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Achieving Information Sharing in Federal Agencies via Data Services, SOA, and Controlled Vocabularies October 12, 2006 A Presentation for the Federal Data.

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Presentation on theme: "Achieving Information Sharing in Federal Agencies via Data Services, SOA, and Controlled Vocabularies October 12, 2006 A Presentation for the Federal Data."— Presentation transcript:

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

2 2 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

3 3 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)

4 4 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, weve moved into an environment where weve got faster networks, more powerful processors, but it really comes down to the data Michael Todd, DOD CIO office Data Interoperability Is At The Very Core of The Transformation Sought by the Federal Government

5 5 Dr. Linton Wells, as quoted in Septembers 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 …

6 6 NCES & Data Net-Centricity Application DBMS Server Application DBMS Server As-is = Application Silos To-be = SOA Stack 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

7 7 The Data Challenges 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 Getting the right information to the right person at the right time requires:

8 8 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

9 9 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

10 10 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

11 11 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 Mission Challenges Time-to-deploy Agility - Responsiveness to change Automation – Reduce cost of new development and operations ROI of enterprise information Agency Challenges 100s/1000s of data sources 100s/1000s 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 Communities of Interest Information Challenges ?

12 12 Information Virtualization Information Resources Communities of Interest Information Virtualization Layer

13 13 Information Virtualization Unified Semantic Layer Information Virtualization Layer Data Federation Layer Data Access/Connectivity Layer Enterprise Data Sources Unification of different concepts across systems Single-query access to heterogeneous systems Uniform, standardized access to any system

14 14 What is a Data Service? Master Data Operational Data Store Agency Application Data Service SQL API Call XML/SOAP 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 Bridge the Gap SQL

15 15 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

16 16 Data Service Layer in SOA Client Process & Applications Data Sources Data Services Layer Message Services (ESB) Business Services Business Process Services App Data Service

17 17 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

18 18 Data, Content Sources Logical Data Model Data Services Approaches T Org, Person, Image, Location Materialized Logical Model 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 Model-Driven Integration Layer Data, Content Sources Logical Data Model T Organization, Customer, Imagery, Location Materialized Logical Model Agile Information Services Enriched Data/Content Store

19 19 Search Engine Index / Metadata Catalog Master Data Person / Facility / Vehicle Enterprise Data Services Stage SOA Apps Federal Agencies Data Access Services SQL, Web Service/XML Staged Data (optional) Ontology Mgmt / Reasoning Enterprise Service Bus / Intranet / Extranet Distributed Data Services Land/Sea State/Local Security/Authentication Operations Management Error / Exception Management Orchestration Encryption High Availability Mediation XSLT, Multi- source Information Exchange Topology

20 20 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

21 21 MetaMatrix I.P. MetaMatrix has 2 distinct innovations that work in concert to yield significant business benefits: Model-basedExtensible Sharable, reusable Standards-based Information Modeling Federated Querying Cost-based optimizer Read/write/transactions Uniform API, any source Battle-tested/hardened

22 22 MetaMatrix Enterprise Data Services 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

23 23 Designing data services Modeling Instead of Coding xml databases warehouses spreadsheets services geo-spatial rich media … Enterprise Information Sources (EIS) Information Consumers Reusable, Integrated Data Objects ExposedDataServices (contract) Custom Apps Web Services, Business Processes Packaged Apps Reporting, Analytics EAI, Data warehouses ODBC JDBC SOAP Logistics Intelligence

24 24 MetaMatrix Designer 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. Virtual Models Physical Models Representing Actual Data Sources

25 25 MetaMatrix Integration Server Information Consumers Web SvcXMLRDBMS Packaged Connectors Siebel, SAP Oracle Apps CICS VSAM MetaMatrix Catalog MetaMatrix Designer - Design and deploy data services MetaMatrix Products JMS ODBC JDBC SOAP Query Processor Optimizer Integration Server Virtual Data Bases VDB Integrated Security Users Roles Entitle ments Access Models ViewsXML Docs … Services inoutproc MetaMatrix Connector Framework MetaMatrix Server

26 26 Secure Access – Accredited MetaMatrix Client App username password Membership Provider username password authenticates Connector Data Source Optionally accesses source-specific information source- specific trusted payload MetaMatrix Client App Membership Provider Authentication Service logon info authenticates, generates payload trusted payload trusted payload authenticates, optionally modifies payload payload Username/Password Logon Connector connects with same ID for all queries Optional: Integrated with existing authentication system Trusted Payload Logon: Connector uses different credentials per connection, per query Optional: Integrated with existing authentication system Data Source

