Extension of observational work to other domains

24. www.cse.ogi.edu/footprints • demos - including the QTVR of the ICU (with toys) and SLIMPad • personnel • project description • papers • “Bundles in the Wild: Tools for Managing Information to Maintain Situation Awareness” • “Bundles in Captivity: An Application of Superimposed Information” • papers discussing superimposed information

25. Outline • introduction to superimposed information • a superimposed application: SLIMPad (DLI2 Project) • model-based representation and transformation of information • harvesting information to sustain our forests (NSF Digital Government project)

26. Model-Based Superimposed Information Model Superimposed Schema Data Layer Instance Data with Marks marks marks Base Information Information Layer Source Source 1 2 But the model and schema are optional

27. Our Goals • Represent information generically, for various models • Convert information from one representation scheme to another

28. Influenced by Generic Rep. (Topic Map model) by painter TM Browser Painting Painter mentioned critiqued mentioned biography convert XML Viewer XML SQL DB Transforming Information Generic Rep. (XML model) convert Generic Rep. (Relational model)

29. Our Approach • Metamodel • to represent multiple data models • Generic, Uniform Representation Scheme • to store model, schema, and instances for model-based information • Mapping Formalism • to transform between representation schemes

30. The Metamodel • Provides a level of abstraction above models • Describes the structural features of models Basic Set of Abstractions Metamodel Model Constructs and Relationships Topic Map XML Topic Map Defintions Schema-Level Data DTD Topic Map Instances XML Document Instance-Level Data

31. XML Model, Schema, and Instance Model constructs and relationships defined using the metamodel • Elements, Element Types, Attributes, Attribute Types • Elements contain Attributes • Elements can be nested XML Model <!ELEMENT schedule (flight*)> <!ELEMENT flight (from, to, price)> <!ATTLIST flight name CDATA #REQUIRED> XML DTD (Schema) <schedule> <flight name=“Air Canada Flight 1575”> <from> PDX </from> <to> YVR </to> <price> $213.84 </price> </flight> ... </schedule> XML Document (Instances)

32. Topic Map Example Influenced by by painter Painting Painter critiqued mentioned mentioned biography influenced by by painter “Captive” “Paul Klee” critiqued biography biography “Francisco de Goya” http://... http://... http://... mentioned mentioned http://... “1914” by painter http://...

33. Topic Map Model in UML <<conformance>> topic_instOf TopicType TopicInstance 1 1 * 1 ttypename : String title : String topicInsID : Number 1 1 topic Type1 topic Type2 1 1 topicType topic Ins1 topic Ins2 topicIns * * TopicRelType <<conformance>> rel_instOf * * 1 * relType : String TopicRelInst <<conformance>> anchor_instOf AnchorType AnchorInst * 1 * * anchorRole : String * address 1 <<Mark>> Address markID : String

34. Generic, Uniform Representation • We use RDF and RDF Schema to represent model, schema, and instance uniformly RDF Triples RDF Graph (creator, ‘http://…/~john’, person1) (name, ‘person1’, ‘John Smith’) creator name http://…/~john person1 ‘John Smith’ RDF Schema Triples RDF Schema Graph Property (type, ‘creator’, Property) (domain, ‘creator’, WebPage) (range, ‘creator’, Person) (type, ‘Person’, Class) (type, ‘WebPage’, Class) type Class WebPage domain type type Person creator range

35. The Metamodel Definition connects 2 constructs Structural Connector Basic Metamodel Elements Construct • Construct: A basic structural unit Mark: A connection-point to the base-layer Lexical: A primitive-value type • Connector: A relationship between 2 constructs Conformance: A schema-instance relationship Generalization: An inheritance relationship Special Elements Mark Lexical Conformance Generalization

36. Representing Models (instanceOf, “TopicType”, Construct) (instanceOf, “TopicInstance”, Construct) (instanceOf, “topic_instOf”, Conformance) (domain, “topic_instOf”, TopicInstance) (range, “topic_instOf”, TopicType) (domainMult, “topic_instOf”, “*”) (rangeMult, “topic_instOf”, “1”) (instanceOf, “ttypename”, Connector) (domain, “ttypename”, TopicType) (range, “ttypename”, String) (domainMult, “ttypename”, “*”) (rangeMult, “ttypename”, “1”) TopicType ttypename : String 1 <<conformance>> topic_instOf * TopicInstance

37. Representing Schema Topic Types (schema): painting, painter (instanceOf, “painting_tt”, TopicType) (ttypename, “painting_tt”, “painting”) (instanceOf, “painter_tt”, TopicType) (ttypename, “painter_tt”, “painter”) (instanceOf, “byPainter_rt”, TopicRelType) (relType, “byPainter_rt”, “by painter”) (topicType1, “byPainter_rt”, painting_tt) (topicType2, “byPainter_rt”, painter_tt) (instanceOf, “biography_at”, AnchorType) (anchorRole, “biography_at”, “biography”) (topicType, “biography_at”, painter_tt) Topic Rel Types (schema): by painter Anchor Types (schema): biography by painter painting painter biography

