Achieving Information Sharing in Federal Agencies via Data Services, SOA, and Controlled Vocabularies A Presentation for the Federal Data Architecture.

Achieving Information Sharing in Federal Agencies via Data Services, SOA, and Controlled Vocabularies A Presentation for the Federal Data Architecture Subcommittee Chuck Mosher metamatrix.com October 12, 2006

Agenda Company Overview & Value Proposition
Data Services Rationale & Best Practices MetaMatrix Products & Capabilities Achieving Information Sharing Service Enabling Data Assets Vocabularies & Semantic Interoperability Bridging Structured/Unstructured Information Customer Use Cases Summary, Q & A

MetaMatrix Company Overview Uniform access to integrated information
Vision – Universal bridge between information-consuming applications and enterprise information resources. Products – Lightweight design/deploy environment for project use. Enterprise-caliber information access system for enterprise deployments. Market – Global 5000 Organizations Government Intelligence Agencies Homeland Security Financial Services Pharmaceutical, Life Sciences Manufacturing, Telecommunications Independent Software Companies (ISVs)

Data Interoperability Is At The Very Core of The Transformation Sought by the Federal Government
One of the three enablers which drives domain-wide visibility: “… is a standard enterprise data architecture — the foundation for effective and rapid data transfer and the fundamental building block to enable a common logistical picture.” Army Lt. Gen. Claude Christianson “If you look at all the trends in the IT arena over the past 30 to 40 years, we’ve moved into an environment where we’ve got faster networks, more powerful processors, but it really comes down to the data” Michael Todd, DOD CIO office

Dr. Linton Wells, as quoted in September’s NDIA Magazine, “…data compatibility may be an issue. Enabling digital interaction with nontraditional partners may require middleware or other programs that convert data from totally different formats …”

NCES & Data Net-Centricity
“To-be” = SOA Stack “As-is” = Application Silos Application Application Server … Server DBMS DBMS XML-centric Information Abstraction (= Data Services) How do you achieve? Loose coupling Map existing data to XML Multi-source requests Metadata visibility Information security Service access Service discovery

The Data Challenges Resolving data semantic and structural mismatches
Getting the right information to the right person at the right time requires: Resolving data semantic and structural mismatches Web service enabling legacy data systems (i.e., Net Centricity) Mapping data sources to vocabularies like C2IEDM, NIEM, GJXDM, TWPDES, etc…. Handling multi-source requests (data aggregation, mediation, fusion, federation) Minimizing development and maintenance cost of custom code

MetaMatrix – Quick Facts
Middle-ware, model-driven, data management DoD proven (DISA, NSA, TRANSCOM, etc.) Version 5 – Mature product which is still unique and ahead of the competition NIAP certified and NSA-credentialed Can handle the enterprise (or COI) perspective as well as the bottom-up perspective (data service enablement of legacy systems) Can rapidly implement data integration strategies

Some Key Value Propositions
Lower cost of new application development by 35 to 70% Data interoperability is accomplished using COTS vs code Reduce application maintenance costs Enable detection of changes in data structures No re-coding needed when data structures change Fewer systems to maintain Avoid the need for replication (OHIO) Data owners keep control, managed access Data abstractions are reusable components that generate tremendous value over time. More adaptive computing

Information Challenges
Communities of Interest Agency Challenges 100’s/1000’s of data sources 100’s/1000’s of applications Multiple access points/modes for apps Understanding relationships/semantics Data consistency Data reuse – bridging data silos Support for Web Services & SQL Control & manageability, compliance Security & auditing ? Information Resources Program Challenges Multiple sources Different interfaces/drivers Different physical structures Different semantics Single interface to data desired Real-time access to data Performance Maintainability as data changes Maintainability as apps change How are you going to complete the project on time without coding data integration? How are you going to perform transformation for complex schemas with thousands of elements? How are you going to integrate many different data sources into the application? How are you going to maintain all of the schema changes in the future? Mission Challenges Time-to-deploy Agility - Responsiveness to change Automation – Reduce cost of new development and operations ROI of enterprise information

Information Virtualization
Communities of Interest Information Virtualization Layer Information Resources

Information Virtualization
Information Virtualization Layer Unified Semantic Layer Unification of different concepts across systems Data Federation Layer Single-query access to heterogeneous systems Data Access/Connectivity Layer Uniform, standardized access to any system Enterprise Data Sources

