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NED - Intelligent Information System for Natural Resource Decision Support

NED is a powerful decision support system that integrates data from various sources to provide tools for natural resource management. With features like knowledge models, inference engines, and simulation sources, NED aids in goal setting, analysis, and evaluation of forest management strategies.

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NED - Intelligent Information System for Natural Resource Decision Support

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  1. Welcome

  2. PROLOG/RDBMS Integration In The NED Intelligent Information System

  3. Participants University of Georgia F. Maier D. Nute W.D. Potter J. Wang M. Dass H. Uchiyama USDA Forest Service M. J. Twery H. M. Rauscher P. Knopp S. Thomasma,

  4. NED Goal: provide a set of tools forNatural Resource Decision Support • NED provides - a set of Decision-Support Tools - analysis for integrated prescriptions - multi-variable forest management - multi-scale support from plot to landscape

  5. The NED Decision Process • Create the goals & measurement criteria • Inventory & current condition analysis • Design alternative courses of action • Forecast the future through simulation • Assign values to the measurement criteria • Evaluate how well goals have been met • If not satisfactory, go back to step 1

  6. Knowledge Models Inference Engines Meta-knowledge A G E N T S Blackboard Prolog Clauses Temporary Data Files Simulators GIS Visual Models MS Access Databases HTML Reports Interface Modules Control Flow Information Flow NED Architecture

  7. HeterogeneousSources As an Intelligent Information System, NED provides seamless integration of (possibly heterogeneous, distributed): • Microsoft Access Databases (e.g. inventory) • Knowledge Bases (e.g. treatments and goals) • Simulation Sources (e.g. FVS and Silvah) • Visualization Sources (e.g. Arcview and Envision)

  8. PROLOG/RDBMS The ProData method to query a database did not meet the needs of NED-2 because : • Processing data from multiple tables is slow • It requires the database schema to be known • Changes to the database schema are allowed only at design time (not during operation)

  9. Integration Techniques Techniques for Integration (Brodie & Jarke, 1988): • Coupling existing PROLOG & RDBMS implementations • Extending PROLOG to include DBMS • Extending DBMS to include PROLOG • Tightly integrating LP techniques with DBMS techniques

  10. NED-2 Query Process Initiator B L A C K B O A R D Meta Data NED-2 Agents ProData ODBC Database Database Database Database Database

  11. NED-2 Feature Special Feature in NED-2 Ability to retrieve information from multiple data sources without having to specify, within a query, where the data is to be found (e.g., in DBs, KBs, or as the result of simulations). Metadata is the key.

  12. CREATING METADATA Creating metadata dynamically..

  13. Query Example Query : What is the area of the stand in snap shot 0 ? Prolog Query : known(‘STAND_AREA’([‘SNAPSHOT’ = 0])). Query: ‘STAND_AREA’ = X, ‘SNAPSHOT’ = 0 Source Matching:‘STAND_HEADER’: ‘STAND_AREA’ = X, ‘SNAPSHOT_TREATMENTS’: ‘SNAPSHOT’=0 Join Constraints :‘STAND_HEADER’:‘STAND’ = ‘SNAPSHOT_TREATMENTS’:‘STAND’

  14. Query Example (cont.) SQL Statement : SELECT ‘STAND-HEADER’.‘STAND-AREA’ FROM ‘STAND-HEADER’ ‘SNAPSHOT-TREATMENTS’ WHERE ‘SNAPSHOT-TREATMENTS’.‘SNAPSHOT’ = 0 AND ‘STAND-HEADER’.‘STAND’ = ‘SNAPSHOT-TREATMENTS’.‘STAND’

  15. Query Language Features • Arithmetic operations • Logical Operations • Aggregates • Subqueries • IN and BETWEEN • DISTINCT and ALL

  16. Conclusion • Makes full use of database capabilities. • Faster query set-up and processing. • No need for full knowledge of a schema.

  17. Further Information http://www.fs.fed.us/ne/burlington/ned/

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