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Quantification in SQL
To quantify something, as the theory book has it, is to state its quantity, to say how many of it there are. For example, in Tutorial D the expression COUNT(r) denotes the number of tuples in the relation r, to be interpreted as the number of objects represented by those tuples that satisfy a predicate that r is considered to represent. Universal quantification-stating that something is true of all objects under consideration-is involved in expressions such as
UTL_FILE: The Package UTL_FILE permits your PL/SQL programs to read & write operating system (OS) text files. It gives a restricted version of the standard OS stream file I/O,
Row Operators The Row operators return or reference the particular rows. ALL retains the duplicate rows in the result of a query or in an aggregate expression. The DISTINCT el
Example of Check Constraints Example: Workaround for when subqueries not permitted in CHECK constraints CREATE FUNCTION NO_MORE_THAN_20000_ENROLMENTS ( ) RETURNS BOOLEAN
Updating by replacement Syntax: UPDATE ENROLMENT SET Name = 'Ann' WHERE StudentId = SID ('S1'); Note the use of SET, as already noted in connection with direct a
Extension and AND in SQL The theory book gives the following simple example of relational extension in Tutorial D: EXTEND IS_CALLED ADD ( FirstLetter ( Name ) AS Initial )
Project Description: I am looking to change FullCalendar to add/delete sql server data when events are removed or dropped from Calendar. Events should only be included by dra
ORIGIN OF EARTH - BIG-BAN G HYPOTHESIS - Origin of life is linked to origin of earth. Cosmos, the Universe originated 10-20 billion years ago by Big Bang (thermonu
Logical Operators The logical operators AND, NOT, and OR follow the tri-state logic shown in table below. The AND and OR are binary operators; NOT is a unary operator.
BETWEEN Operator The operator BETWEEN, tests whether the value lies in a specified series. That means "greater than or equivalent to low value and less than or equivalent to hig
Data Abstraction The Data abstraction extracts the important properties of data while ignoring the not necessary details. Once you design a data structure, you can fail to reme
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