What is a Data Service? Decouple data sources from application
Data implementation shielded from application Semantic/Format Mediation Standard vocabulary Single access point Web Service/XML SQL Federation Single source or multi-source Scalability Security, performance XML/SOAP SQL Bridge the Gap Data Service SQL SQL API Call Let’s further define what we mean by data services - decoupling of applications from data sources provides a much-needed abstraction layer that helps insulate systems from change - many times the decoupling requires that the difference in formats and semantics of multiple data sources must be mediated - attributes of a good solution - metadata – needs to be managed and made discoverable, - performance, security – goes without saying, doesn’t always get the attention needed until later stages Master Data Operational Data Store Agency Application

FEA DRM View on Data Services
DRM Version 2 Data Access Services Context Awareness Services Structural Awareness Services Transactional Services Data Query Services Content Search and Discovery Services Retrieval Services Subscription Services Notification Services Service Types include: Metadata / Data Structured / Unstructured Read / Write Push / Pull

Data Service Layer in SOA
Client Process & Applications App App App App App App Business Process Services Business Services Message Services (ESB) A set of data services provides the foundation layer within an SOA. Data services don’t include the business logic, or business services Takes care of access and mediation Works with an ESB to get the data into the right form Data Service Data Service Data Service Data Service Data Service Data Services Layer Data Sources

Data Services: Architecture for the Ages
Data Services Best Practices Provide transparency across all sources Define known relationships today and accommodate future relationships Support independence of mission systems Support ownership of operational data sources at the source Provide accelerated mechanisms for integrating new sources Support existing security policy and add degrees of security The value of a managed metadata abstraction layer "Future Proofing" (future standards, exchange models, platforms) Limited skill set requirements Fixed long term costs for integration middleware Building consensus Assure data owners they will continue to have control, and … Vocabulary of existing production systems will not be impacted Offer an option where legacy data migration is not 'required' 1st

Data Services Approaches
Data Services for Multiple Purposes: Simplified access to value-added (tagged) data in real-time Value-added (tagged) data materialized & staged Phased-in migration from legacy to new Managed archiving via classification, retention tags Enhanced search via consistent content tags Agile Information Services Model-Driven Integration Layer <X> </X> <X> </X> Logical Data Model Logical Data Model T Org, Person, Image, Location <X> </X> <X> </X> T Organization, Customer, Imagery, Location Materialized Logical Model Materialized Logical Model Data, Content Sources Data, Content Sources <X> </X> <X> </X> <X> </X> Enriched Data/Content Store

Information Exchange Topology
Master Data Person / Facility / Vehicle Search Engine Index / Metadata Catalog Ontology Mgmt / Reasoning Mediation XSLT, Multi-source Enterprise Service Bus / Intranet / Extranet State/Local Orchestration Encryption High Availability Security/Authentication Operations Management Error / Exception Management Data Access Services SQL, Web Service/XML Staged Data (optional) Distributed Data Services Enterprise Data Services Stage SOA App’s Land/Sea Federal Agencies

MetaMatrix I.P. MetaMatrix has 2 distinct innovations that work in concert to yield significant business benefits: Model-based Extensible Sharable, reusable Standards-based Information Modeling Cost-based optimizer Read/write/transactions Uniform API, any source Battle-tested/hardened Federated Querying

MetaMatrix Enterprise Data Services
Problem: data sources throughout the enterprise, vs. the need to use these data sources for purposes other than for what they were intended Solution: data services – abstraction/decoupling of the data sources from the consumers of the data Features: each of the bullet points on the slide, some more relevant than others, depending on the audience Scope: solution can scale to enterprise deployment where appropriate; many customers start by deploying data services that are more project-specific Project-level or Enterprise-wide data services layer Integrated views of data from multiple sources Metadata-driven Optimized performance Interoperable security Complements BI, ETL, ESB/EAI, DQ, CDI, Search

Modeling Instead of Coding
Designing data services Information Consumers Exposed Data Services Reusable, Integrated Data Objects Enterprise Information Sources (EIS) Web Services, Business Processes <WSDL> (contract) services SOAP <WSDL> (contract) warehouses EAI, Data warehouses databases <WSDL> (contract) Packaged Apps Logistics spreadsheets Re-useable data services Enterprise-wide data abstraction layer Integrated views of data from multiple sources Metadata-driven Optimized performance Interoperable security Complements other tools (ETL, EAI, ESB, DQ, BI) <sale/> <value/> </ sale > xml Custom Apps ODBC geo-spatial Reporting, Analytics JDBC Intelligence rich media …

Physical Models Representing
MetaMatrix Designer Physical Models Representing Actual Data Sources Virtual Models Shows structural transformations from one or more other classifiers Defines transformations with Selects Joins Criteria Functions Unions User Defined Data Service Abstraction Layers: Broker, translate, aggregate, fuse or integrate data.

