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BUILDING A RELATIONAL DATABASE FROM THE DATA MODEL The data model comprises the conceptual schema, or the description of the structure of the database This is one of three schemas, or designs that database developers refer to The other schemas include the external schema, which is the database as conceived by the end-users, and the internal schema, which is the set of actual file structures on disk used by the database management system (SQL Server, Oracle, etc) With the conceptual schema created, the next task is to convert the data model into tables, relationships, and data integrity rules An entity type is represented as a table, where each row in the table represents an instance of that entity type In relational database terminology, a table is called a relation Note that a relation is a table, not a relationship Later we will also create means to represent relationships.

CHAP. 11]

Execute hEx10_5.CIRi and use the Probe feature of PSpice to plot the waveforms of Fig. 10-9.

Fig. 10-9

19 20 21 22 23 24 25 26 27 28 29 30 31 32

A relation consists of rows, each of which represents an instance of the entity type, and of columns, each of which represents one of the attributes of the entity type Each row of a relation is called a tuple Practitioners use the word row more often than tuple, but a tuple is a row of a table, an instance of a relation, an instance of an entity type Each tuple consists of the values of the attributes for that instance of the entity type Another word for attribute is field So, in discussions of relational databases, you must keep in mind these synonyms: relation and table, tuple and row, attribute and field The first step is to create a relation for each strong entity in the data model Each of the attributes of the entity type in the data model will become a column name in the relation.

The following netlist code describes the ampli er circuit:

for (it = tree.new InOrder(); it.hasNext(); ) { System.out.print(it.next() + " "); } System.out.print("\nPostOrder Traversal: "); for (it = tree.new PostOrder(); it.hasNext(); ) { System.out.print(it.next() + " "); } System.out.print("\nLevelOrder Traversal: "); for (it = tree.new LevelOrder(); it.hasNext(); ) { System.out.print(it.next() + " "); } System.out.println(); } }

Prb8_21.CIR vi 1 0 AC 0.25V CC1 1 2 15uF RS 2 0 2kohm J 3 4 2 NJFET .MODEL NJFET NJF( Vto=-4V Beta=0.0005ApVsq + Rd=1ohm Rs=1ohm CGS=2pF CGD=2pF) R1 4 5 10kohm R2 4 0 10kohm RD 3 5 500ohm VDD 5 0 15V CC2 3 6 15uF RL 6 0 15kohm .AC DEC 100 10Hz 50MegHz .PROBE .END

The output is:

At this time one must choose an attribute, or set of attributes, called a primary key, which will uniquely identify each row of the relation The ideal key is short, numeric, and never-changing The ideal is not always possible to achieve, but it can be helpful to keep the ideal in mind when choosing a key For instance, if the Student table includes the attributes of name, address, and social security number (SSN), in addition to other attributes, one could probably choose the combination name address, or the single attribute SSN, to uniquely identify students Choosing SSN would be wiser, because SSN will be more efficient to process, due to its numeric type, and it will change even less frequently than a name or an address Sometimes there is no obviously good key attribute among the attributes of the table.

Execute hPrb8_21.CIRi and use the Probe feature of PSpice to yield the gain magnitude plot of Fig. 8-28. The marked points show the cuto frequencies to be fL 36:4 Hz and fH 9:9 MHz.

tree = [A, B, D, G, E, C, PreOrder Traversal: A B InOrder Traversal: D G PostOrder Traversal: G D LevelOrder Traversal: A B F, H, D G E B E A E B H C D E I] C F H F I F F G H I C H I C A I

Fig. 8-28

The following algorithm shows how a forest can be represented by a single binary tree. Algorithm 11.8 The Natural Mapping of a Forest into a Binary Tree 1. Map the root of the first tree into the root of the binary tree. 2. If node X maps into X' and node Y is the first child of X, then map Y into the left child of X'. 3. If node X maps into X' and node Z is the sibling of X, then map Z into the right child of X'. The roots of the trees themselves are considered siblings.