Superimposed Information - Stanford DB talk1 Technology for Superimposed Information Lois Delcambre with Shawn Bowers, David Maier, Mat Weaver Database and Object Technology Lab Computer Science and Engineering Department Oregon Graduate Institute

Superimposed Information - Stanford DB talk2 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)

Superimposed Information - Stanford DB talk3 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

Superimposed Information - Stanford DB talk4 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

Superimposed Information - Stanford DB talk5 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

Superimposed Information - Stanford DB talk6 The superimposed and base layers with marks Superimposed Layer Base Layer Information Source 1 Information Source 2 Information Source n … marks

Superimposed Information - Stanford DB talk7 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)

Superimposed Information - Stanford DB talk8 Paul Gorman, MD Lois Delcambre, PhD David Maier, PhD

Superimposed Information - Stanford DB talk9 Bundles in the wild……….. Observational team: Paul Gorman Joan Ash Mary Lavelle Jason Lyman …………..Bundles in captivity Computer science team: Lois Delcambre Dave Maier Shawn Bowers Longxing Deng Mathew Weaver

Superimposed Information - Stanford DB talk10 Let’s take a trip to the ICU

Superimposed Information - Stanford DB talk11 (Wild) Bundles

Superimposed Information - Stanford DB talk12 (Wild) Bundles

Superimposed Information - Stanford DB talk13 (Wild) Bundles

Superimposed Information - Stanford DB talk14 (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

Superimposed Information - Stanford DB talk15 (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)

Superimposed Information - Stanford DB talk16 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

Superimposed Information - Stanford DB talk17 (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)

Superimposed Information - Stanford DB talk18 SLIMPad demo

Superimposed Information - Stanford DB talk19 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

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Superimposed Information - Stanford DB talk22 SLIM API: as seen by application

Superimposed Information - Stanford DB talk23 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

Superimposed Information - Stanford DB talk24 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

Superimposed Information - Stanford DB talk25 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)

Superimposed Information - Stanford DB talk26 Model Schema Data Instance Data with Marks Information Source 1 Information Source 2 Superimposed Layer Base Layer marks Model-Based Superimposed Information But the model and schema are optional

Superimposed Information - Stanford DB talk27 Our Goals Represent information generically, for various models Convert information from one representation scheme to another

Superimposed Information - Stanford DB talk28 Transforming Information Generic Rep. (XML model) Generic Rep. (XML model) convert Generic Rep. (Topic Map model) XML DB XML Viewer SQL TM Browser Painting Painter by painter Influenced by mentionedbiographymentionedcritiqued convert Generic Rep. (Relational model)

Superimposed Information - Stanford DB talk29 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

Superimposed Information - Stanford DB talk30 The Metamodel Provides a level of abstraction above models Describes the structural features of models Topic Map Topic Map Defintions Topic Map Instances XML DTD XML Document Basic Set of Abstractions Model Constructs and Relationships Schema-Level Data Instance-Level Data Metamodel

Superimposed Information - Stanford DB talk31 XML Model, Schema, and Instance Elements, Element Types, Attributes, Attribute Types Elements contain Attributes Elements can be nested PDX YVR $ XML Model XML DTD (Schema) XML Document (Instances) Model constructs and relationships defined using the metamodel

Superimposed Information - Stanford DB talk32 Topic Map Example Painting Painter by painter Influenced by “Captive” “Paul Klee” by painter influenced by “Francisco de Goya” “1914” by painter mentioned biography mentioned biography critiqued mentioned

Superimposed Information - Stanford DB talk33 Topic Map Model in UML TopicType ttypename : String TopicRelType relType : String AnchorType anchorRole : String TopicInstance title : String topicInsID : Number TopicRelInst AnchorInst > Address markID : String * * * * * * > topic_instOf > rel_instOf > anchor_instOf address topicIns topicType 11 ** topic Type1 topic Type2 11 ** topic Ins1 topic Ins2

Superimposed Information - Stanford DB talk34 Generic, Uniform Representation We use RDF and RDF Schema to represent model, schema, and instance uniformly creator (creator, ‘ person1) (name, ‘person1’, ‘John Smith’) Class Property creator type Person WebPage type domain range (type, ‘creator’, Property) (domain, ‘creator’, WebPage) (range, ‘creator’, Person) (type, ‘Person’, Class) (type, ‘WebPage’, Class) person1 ‘John Smith’ name RDF Triples RDF Graph RDF Schema Triples RDF Schema Graph

Superimposed Information - Stanford DB talk35 The Metamodel Definition Construct Structural Connector MarkLexicalConformanceGeneralization connects 2 constructs Basic Metamodel Elements Special Elements  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

Superimposed Information - Stanford DB talk36 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 TopicInstance * 1 > topic_instOf

Superimposed Information - Stanford DB talk37 Representing Schema (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 Types (schema): painting, painter Topic Rel Types (schema): by painter Anchor Types (schema): biography painting painter by painter biography

Superimposed Information - Stanford DB talk38 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”, Topic (instances): Paul Klee, Captive Topic Relationship (instance): a by painter relationship Anchor (instance): a biography anchor Address (instance): mark to URL