Information Consumers MetaMatrix Connector Framework
MetaMatrix Products Information Consumers MetaMatrix Server MetaMatrix Designer - Design and deploy data services JMS ODBC JDBC SOAP MetaMatrix Integration Server Integrated Security Users Roles Entitle ments Access Models Views XML Docs <a> </a> <b> </b> … Services in out proc Integration Server Virtual Data Bases VDB Query Processor Optimizer 3 core components Designer Repository/Catalog MetaMatrix Server – engine where Data Services get deployed and executed MetaMatrix Connector Framework Packaged Connectors Web Svc XML RDBMS MetaMatrix Catalog Siebel, SAP Oracle Apps CICS VSAM

Secure Access – Accredited
Username/Password Logon Connector connects with same ID for all queries Optional: Integrated with existing authentication system MetaMatrix Data Source Client App Connector username password username password Membership Provider authenticates Trusted Payload Logon: Connector uses different credentials per connection, per query Optional: Integrated with existing authentication system source- specific MetaMatrix Data Source Client App Connector trusted payload trusted payload logon info trusted payload payload payload Optionally accesses source-specific information Authentication Service Membership Provider authenticates, generates payload authenticates, optionally modifies payload

Managing Data Service Metadata
Relational Transformations XML XML Rel Datatypes XML MetaMatrix Catalog MetaMatrix Designer Generic Typed Relationships Domain [UML/ER] Models & Files [versioned] Web Services [WSDL] Processes [BPM/BPEL] Search Index Web Reporting WSDL Process X Process Y Service A B Application/ Configuration Ontologies [OWL/RDF] Taxonomies Classification Schemes Taxonomy A KeyWords B

MetaMatrix Product Lines
MetaMatrix Enterprise Web services & SQL Modeling enterprise data Scalable deployment server Metadata management Application/legacy connectors MetaMatrix Enterprise Enterprise MetaMatrix Dimension MetaMatrix Dimension Web service-enablement of data sources Expose business views as XML Lightweight modeling – rapid integration Standard WAR-based deployment Project, Node Introducing Dimension Historically Enterprise or Query, where Query = the embeddable engine (minus the design tool) for MM software partners looking to bundle MM for std relational DB’s Now Dimension doesn’t have all the bells/whistles of Enterprise - instead is solely focused on providing data services solution for information sharing purposes Single-purpose, therefore more intuitive Designer tool Smaller footprint, lower cost Rapid creation and deployment of web service interfaces to databases Also good for distributed nodes in a larger info sharing network (federal vs. state/local) MetaMatrix Query Embeddable Java component Federated query engine Query optimization Standard JDBC to all sources Standard SQL to all sources MetaMatrix Query ISV / Project

Mediation: XML From Non-XML Sources
Target: Fixed (potentially complex) XML Schema Need: Data complying to Schema Source: Data Sources containing Information to integrate «Relational» «XML» <person> <addresses> … </addresses> <accounts> <accountID=…> </accountID> </accounts> </person> «Text File» T One of the biggest challenges within an SOA environment is to take data from one or more relational or legacy sources and transform it to XML. Often the XML output needs to conform to a specific data model or schema such as FXml and NIEM. MetaMatrix can easily map data from any relational, file or legacy source to the required XML format – all without coding. This mediation capability is one of the most important roles of the data services layer. MetaMatrix: Mapping from Data to XML «Application»

Map Data Sources to XML & Deploy
MetaMatrix Designer – for XML-centric Data Services Model XML Docs, Schemas Build XML Doc. models from XML Schemas Map XML Doc. models to other data models Enable data access via XML

Dimension – Choose your approach
Rapid design & deployment of Web Services Expose integrated data as XML-based business views Deployment of Web Services as standard Web apps Runtime execution optimized through use of MetaMatrix Query Engine Dimension Models Start Here? Web Server Data Sources Business Views <XML> Web Service Operations WSDL XSD Source Models Deploy Import Map Model WAR as to Quick design/deploy Use std vocabulary Use web services standards – XML, XSD, SOAP, WSDL Optimized performance Start Here?

