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HIT334

HIT334. Week 2. Some Definitions. Set : A collection of objects that contain NO duplicates Relation : Table, a set of tuples Tuple : Row in a table, a collection of attributes Attribute : Column Heading, an actual role played by Domain

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HIT334

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  1. HIT334 Week 2

  2. Some Definitions • Set: A collection of objects that contain NO duplicates • Relation: Table, a set of tuples • Tuple: Row in a table, a collection of attributes • Attribute: Column Heading, an actual role played by Domain • Domain: A Data type describing the types of values that appear in each column

  3. Relation Algebra • Proposed by Codd as an algebra on sets of tuples. • Originally consisted of: • Union, Set, Difference, Projection, Selection • Later expanded to include joins • Original Relational DBs query language, it underpins SQL

  4. PROJECTION -  Projection:Used to produce, from relation R, a new relation that has only some of R’s attributes. Denoted as  A1,A2..AnR , which gives a relation that only has the columns for attributes A1, A2…An For Example:  title, year, studio MOVIE =

  5. Projection  inColour Movie = • Note: No duplicates because: • In the relational algebra of sets, duplicate tuples are always eliminated

  6. SELECTION -  •  : The selection operator, when applied to a relation, produces a new relation with a subset of R’s tuples, based on some condition • Denoted as: <select condition> R • length > 100 MOVIE

  7. SELECTION -  • length > 100 AND studioName = ‘Fox’ (MOVIE) • Combining them… • title, year, studio(length > 100 AND studioName = ‘Fox’(MOVIE))

  8. title, year, studio(length > 100 AND studioName = ‘Fox’ (MOVIE)) Instead of nesting, you can explicitly show a sequence of operations: TEMP <-  length > 100 AND studioName = ‘Fox’(MOVIE)) RESULT<-  title, year, studio(TEMP)

  9. Mapping back to SQL would give: SELECT title, year, studio FROM movie WHERE length > 100 AND studioname = ‘FOX’ • Note that is not the same as sql “SELECT”

  10. Cartesian Product • Cross Product or Product • Denoted as X e.g R X S • Pairing of each tuple with each tuple from S.

  11. Cartesian Product Relation R Relation S R X S =

  12. To find the tuples that agree on the value in column B : TEMP <- R X S RESULT <- σR.B = R.S TEMP

  13. Natural Join • R <join condition> S • R R.B = S.B S • The relation that results from a JOIN operation has • One tuple for each combination of tuples from R & S that satisfy the join condition • A Number of attributes equal to the number of attributes in R plus the number of attributes in S

  14. RENAME • s(X,C,D) (S) • Produces a relation S, that has attributes called X, C and D, instead of B, C and D • When we then take the product R x S, there is no naming conflict. Alternatively: • R X S(X,C,D) (S) => RS(A,B,X,C,D) •  RS(A,B,X,C,D) (R X S) => RS(A,B,X,C,D)

  15. RECAP • PROJECT:  A1,A2..AnR • SELECT: <select condition> R • CROSS PRODUCT: R X S • JOIN: R <join condition> S • RENAME: if R(A1,A2..An) •  S(B1,B2..Bn) R •  S R •  (B1,B2..Bn) R

  16. To Retrieve the manager of each Department: • Combine each department with each employee whose SSN matches the MGRSSN value in the Department tuple: DEPT_MGR  DEPARTMENT MGRSSN=SSN (EMPLOYEE) • Project the result over the required attributes: RESULT  DNAME,LNAME,FNAME(DEPT_MGR) What if you only wanted the Manger for the Administration Department?

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