38. Representing Instances (instanceOf, “painter1”, TopicInstance) (title, “painter1”, “Paul Klee”) (topicInsID, “painter1”, “5”) (topic_instOf, “painter1”, painter_tt) (instanceOf, “painting1”, TopicInstance) (title, “painting1”, “Captive”) (topicInsID, “painting1”, “19”) (topic_instOf, “painting1”, painting_tt) (instanceOf, “byPainter1”, TopicRelInst) (rel_instOf, “byPainter1”, byPainter_rt) (topicIns1, “byPainter1”, painting1) (topicIns2, “byPainter1”, painter1) (instanceOf, “biography1”, AnchorInst) (anchor_instOf, “biography1”, biography_at) (address, “biography1”, a1) (instanceOf, “a1”, Address) (markID, “a1”, “URLMarkManager@954308545”) Topic (instances): Paul Klee, Captive Topic Relationship (instance): a by painter relationship Anchor (instance): a biography anchor Address (instance): mark to URL

39. Basic Types of Mappings Mapped Model1 Model2 Converted Schema1 Schema1 Inter-Model Converted Instances1 Instances1 Model1 Model1 Mapped Inter-Schema Schema1 Schema2 Converted Instances1 Instances1 Mapped Model1 Model2 Model-to-Schema Schema1 Schema2 Converted Instances1 Instances2 Converted

40. Mapping Rules Simple production rules over triples Mapped XMLElem TopicInstance S(‘source’, (‘instanceOf’, X, ‘TopicInstance’))  S(‘target’, (‘instanceOf’, X, ‘XMLElem’))

41. Mapping Rules (cont.) TopicInstance XMLElem Mapped topic_instOf elem_instOf TopicType XMLElemType S(‘source’, (‘topic_instOf’, X, Y)) S(‘target’, (‘instanceOf’, X, ‘XMLElem’)) S(‘target’, (‘instanceOf’, Y, ‘XMLElemType’))  S(‘target’, (‘elem_instOf’, X, Y))

43. Applications • SLIM Pad • Scratchpad application with Bundle-Scrap model (uses superimposed information) • XML Extractor • “Extracts” XML information and transforms it into a Topic Map for searching/browsing in out mapped DBMS XML Extractor stored XML Files Generic Rep. (XML model) Generic Rep. (TM model) Topic Map Browser

44. IDMEF to CISL • IDMEF - Intrusion Detection

45. Harvesting Information to Sustain our Forests: Creating an Adaptive Management Portal NSF DIGITAL GOVERNMENT PROGRAM Tim Tolle & Lois Delcambre ttolle@fs.fed.us lmd@cse.ogi.edu Co-Project Directors

46. Project focuses on the: Adaptive Management Areas USDA Forest Service USDI Bureau of Land Management USDI Fish and Wildlife Service

47. Adaptive Management Portal:a value-added, Internet-based service • Provide multiple access paths to forest information. • Preserve local autonomy and local focus of each site. • Support diverse users and types of information. • Use proposed, existing, and de facto standards for content, classification, and technology. • Be low-cost, scalable, extensible.

48. Project Funding • Duration: 3 years • Budget: $1.5 million • Principal financial sponsors • National Science Foundation • Bureau of Land Management (Oregon State Office) • Forest Service (R-6 and PNW Station) • National Park Service (Western Region)

49. Team Members Tim Tolle Regional Coordinator for AMA, US Forest Service Eric Landis Forest Information System Specialist, Consultant Craig Palmer Natural Resources Monitoring Expert, UNLV Fred Phillips Professor, Head, Mgt. of Science and Tech., OGI Patty Toccalino Asst. Prof., Environmental Science and Eng., OGI Lois Delcambre Professor, Computer Science and Eng., OGI David Maier Shawn Bowers Mat Weaver Professor, Computer Science and Eng., OGI PhD Student, Computer Science and Eng., OGI PhD Student, Computer Science and Eng., OGI Forest/environmental expertise Computer science expertise

50. Advisory Board Co-Inventor of the Topic Map Model Michel Biezunski President, IUFRO, Oxford Forestry Institute, Dept of Plant Sciences Jeff Burley USDA Forest Service, Pacific NW Region Robert Devlin Martin Goebel Sustainable Northwest MD, Asst. Professor, Division of Medical Informatics and Outcomes Research, OHSU Paul Gorman Executive Director, IMFN Secretariat Fred Johnson Chief, Office of Technical Support, Forest Resources, USDI Fish and Wildlife Service Monty Knudsen Communications Director, USDA Forest Service, PNW Research Station Cynthia L. Miner Science Advisor, USDI, National Park Service Regina Rochefort Staff Scientist, Pacific Northwest National Laboratory Mark Whiting Forest/environmental expertise Computer science expertise