Superimposed Information - Stanford DB talk39 Basic Types of Mappings Mapped Converted Inter-Model Inter-Schema Model-to-Schema Model 2 Schema 1 Instances 1 Model 1 Schema 1 Instances 1 Model 1 Schema 1 Instances 1 Model 1 Schema 1 Instances 1 Model 1 Schema 2 Instances 1 Model 2 Schema 2 Instances 2 Mapped

Superimposed Information - Stanford DB talk40 S(‘source’,  (‘instanceOf’, X, ‘TopicInstance’))  S(‘target’,  (‘instanceOf’, X, ‘XMLElem’)) XMLElem TopicInstance Mapped Mapping Rules Simple production rules over triples

Superimposed Information - Stanford DB talk41 Mapping Rules (cont.) XMLElem TopicInstance XMLElemType TopicType Mapped elem_instOf topic_instOf S(‘source’,  (‘topic_instOf’, X, Y)) S(‘target’,  (‘instanceOf’, X, ‘XMLElem’)) S(‘target’,  (‘instanceOf’, Y, ‘XMLElemType’))  S(‘target’,  (‘elem_instOf’, X, Y))

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Superimposed Information - Stanford DB talk43 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 XML Files Generic Rep. (XML model) Generic Rep. (TM model) DBMS Topic Map Browser XML Extractor XML Extractor outmapped stored in

Superimposed Information - Stanford DB talk44 IDMEF to CISL IDMEF - Intrusion Detection

Superimposed Information - Stanford DB talk45 Harvesting Information to Sustain our Forests: Creating an Adaptive Management Portal NSF DIGITAL GOVERNMENT PROGRAM Tim Tolle & Lois Delcambre Co-Project Directors

Superimposed Information - Stanford DB talk46 Project focuses on the: Adaptive Management Areas USDA Forest Service USDI Bureau of Land Management USDI Fish and Wildlife Service

Superimposed Information - Stanford DB talk47 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.

Superimposed Information - Stanford DB talk48 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)

Superimposed Information - Stanford DB talk49 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 Professor, Computer Science and Eng., OGI Shawn Bowers PhD Student, Computer Science and Eng., OGI Mat Weaver PhD Student, Computer Science and Eng., OGI Forest/environmental expertise Computer science expertise

Superimposed Information - Stanford DB talk50 Staff Scientist, Pacific Northwest National Laboratory Mark Whiting Science Advisor, USDI, National Park Service Regina Rochefort Communications Director, USDA Forest Service, PNW Research Station Cynthia L. Miner Chief, Office of Technical Support, Forest Resources, USDI Fish and Wildlife Service Monty Knudsen Executive Director, IMFN Secretariat Fred Johnson MD, Asst. Professor, Division of Medical Informatics and Outcomes Research, OHSU Paul Gorman Sustainable Northwest Martin Goebel USDA Forest Service, Pacific NW Region Robert Devlin President, IUFRO, Oxford Forestry Institute, Dept of Plant Sciences Jeff Burley Co-Inventor of the Topic Map Model Michel Biezunski Advisory Board Forest/environmental expertise Computer science expertise

Superimposed Information - Stanford DB talk51 Task 1 – Status Snoqualmie Pass Adaptive Management Area, Cle Elum, WA (June and July) Interviews with Forest Service Corvallis Forest Sciences Lab and USGS FRESC, Corvallis ( August) Interviews with Central Cascades Adaptive Management Area, Eugene (August) Interviews with the Applegate Partnership and its associated agencies (August) Rainier National Park (planned for October)

Superimposed Information - Stanford DB talk52 Things we’ve learned from Task 1 NSF Digital Government work is project-based primary product is information: assessments, studies, surveys, environmental impact statements multiple agencies are involved each agency serves as information gatherer; information broker; information consumer even though information is a primary product, information technology is secondary (stewardship of the land is the primary mission)

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Superimposed Information - Stanford DB talk54 Research Issues Models for the superimposed layer How does the superimposed model influence the capabilities it supports? How does the form of superimposed information affect the effort to construct and maintain it? –Are some forms more robust to updates in the base layer –What forms map onto current information management tools

Superimposed Information - Stanford DB talk55 Research Issues (2) Challenges when superimposed and base layer have different models –E.g., structured over unstructured, or vice versa Bi-level tools –Browsing between layers –Queries over both layers How do we delimit the universe of discourse in the base layer? Is it easier to fuse superimposed information than base information?

Superimposed Information - Stanford DB talk56 Research Issues (3) Variations on the conceptual architecture –Commingled layers –“Super-superimposed information” How do capabilities of base layer affect structure and operations over superimposed information? –Addressing modes –Address comparison –Querying Addressing for non-web sources –Relational, object-oriented DBs

Superimposed Information - Stanford DB talk57 Research Issues (4) How to extend DBMSs to better deal with information they don’t store. How to help population superimposed information spaces. What are good formats for representation and exchange of superimposed information?

Superimposed Information - Stanford DB talk58 Why Databases Don’t (Currently) Solve It Seems closely related to view and data integration However –Superimposed information can’t always be derived from the base data –DB approaches assume schema and common model –DBs like to work with data they control –Traditional approaches are heavy weight semantic analysis schema integration query mapping On a source-by-source basis