C2, Logistics, Intelligence, …
COI Data Dictionary Business Intelligence Applications Search Applications Web Services ODBC/JDBC JDBC SOAP Application views of information: Relational, XML XML Document <a> <b> … </b> </a> T T T C2, Logistics, Intelligence, … Logical Data Model: Agency or COI-specific Rationalize, harmonize, mediate bldg_id SITENUM Facility_ID Location_ID bldg_type Depot_Number Location_Type T T T Authoritative Sources: Mapped to logical Multiple Internal/External Information Sources

Semantic Matching - example
Ontology “Sex” semantically related to “Gender” Semantic Data Services key component of information sharing and interoperability programs automated semantic mapping to aid domain experts in quickly reconciling disparate schemas and vocabularies more rapid deployment of a mediation solution MatchIt an extensible ontology-driven tool variety of algorithms for determining semantic equivalence discovers similarities between elements of heterogeneous data, automatically exposing potential semantic matches. matches elements of data sources to target schemas of Data Services, such as TWPDES, GJXDM, NIEM, C2IEDM, HL7 Matched (Confidence of 90%) Gender ID Semantic Data Services Person Sex Code FBI CBP NYC NY NJ Data Sources

Automated Term Discovery (Interpret)
All the available definitions found in the MatchIT knowledge-base Results of the automated tokenization All the usage instances where each term was used in any of the sources A comprehensive list of terms automatically discovered across all sources

Contextualize (Interpret)
ArticleAmount Amount Article Sum Assets Creation Synonym Type-of Automated term tokenization Automated semantic linking using the default knowledge-base contained within MatchIT

Semantic Matching (Mediate)
With relationships pre-established within the knowledge-base… Identify the Target and the Source(s) and run the match. ArticleAmount Automatically linked by a specific % distance ProductShares

Facilitate Decision Making (Mediate)
Target element for matching Automatically calculated semantic distance between terms Helps facilitate rapid decision making Source candidate for matching

Support Multiple Enterprise Semantic Models
Business Intelligence Applications Portal Applications Web Services ODBC/JDBC JDBC SOAP J-8 Force Structure J-7 Operational Plans J-6 C4CS J-5 Plans & Policy Enterprise-wide or COI-driven Data Models Rationalization Harmonization Data Catalogs (DDMS) J-4 Logistics (GCSS) J-3 Operations J-2 Intelligence J-1 Manpower / Personnel T T T Data Sources - Authoritative Redundant Overlapping Multiple Internal/External Information Sources

Why Vocabulary Management?
You can’t act on data alone! Knowledge lies everywhere - you must involve data from disparate sources The volume and disparately of data is too significant - you must enable machine involvement Using semantics is not enough - you must be able to leverage domain concepts and terminologies You must have the ability to infer relationships across the data

Benefits of Vocabulary Management
Develop reusable information models and schemas Implicitly improves data integrity Capture business and technology requirements in a single vocabulary Capture institutional knowledge Enables semantic mining techniques for deeper data discovery and information sharing Accelerate interoperability, web services and SOA development and deployment Establish and maintain a common relationship across data sources Establish and maintain compliance with industry exchange models Reduce IT expenses by leveraging data in its native source Reduce IT expenses associated with building and maintaining partner integration Improved information sharing directly enhances decision making

Knoodl.com - from Revelytix
A publicly-available collaborative wiki for collaborative vocabulary/ontology development Extends the wiki metaphor with a formal model for semantic markup Ideal for Community of Interest (COI) based OWL development Domain vocabulary creation and management OWL registry/repository Scheduled to go live 30 Oct 06

Integration Driven By Semantics
Ontology Models (e.g. OWL, RDF) Relate information in different domains/models Search within and across domains for related information Enterprise Model (UML) Model & Relate information within any domain Data Models (Relational, XML) Physical Sources XML

Ontology-Driven Integration Example
Logical Views Physical Sources equivalence Transportation T Land T 4 Wheel 2 Wheel T Bus Truck Car T Fuel Truck Cargo Truck

Person Search - Conceptual Use Case
Enterprise Information: Addresses Organizations Affiliations Accounts Transactions Call History Agreements Policies Relationships inherent in the search results link to enterprise apps, databases, and other repositories

Incorporating Enterprise Data into Search
The usefulness of an organization's data is dependent upon understanding and applying context In a typical text search application, context is supplied by document content, or metadata tags (filename, author, date, etc.) An organization's structured data sources do not usually lend themselves to document-centric approaches The context of structured data relies on: metadata (typically implicit) for table names, column names, datatypes, and business descriptions for each implied DB relationships such as foreign keys between tables relationships (mappings) to a business data dictionary The volume of structured data requires a combination of indexed and non-indexed approaches

