Federal Government IT Strategy

1. Federal Government IT Strategy Michael Lang January 8, 2007

2. Background • I founded Metamatrix eight years ago • The federal government became our largest customer by accident • I have worked with dozens of federal IT programs and with dozens of integrators • Mostly interested in information management and systems architecture • Now concentrating on semantic technology

3. Agenda • Federal IT Overview • Federal Enterprise Architecture • Net Centric Enterprise Services • Communities of Interest • Domain Vocabularies • Semantic Technology

4. Federal IT Investment • Your Federal Government is doing billions of dollars of R&D in the IT area • There are hundreds of IT programs • Orion – NASA • Trailblazer, Groundbreaker – Ft Meade • TTIC, US Visit – DHS • Sentinel, NDEX, RDEX – DOJ • DLA IDE, GCSS, GCCS - DOD

5. Federal IT Communities • There are three distinct communities in the Federal IT space • Intelligence • Looks a little like financial service firms • Department of Defense • Looks most like commercial enterprises • Civilian • All three have very different use cases and agendas

6. Intelligence • Pre 9-11 systems were all secure silos • Sharing was avoided • Security was paramount • A lot of custom code • Fair mix of structured and unstructured information • Use case is “analysis”

7. Intelligence • An Executive Order mandating information sharing across the intelligence community was issued right after 9-11. • Information sharing is now paramount • Metadata management is key • Logical data models for each domain • Data is being exposed as services • Progress is very slow because of security concerns

8. Department of Defense • Mission changed with the collapse of the Soviet Union and the arrival of Don Rumsfeld • Much nimbler warfighter • Smaller missions, faster response • Requires better co-ordination between military branches and commands • Largely client server • Mostly structured information

9. Department of Defense • Move to SOA is well under way • Data being exposed as services • Registries and repositories proliferate • Many domain data models • Many, many efforts under way to achieve greater degrees of interoperability • Throw spaghetti at the wall and see what sticks

10. Civilian • Mission changed with the arrival of the Internet • Executive order creates eGov initiative • Citizen centric services • No sense of urgency here • Relatively small budgets

11. FEA and NCES Federal Enterprise Architecture And Net Centric Enterprise Services

12. Overarching Programs • There are two long running, overarching IT initiatives whose goal is to re-engineer the federal government IT infrastructure • FEA, Federal Enterprise Architecture • Managed by OMB • Top down • NCES, Net Centric Enterprise Services • Managed by DOD, DISA • Bottom up

13. FEA • This program began in 2002 as a result of an executive order from the White House that created the eGov initiative • http://www.whitehouse.gov/omb/egov/ • “To transform the Federal government to one that is citizen-centered, results-oriented, and market-based, the Office of Management and Budget (OMB) is developing the Federal Enterprise Architecture (FEA), a business-based framework for government-wide improvement.”

14. Architecture Principles FEAPMO • Motherhood and Apple Pie • The federal government focuses on citizens • The federal government is a single, unified enterprise • Federal agencies collaborate with other governments and people • Information is a national asset • The federal architecture is mission-driven • Security, privacy and protecting information are core government needs • The federal architecture simplifies government operations

15. FEA Reference Models

16. FEA Current State • Even though there are budgetary enforcement procedures mandating agencies to begin implementation of the FEA, they are largely ignored • The root of the problem is that the architecture does not hang together and the prospective users know it • The DRM is not credible

17. Data Reference Model • I spent two years working on the DRM, it is the most troublesome layer of the stack • The DRM provides a standard means by which data may be described, categorized, and shared. These are reflected within each of the DRM’s three standardization areas: • Data Description: Provides a means to uniformly describe data, thereby supporting its discovery and sharing • Data Context: Facilitates discovery of data through an approach to the categorization of data according to taxonomies; additionally, enables the definition of authoritative data assets within a community of interest (COI) • Data Sharing: Supports the access and exchange of data where access consists of ad-hoc requests (such as a query of a data asset), and exchange consists of fixed, re-occurring transactions between parties

18. NCES • Net Centric Enterprise Services • NCES started at about the same time as FEA, but is an initiative out of DISA (Defense Information Systems Agency) the CTO office of DOD. • NCES does not pay much attention to FEA • Global Information Grid – GIG • Includes the physical networks and other hardware

19. NCES Mission • NCES will enable the secure, agile, robust, dependable, interoperable data-sharing environment for DOD where warfighter, business, and intelligence users share knowledge on a global network. This, in turn, facilitates information superiority, accelerates decision-making, effective operations and net-centric transformation. • To enable successful conduct of warfare and other operations in the Information Age. • Make information available on a network that people can depend upon and trust. • Populate the DOD networks with new, dynamic sources of information to defeat the enemy. • Sounds a lot like any commercial enterprise mission statement

20. NCES Mission • NCES represents a different approach to building and fielding DOD Information Systems • Market-based approach, recognizing that a user's information technology (IT) needs are dynamic and are rarely satisfied by systems built with a set of pre-determined user needs • Users themselves are best able to define their requirements • The NCES approach is DOD-wide • It offers unprecedented access to information from global sources while leveraging existing IT investments

21. NCES Current State • Service Oriented Architecture • A lot of the infrastructure is in place • Metadata catalogs/repositories • Services Registry • Tools for converting relational to XML • Tools for creating and publishing services • XML Schemas describing domains • Quality of service software • Security software and hardware • Governance

