Welcome Northeastern Research Station Southern Research Station The

Welcome Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center

PROLOG/RDBMS Integration In The NED Intelligent Information System Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center

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, Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center

NED Goal: provide a set of tools for Natural 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 Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center

The NED Decision Process 1. Create the goals & measurement criteria 2. Inventory & current condition analysis 3. Design alternative courses of action 4. Forecast the future through simulation 5. Assign values to the measurement criteria 6. Evaluate how well goals have been met 7. If not satisfactory, go back to step 1 Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center

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

Heterogeneous Sources 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) Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center

PROLOG/RDBMS The Pro. Data 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) Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center

Integration 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 Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center

NED-2 Query Process Initiator B L A C K B O A R D Database Meta Data NED-2 Agents Pro. Data ODBC Database Northeastern Research Station Southern Research Station Database The University of Georgia Artificial Intelligence Center

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. Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center

CREATING METADATA Creating metadata dynamically. . Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center

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’ Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center

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’ Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center

Query Language Features • • • Arithmetic operations Logical Operations Aggregates Subqueries IN and BETWEEN DISTINCT and ALL Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center

Conclusion • Makes full use of database capabilities. • Faster query set-up and processing. • No need for full knowledge of a schema. Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center

Further Information http: //www. fs. fed. us/ne/burlington/ned/ Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center