MetaMatrix and Google . Google Search Appliance MetaMatrix Server
Content Repository 2 Content Repository Text Search w/ filtering criteria (optional) . Content Repository Select & drill down to discover record details, related data links, & metadata Structured Data crawling & index build 3 1 HTML I/F RDBMS Field name look-up in Business Data Dictionary HTML I/F JDBC Connector Framework MetaMatrix Server ERP, CRM… 4 Legacy Systems Navigate to related data from Search UI Custom Application

Data Source Schema (as is)

Enhanced Data Model for Search
Transformations from one or more sources Transformations defined with: Joins/unions Criteria Functions Elements mapped to dictionary Business definitions captured

Major US Federal Government Customers
NSA - Multiple Programs (NES Base-lined) In-Q-Tel/CIA TRANSCOM – Command Metadata Management System Air Force - Command and Control Center DISA - Global Combat Support Systems (GCSS) DISA – Anti Drug Network (ADNET) DLA – Integrated Data Environment (IDE) Mitre – Air Force ESC/DoD DDMS work UK – NSA Equivalent, CJIT

DISA GCSS – Customer Use Case
Global Combat Support System (GCSS) Mission: supply the war-fighter with access to accurate and timely logistics information Focused Logistics Fusion of information technologies to enable forces of the future to be more mobile and versatile Provides the joint war fighter with a single capability to manage and monitor units, personnel, and equipment Deployed at 23 sites around the world Networked environment allows DoD users to access shared data & applications, regardless of location Conducted and comprehensive evaluation/competitive procurement and selected MetaMatrix

GCSS Architectural Overview
WebCOP CSDE Portal Portal Clients Web Browser Sun 280R Solaris 9: WL8.1 Sun 280R Solaris 9: Sun V880 Solaris 9/10: WL8.1 Electronic Battlebook Oracle Query Tools Watchboard MetaMatrix Force Closure NGA BI tool Web Services DoD PKI Directory Web Services DMDC Ligthhouse JOPES FLIS CSDS GTN GDSS JTAV GSORTS (JOPES2K) Theater Data Sources (also TMS)

GCSS Modeling Approach
Virtual Query Layer (VQL) (Exposed Views) Public Data Material_VQL Plans_VQL Facilities_VQL Virtual Mid Layer (VML) Facilities_VML Material_VML Private Data and Metadata Virtual Base Layer (VBL) CSDS_VBL Physical Layer (PL) CSDS_PL GDSS JOPES Classic GTN CSDS DMDC FLIS JOPES 4.0 IDE/AV CFDB NGA GSORTS

AFC2ISRC's Air Ops Data Unification
Data Standards Communities Of Interest JC3IEDM U N I F E D C2IEDM VMF USMTF V O C A B U L R Y BFT ISR SADL UID Link-16 CMD Programs METOC TST JSA When we fold all three of these types of organizations together, we can see how this becomes rather large and complex very quickly. From just from data standards communities, the USMTF brings in over 7,000 data fields, and Link 16 adds almost 400 more. When you combine that with multiple programs of record existing data stores and structures along with the various communities of interest schemas, the control and management aspect becomes critical. The next slide shows our current approach for trying to address this complexity. ADOCS Mobility Ops TBONE GCCS DCGS GCSS

US TRANSCOM: Metadata Federation
Integrate diverse sources of metadata to achieve enterprise-wide, end-to-end systems analysis and impact awareness Metadata Search & Reporting Common Metadata Repository Viewer Metadata Integration Layer MetaMatrix VDB ERWin CRIS (DTS Metadata) MetaBase Import DTS-ERWin Relationships

Data Relationships in CMDR Viewer
Source System Target System Interface Template Interface Template Elements Related Entity & Attributes from ERWin Model New Source-to-Interface-to-Target Relationship Submitted to Repository

NSA’s E-Space Portal for STRATCOM
Client(s) Planners Operators Application Server Host(s) Information Integration Host(s) Data Host(s) Browser ODS Cached Data Presentation Layer Query Page Control ResultsPage Control Form Page Control MetaMatrix Query Engine Browser Browser MetaBase Mission Data Query Service (Business Layer) Mission Data Data dictionary Integration paths Portal metadata Mission Data Sources Portal Metadata Name Information Context Usage Description Display Name Default Value Label Attribute Units Logical Operator(s) Presentation Type Sort Order Visible Top-down (mapping) Bottom-up (harmonization) External Feeds Org, Person, Image, Location <X> </X> T