22. NCES Current Bottleneck • Interoperability • As soon as the number of services proliferate • The number of silos proliferate • They are more granular but still hard to use and manage • Pulled a lot of the funding from programs that are creating “services” • Funding a lot of pilot projects to solve interoperability

23. Domain Vocabularies • Early efforts used XML Schema and ER diagrams to define the domain “data model” • Global Justice XSD • National Information Exchange Model – NIEM • Command and Control – C2IEDM • Not extensible, not semantic • No connection between the businessperson and the data

24. Communities of Interest • Communities of Interest form to create domain vocabularies • All of the terms in a domain • Data dictionary, logical model, schema • What they mean • How they are used • How they are related • The Domain vocabulary is the interoperability master key • All data elements in all systems are mapped to terms in the domain vocabularies

25. Use of Vocabularies • Permit humans express their concepts in a machine readable language • Enable machines to perform the data translation and transformation required by data integration • Vocabularies are the essential underpins to sharing data or system interoperability that requires “dynamic links” among unknown, unlimited numbers of data sources • Essential to all semantic technologies, including semantic search

26. Semantics • Most programs have moved to OWL for defining domain vocabularies • http://www.opengroup.org/projects/soa-ontology/ • http://osera.gov/web/guest/projects/fea-rmo • Flexible and extensible • Naturally distributed, URI and URLs • Best design-time metadata representation model • Machine readable at runtime • Functions at the scale of the WWW

27. Semantic Technology Standards OWLOntology W3C Semantic Technology Standards

28. Term 1 4 4 Graph-Based Approach 2 2 Term Semantic “cluster” 3 3 Solving Data Relationships (Related) • Why Ontologies are so important • “An ontology is an abstract representation of concepts and their relationships that enables deductive and inferential reasoning upon itself.” • They are uniquely capable of creating relationships, otherwise impossible to identify on a mass scale, that explicitly reason for all relationships.

29. MBI’s SOA-Enabled DoDIIS Data Layer • Use Ontology to semantically match elements across disparate sources • Build virtual layer • Service enable data layer

30. Government Leads the Way • Semantic technology • The government last led the charge with relational database technology and IP networks • DARPA funded the R&D for RDBMS for 10 years • And then became the early adopter • DARPA created OWL (DAML+OIL) eight years ago • Numerous projects funded to employ semantic technology • Just making it into operational systems

31. Conclusions • Bottom up architectural approach works better than top down • Communities will form and participate in the construction of the system especially the domain vocabularies • The effort should and can include business people, technology people and data people

32. Conclusions • For transactional systems, data is being represented by XML and exposed as services (WSDL) in an SOA • Domain vocabulary is being described in OWL • Interoperability • For analysis, data is being represented as RDF and queried using SPARQL • The ontology is the integration layer

33. Thank You Michael Lang michaelalang@gmail.com

34. Web Services Web Services Discovering and Binding Services You can haveone or moreof these Mapping Vocabularies A & B Mapping Vocabulary “same as” or “same class as” Vocabulary A Vocabulary B generate describe the RDF generate describe the RDF XML Messages (in RDF XML) XSD XSD XML Messages (in RDF XML) Describe the structure (elements & attributes) Describe the structure (elements & attributes) reference reference WSDL WSDL describe describe

35. combine extract extract extract Web Services XML Messages (in RDF XML) RDF Content RDF Contentfrom allResponses RDF Content combine combine RDF Content Using Service Responses KNOWN FACTS

36. “Semantic Interpreter” or “Semantic Message Translator” Small wrapper around Jena Vocabularies (OWL) Composed at design-time submit produce QUERY (SPARQL) KNOWN FACTS NEXT SERVICE REQUEST MESSAGE Designed to obtaindesired messagefor next service call Composed from previousmessages in a SOAtransaction plus assertions(facts) obtained fromother sources

37. Single Vocabulary/Dictionary Composite(s) Unit Identification Code Nationality Fields Armed Service Sequential Location Number Valid Entries Nationality:string(2) - enumeration value="AF" - enumeration value="AL" - enumeration value="AG" - enumeration value="AQ" - enumeration value="AN" … Armed Service:string(1) - enumeration value="F"/> - enumeration value="A"/> - enumeration value="C"/> - enumeration value="B"/> - enumeration value="J"/> … Sequential Location Number:integer - min value="0000" - max value="999999" - pattern value="[0-9]{4,6}" + Other Metadata

38. T T T Enterprise Vocabulary/Dictionary Enterprise Vocabulary Fields Composites Valid Entries A USMTF Vocabulary Link 16 Vocabulary VMF Vocabulary B D A Fields Composites Valid Entries Fields Composites Valid Entries Fields Composites Valid Entries

39. Logical (Relationship) View • Reference Model for naming conventions, data-typing conventions, and business component structure • Purely Conceptual -- Represents abstract view of data relationships within a vocabulary (cannot be queried from data) • Improves ability to manage change and support new virtual models more quickly

40. Info Exchanges/Use Cases Enterprise Vocabulary Harmonized Standard Views A Sets Messages Web Services Fields Composites Valid Entries USMTF Vocabulary Link 16 Vocabulary VMF Vocabulary Community Specific B D A Fields Composites Valid Entries Fields Composites Valid Entries Fields Composites Valid Entries Specific Information Exchanges (Messages/Virtual Models)