27 27 Process X Process Y Processes [BPM/BPEL] Ontologies [OWL/RDF] Taxonomies Service A Service B Web Services [WSDL] Classification Schemes Taxonomy A KeyWords B Relational XML Transformations Datatypes XML Rel Domain [UML/ER] MetaMatrix Designer MetaMatrix Catalog Generic Typed Relationships Models & Files [versioned] Search Index Web Reporting WSDL Application/ Configuration Managing Data Service Metadata

28 28 MetaMatrix Enterprise MetaMatrix Dimension MetaMatrix Query MetaMatrix Product Lines MetaMatrix Enterprise Web services & SQL Modeling enterprise data Scalable deployment server Metadata management Application/legacy connectors MetaMatrix Dimension Web service-enablement of data sources Expose business views as XML Lightweight modeling – rapid integration Standard WAR-based deployment MetaMatrix Query Embeddable Java component Federated query engine Query optimization Standard JDBC to all sources Standard SQL to all sources Enterprise Project, Node ISV / Project

29 29 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

30 30 T «Text File» «Relational»«Application» MetaMatrix: Mapping from Data to XML Source: Data Sources containing Information to integrate Target: Fixed (potentially complex) XML Schema Need: Data complying to Schema Mediation: XML From Non-XML Sources «XML» … …

31 31 Model XML Docs, Schemas Build XML Doc. models from XML Schemas Map XML Doc. models to other data models Enable data access via XML Map Data Sources to XML & Deploy MetaMatrix Designer – for XML-centric Data Services

32 32 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 Web Server Data Sources Business Views Web Service Operations WSDLXSD Source Models DeployImportMapModel WARasto Start Here? Start Here?

33 33 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

34 34 T Authoritative Sources: Mapped to logical Multiple Internal/External Information Sources Application views of information: Relational, XML T T XML Document … T T T ODBC/JDBC JDBC SOAP Web Services Web Services Search Applications Business Intelligence Applications Business Intelligence Applications Logical Data Model: Agency or COI-specific Rationalize, harmonize, mediate C2, Logistics, Intelligence, … COI Data Dictionary bldg_idSITENUMFacility_ID Location_ID bldg_typeDepot_Number Location_Type

35 35 FBICBPNYCNYNJ Semantic Data Services Matched (Confidence of 90%) Gender ID Person Sex Code Ontology Sex semantically related to Gender Semantic Matching - example Data Sources 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

36 36 Automated Term Discovery (Interpret) A comprehensive list of terms automatically discovered across all sources All the available definitions found in the MatchIT knowledge-base All the usage instances where each term was used in any of the sources Results of the automated tokenization

37 37 Contextualize (Interpret) Automated term tokenization Automated semantic linking using the default knowledge-base contained within MatchIT ArticleAmount AmountArticle Sum Assets Creation Synonym Type-of

38 38 Semantic Matching (Mediate) With relationships pre-established within the knowledge-base… Identify the Target and the Source(s) and run the match. ArticleAmount ProductShares Automatically linked by a specific % distance

39 39 Facilitate Decision Making (Mediate) Helps facilitate rapid decision making Target element for matching Automatically calculated semantic distance between terms Source candidate for matching

40 40 J-8 Force StructureJ-7 Operational PlansJ-6 C4CS T Data Sources - Authoritative - Redundant - Overlapping Multiple Internal/External Information Sources T T ODBC/JDBC JDBC SOAP Web Services Web Services Portal Applications Business Intelligence Applications Business Intelligence Applications Enterprise-wide or COI-driven Data Models Rationalization Harmonization Data Catalogs (DDMS) Support Multiple Enterprise Semantic Models J-5 Plans & PolicyJ-4 Logistics (GCSS)J-3 OperationsJ-2 IntelligenceJ-1 Manpower / Personnel

41 41 Why Vocabulary Management? –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 You cant act on data alone!

42 42 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

43 43 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

44 44 Enterprise Model (UML) Data Models (Relational, XML) XML Physical Sources Model & Relate information within any domain Ontology Models (e.g. OWL, RDF) Relate information in different domains/models Search within and across domains for related information Integration Driven By Semantics

45 45 Ontology-Driven Integration Example Land 4 Wheel 2 Wheel Truck Bus Car Fuel Truck Cargo Truck Transportation T T T T equivalence Logical Views Physical Sources Ontology

46 46 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

47 47 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

48 48 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

49 49 MetaMatrix and Google MetaMatrix Server RDBMS ERP, CRM… Content Repository Legacy Systems Google Search Appliance Content Repository...... Custom Application Content Repository Text Search w/ filtering criteria (optional) Structured Data crawling & index build Navigate to related data from Search UI HTML I/F JDBC Connector Framework Select & drill down to discover record details, related data links, & metadata Field name look-up in Business Data Dictionary 1 2 3 4

50 50 Data Source Schema (as is)

51 51 Transformations from one or more sources Transformations defined with: –Joins/unions –Criteria –Functions Elements mapped to dictionary Business definitions captured Enhanced Data Model for Search