The Path to Information Sharing
RELATE Create vocabularies Domain collaboration MEDIATE Automate semantic matching Decision facilitation Data service creation INTERPRET Discover terminologies Relate to embedded knowledge-base Inventory, assess, & analyze resources GATHER Import or reverse engineer resources Import exchange models & knowledge-bases Metadata repository for storing/relating/querying

Synergistic products RELATE MEDIATE INTERPRET GATHER
Knoodl.com & MetaMatrix Ontology or Knowledge-base Mgmt. Use domain knowledge to mapping Bottom-Up or Top-Down MEDIATE MetaMatrix & MatchIT Import domain vocabularies Disambiguate match sets using semantics Create enterprise or domain-level data services Map to schema-compliant XML documents INTERPRET MetaMatrix & MatchIT Automated symbol discovery Present “target” logical model Inventory & assess resources GATHER MetaMatrix Use importers (JDBC, ODBC, UML, ERwin, Popkin, and XML Schema) Store in integrated metadata repository

MetaMatrix Value Proposition
Rapid, cost-effective COTS tool for enterprise information integration and exchange On-demand information Real time data integration Information sharing between business units Enabling SOA in an evolving world Consume and produce Web services And still provide full support for ODBC, JDBC, and legacy Federation of disparate information Rationalized to controlled vocabularies Relational + XML + Web Services + Enterprise Apps + Legacy Faster time to market Integrated information in days, weeks Tight coupling of design & implementation phases Leveraging the skill-set of the data architects for integration Costs across application lifecycle reduced Model-driven abstraction layer eases development/maintenance Better management of data assets across the enterprise The benefits of an EII based solution are: The integration of historical, reference, and operational data into a single virtual database. Breaking down the barriers between lines of business to expose a wide and complete source of enterprise information. The federation of multiple different sources of information such as databases, warehouses, packages applications, legacy systems, and XML documents. Shorter time-to-market, and faster implementation because of the model driven nature of the infrastructure. Ability to directly leverage the skills of information architects who are familiar with the enterprise information assets, but who may not be comfortable in a programmatic environment. Reduced costs due to the metadata repository that provides tight linkage between the design and implementation phases. Reduced costs during maintenance because the ability of the metadata repository to capture documentation, attributes, and ownership of the metadata, providing impact analysis and reporting during maintenance.

Achieving Information Sharing in Federal Agencies via Data Services, SOA, and Controlled Vocabularies A Presentation for the Federal Data Architecture Subcommittee Chuck Mosher metamatrix.com October 12, 2006

Additional Technical Material
Chuck Mosher metamatrix.com October 2006

Certifications NIAP Certification in process DCIDS 6/3 Common criteria
Evaluation Assurance Level 2 (EAL2) Security Target document completed Cygnacom – testing, validation DCIDS 6/3 Protection Level 3 (PL3), Sept-Oct 2004 Ft. Meade Enterprise Information Technology Center Working in conjunction with X7 group Hosted on Sun Solaris for specific Ft. Meade program

Meta Object Facility (MOF)
model Model Data

Connector Framework MetaMatrix Query Engine Connector Framework
Connectors use the framework + metadata to integrate new sources quickly – avoids significant cost, time of new wrappers. Request Result Connector Framework Connector Connector Connector Translator Translator Translate Input Translate Output Connection Read Response Write Request Translator Translate Input Translate Output Connection Read Response Write Request Translator turns MM requests into source-specific requests, and translates results. Translate Input Translate Output Connection Write Request Read Response Connection holds the (pooled) connection, sends requests, receives responses Any Information Source

MetaMatrix Complements ESB’s
Dimension adds the following capabilities to an ESB… Rich, advisor-based, model-driven design tool Ability to leverage data models and manage metadata Clear way to visualize and define mappings between non-XML sources and XML views (even for complex industry schemas – C2IEDM, NIEM, GJXDM, HL7, XBRL) Ability to do SQL-based transformations, not just XSLT (including multi-source, complex joins and unions) Query planner/optimizer that makes intelligent decisions about whether to execute transformations “at the source” vs. “on the bus” Automated semantic matching & generation of transformations Data Services to connect ESB’s to Enterprise Data

Achieving Information Sharing in Federal Agencies via Data Services, SOA, and Controlled Vocabularies A Presentation for the Federal Data Architecture.

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Achieving Information Sharing in Federal Agencies via Data Services, SOA, and Controlled Vocabularies A Presentation for the Federal Data Architecture.

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