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Michigan DMR Data Exchange With EPA or “What I Node To Be True”

Michigan DMR Data Exchange With EPA or “What I Node To Be True”. Bill Geake Michigan Department of Information Technology. DEQ Data Coding Operators. Overview of DMR Data Flow Process. Regulated Wastewater Facilities. Michigan DEQ. US EPA. PCS. IDEF GenTran. MI Node Client.

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Michigan DMR Data Exchange With EPA or “What I Node To Be True”

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  1. Michigan DMR Data Exchange With EPAor “What I Node To Be True” Bill Geake Michigan Department of Information Technology

  2. DEQ Data Coding Operators Overview of DMR Data Flow Process Regulated Wastewater Facilities Michigan DEQ US EPA PCS IDEF GenTran MI Node Client Legacy CDX CDXNode

  3. Facility-to-State DMR Data Flow Regulated Wastewater Facilities DB Michigan DEQ DEQ DMZ DEQ Intranet E-DMR DB NMS DB

  4. State-to-EPA DMR Data Flow State of Michigan EPA DEQ Intranet PCS NMS DB IDEF GenTran MI Node Client Legacy CDX CDX Node

  5. DMRFlow.DLL XML Parser IDEF MV XML File E-DMR XML File E-DMR Toolset DMR-IDEF-v1.xslt Michigan Node Client Processing Michigan Node Client NMS DB

  6. IDEF PCS Report Notification Receipt Acknowledgement EPA-to-StateFeedbackFlow EPA PCS IDEF GenTran Processor Legacy CDX CDX Node From State

  7. EPA-to-State Feedback Flow PCS Update Audit Report EPA State of Michigan PCS Reject Resolution Utility CDX Web Site NMS DB DEQ Data Coding Operator

  8. The BIG Picture 80-Column text file State NPDES Permit Database PCS Edit/Update Reports

  9. The BIG Picture State NPDES Permit Database PCS IDEF GenTran IDEF XML CDX Web Site Edit/Update Reports (PDF Format)

  10. The BIG Picture State NPDES Permit Database PCS IDEF GenTran CDX Web Site Edit/Update Reports (PDF Format) CDX Node IDEF XML

  11. FLOWS are about PROCESSING! • Two Aspects of Data Exchange: • Format • Data Container • XML Schema • Process • Rules surrounding the flow of data • More complex than creating a format • Can we have XML-based processing instructions?

  12. The “Ideal” Data Flow Scenario • One Owner/Authority for a given dataset • Each owner publishes to their node • Nodes each provide a common interface for given data flow

  13. The “Ideal” Data Flow Scenario • Data Consumer should initiate Requests from the Data Provider • “Come and get it” approach • Only the consumer knows what it needs and when it needs it Want My Data? Data ProviderNode Data ConsumerNode No…No…No…

  14. The “Ideal” Data Flow Scenario • Data Provider should need NO knowledge of Data Requestor’s state • Complexity of storing data consumer’s state on the provider side • Not as scalable Gimme the Data I Don’t Have Data ProviderNode Data ConsumerNode Nope. Tell me what you want!!!

  15. The “Synchronization” Data Flow Scenario • Push or Pull will work • Should all happen in the background • Only require user intervention when and exception occurs • Difficult to achieve when synchronizing heterogeneous systems • Crosswalking data…what a mess!

  16. Final Thoughts…

  17. The Ubiquitous “Questions” Slidewith Tacky WordArt Questions???

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