Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center 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 provides - a set of Decision-Support Tools - analysis for integrated prescriptions - multi-variable forest management - multi-scale support from plot to landscape NED Goal: provide a set of tools for Natural Resource Decision Support

Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center 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 The NED Decision Process

Blackboard Prolog Clauses MS Access Databases AGENTSAGENTS Inference EnginesKnowledge Models Meta-knowledge Temporary Data Files Simulators GIS Visual Models HTML Report s Interface Modules Control Flow Information Flow NED Architecture

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

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

Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center NED-2 Query Process Initiator BLACKBOARDBLACKBOARD NED-2 Agents ProData ODBC Database Meta Data

Northeastern Research Station Southern Research Station 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 Query : What is the area of the stand in snap shot 0 ? Prolog Query 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