52 52 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

53 53 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

54 54 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

55 55 GCSS Architectural Overview CSDE MetaMatrix Sun V880 Solaris 9/10: WL8.1 Portal NGA Sun 280R Solaris 9: WL8.1 WebCOP Sun 280R Solaris 9: GDSS JTAV GSORTS JOPES (JOPES2K) Theater Data Sources (also TMS) GTN Ligthhouse Portal Clients Web Browser DoD PKI Directory CSDSFLIS DMDC Oracle BI tool Electronic Battlebook Query Tools Watchboard Force Closure Web Services Web Services

56 56 CFDB CSDS DMDC GSORTS IDE/AV NGA FLIS CSDS_PL CSDS_VBL Facilities_VML Material_VML Facilities_VQL Material_VQL GDSS Plans_VQL Private Data and Metadata Public Data Virtual Mid Layer (VML) Virtual Query Layer (VQL) (Exposed Views) JOPES Classic JOPES 4.0 Virtual Base Layer (VBL) Physical Layer (PL) GTN GCSS Modeling Approach

57 57 UID CMD METOC JSA ISR JC3IEDM C2IEDM VMF SADL AFC2ISRC's Air Ops Data Unification ADOCS USMTF Link-16 TBONE GCCS GCSS UNIFIEDUNIFIED VOCABULARYVOCABULARY DCGS BFT TST Communities Of Interest Data Standards Programs Mobility Ops

58 58 US TRANSCOM: Metadata Federation Integrate diverse sources of metadata to achieve enterprise-wide, end-to-end systems analysis and impact awareness CRIS (DTS Metadata) MetaBase Metadata Integration Layer ERWin MetaMatrix VDB Metadata Search & Reporting Common Metadata Repository Viewer Import DTS-ERWin Relationships

59 59 Data Relationships in CMDR Viewer Source System Target System Interface Template Interface Template Elements Source System Related Entity & Attributes from ERWin Model New Source-to-Interface-to-Target Relationship Submitted to Repository

60 60 MetaBase Data dictionary Integration paths Portal metadata NSAs E-Space Portal for STRATCOM Data Host(s) ODS Cached Data Mission Data Mission Data Mission Data Sources MetaMatrix Query Engine Information Integration Host(s) Application Server Host(s) Query Service (Business Layer) Presentation Layer Query Page Control Results Page Control Form Page Control Browser Client(s) Planners Operators T Bottom-up (harmonization) Top-down (mapping) Org, Person, Image, Location Portal Metadata Name Information Context Usage Description Display Name Default Value Label Attribute Units Logical Operator(s) Presentation Type Sort Order Visible External Feeds

61 61 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

62 62 The Path to Information Sharing Import or reverse engineer resources Import exchange models & knowledge-bases Metadata repository for storing/relating/querying Discover terminologies Relate to embedded knowledge-base Inventory, assess, & analyze resources Automate semantic matching Decision facilitation Data service creation Create vocabularies Domain collaboration GATHER INTERPRET MEDIATE RELATE

63 63 Synergistic products MetaMatrix Use importers (JDBC, ODBC, UML, ERwin, Popkin, and XML Schema) Store in integrated metadata repository MetaMatrix & MatchIT Automated symbol discovery Present target logical model Inventory & assess resources MetaMatrix & MatchIT Import domain vocabularies Disambiguate match sets using semantics Create enterprise or domain-level data services Map to schema-compliant XML documents Knoodl.com & MetaMatrix Ontology or Knowledge-base Mgmt. Use domain knowledge to mapping GATHER INTERPRET MEDIATE RELATE Bottom-Up or Top-Down

64 64 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 MetaMatrix Value Proposition Rapid, cost-effective COTS tool for enterprise information integration and exchange

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

66 Additional Technical Material October 2006 Chuck Mosher cmosher @ metamatrix.com

67 67 NIAP Certification in process –Common criteria –Evaluation Assurance Level 2 (EAL2) –Security Target document completed –Cygnacom – testing, validation –http://www.cygnacom.com/labs/sel_epl.htm 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 Certifications

68 68 Data Model Meta- model Meta Object Facility (MOF)

69 69 Connector ResultRequest Connector Framework Connectors use the framework + metadata to integrate new sources quickly – avoids significant cost, time of new wrappers. Any Information Source Translator turns MM requests into source- specific requests, and translates results. Connection holds the (pooled) connection, sends requests, receives responses Connector Translator Translate Input Translate Output Connection Read Response Write Request MetaMatrix Query Engine Translator Translate Input Translate Output Connection Read Response Write Request Translator Translate Input Translate Output Connection Read Response Write Request Connector Framework

70 70 MetaMatrix Complements ESBs 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 ESBs to Enterprise Data


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