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Database Design

Database Design. Dr. M.E. Fayad, Professor Computer Engineering Department, Room #283I College of Engineering San José State University One Washington Square San José, CA 95192-0180 http://www.engr.sjsu.edu/~fayad. Lesson 09: The MLPQ System. 2. Lesson Objectives.

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Database Design

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  1. Database Design Dr. M.E. Fayad, Professor Computer Engineering Department, Room #283I College of Engineering San José State University One Washington Square San José, CA 95192-0180 http://www.engr.sjsu.edu/~fayad SJSU -- CmpE -- M.E. Fayad

  2. Lesson 09: The MLPQ System 2 SJSU -- CmpE -- M.E. Fayad

  3. Lesson Objectives • Understand the MLPQ System • Learn about: • Database System Architecture • MLPQ Input Files • MLPQ Graphical User Interface • Recursive Queries 3 SJSU -- CmpE -- M.E. Fayad

  4. MLPQ • MLPQ is short for Management of liner programming queries. • MLPQ is a constraint database system for rational linear constraint databases. • It allows: • Datalog Queries • Minimum and maximum aggregation operations over linear objective functions • And other operators 4 SJSU -- CmpE -- M.E. Fayad

  5. Two Main Application Areas • Operations research when the available data in a database needs to be reformulated by some database query before we can solve a problem by linear programming. • Dealing with spatial and spatiotemporal data. The MLPQ allows the ability to go beyond two or three dimensions of mutually constrained data. 5 SJSU -- CmpE -- M.E. Fayad

  6. System consists of six main modules: Representation Query Evaluation Visualization Approximation Update Export Conversion Refer to Chapter 18 for a diagram and more details The MLPQ Database System Architecture 6 SJSU -- CmpE -- M.E. Fayad

  7. MLPQ Input Files • Each Input File has this structure: begin %moduleName% 1 2 . . n end %moduleName% Where n is a Datalog Rule or rational linear constraint tuple. 7 SJSU -- CmpE -- M.E. Fayad

  8. Differences between Datalog and MLPQ Input File • Each Linear constraint has the form: a1x1 + a2x2 + … + anxn b Where each ai is a constant and each xi is a variable, and i is a relation operator of the from =, <, >, <=, or >=. • The optional aggregate operator has the from OP(f) where OP is one of the aggregate optators: max, min, MAX, MIN, sum_max, sum_min, and f is a liner function of the variables in the rule. 8 SJSU -- CmpE -- M.E. Fayad

  9. Differences ( continued ) • For the negation the symbol ! Is used instead of not. • The Module Name controls what type of query evaluation methods will be used. It should be one of these strings: • MLPQ– to evaluate only non-recursive Datalog Queries. • RECURSIVE – to evaluation recursive Datalog Queries • GIS – to evaluate both Datalog and iconic queries 9 SJSU -- CmpE -- M.E. Fayad

  10. Example Database file – regions.txt begin%Test% country(id,x,y,t):- id = 1, x >= 0, x <= 4, y >=5 , y <= 15, t >=1800 , t <=1950. country(id,x,y,t):- id = 1, x >= 0, x <= 8, y >=5, y <=15, t >=1950 , t <= 2000. country(id,x,y,t):- id = 2, x >= 4, x <= 12, y >=5 , y <=15 , t >= 1800, t <=1950 . country(id,x,y,t):- id = 2, x >= 8, x <= 12, y >=5 , y <=15 , t >= 1950, t <= 2000. country(id,x,y,t):- id = 3, x >= 0, x <= 12, y >=0 , y <=5 , t >= 1800, t <= 2000. location(c,x,y):- x = 3, y = 2, c = 101. location(c,x,y):- x = 7, y = 3, c = 102. location(c,x,y):- x = 5, y = 6, c = 103. location(c,x,y):- x = 7, y = 10, c = 104. location(c,x,y):- x = 10, y = 8, c = 105. location(c,x,y):- x = 1, y = 7, c = 106. location(c,x,y):- x = -8, y = 6, c = 107. growth(t,c,p):- c = 101, p = 10000 , t >=1800 , t <= 2000. growth(t,c,p):- c = 102, p = 20000 , t >=1800 , t <= 2000. growth(t,c,p):- c = 103, p = 10000 , t >=1800 , t <= 2000. growth(t,c,p):- c = 104, p = 30000 , t >=1800 , t <= 2000. growth(t,c,p):- c = 105, p = 40000 , t >=1800 , t <= 2000. growth(t,c,p):- c = 106, p = 35000 , t >=1800 , t <= 2000. end%Test% 10 SJSU -- CmpE -- M.E. Fayad

  11. The MLPQ Graphical User Interface 11 SJSU -- CmpE -- M.E. Fayad

  12. SQL Query • Find all cities that in 1900 belonged to the USA and had a population of over 10000. Click [Qs], click SQL - Basic button In Create View field enter: “cityUSA1900” In Select field enter: “growth.c, location.x, location.y” In From field enter: “growth, location, country” In Where field enter: “growth.c = location.c, location.x = country.x, location.y = country.y, growth.t = 1900, growth.p > 10000, country.id = 1, country.t = 1900” 12 SJSU -- CmpE -- M.E. Fayad

  13. Results 13 SJSU -- CmpE -- M.E. Fayad

  14. Many Operators 14 SJSU -- CmpE -- M.E. Fayad

  15. Recursive Queries • Recursive Datalog queries are entered the same way as non-recursive queries except that the module name is changed to RECURSIVE. This activates some special evaluation routines that are applicable for recursive queries only. SJSU -- CmpE -- M.E. Fayad

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