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tuples and attributes Second, each tuple must be uniquely identi able by the data within the tuple some sort of primary key data (for example, a Social Security number or employee number) Third, each table must have a unique identi er the name of the relation Fourth, there can be no duplicate attributes or tuples Finally, there can be only one value in each row-column cell in a table In a relational database, three basic operations are used to develop useful sets of data: select, join, and project The select operation creates a subset consisting of all records in the le that meet stated criteria Select creates, in other words, a subset of rows that meet certain criteria The join operation combines relational tables to provide the user with more information than is available in individual tables.

Z((n 1)T / n) =

Figure 3-10: Range B3:B8 in this worksheet is named MyData. Cell D5 demonstrates an implicit intersection.

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The project operation creates a subset consisting of columns in a table, permitting the user to create new tables that contain only the information required One of the greatest advantages of the relational model is its conceptual simplicity and the ability to link records in a way that is not prede ned (that is, they are not explicit as in the hierarchical and network models) This ability provides great exibility, particularly for end users The relational or tabular model of data can be used in a variety of applications Most people can easily visualize the relational model as a table, but the model does use some unfamiliar terminology Consider the relational database example on East Coast managers shown in Figure T-38 The table contains data about the entity called East Coast managers Attributes or characteristics about the entity are name, title, age, and division.

Originally, Don Quixote requested that Rueful Figures, Inc., staff members be grouped according to Table 3.1. Example 3.2 illustrates this simultaneous user account creation with group account assignment. Freston has complied and created the groups. Here is how he created knights1:

for(i = 0; i < con g->_dim; i++ ) for(j = 0; j < d[i]; j++ ) con g->v[i][j] = 1; for( i = 0; i < con g->_dim; i++ ) { for( j = d[i]; j < VMAX; j++ ) { con g->v[i][j] = con g->v[i][j-d[i]]; save = POLY[i]; m = pow( 2, d[i] ); for( k = d[i]; k > 0; k-- ) { con g->v[i][j] = con g->v[i][j] ^ m*(save%2)*con g->v[i][j-k];

The methodology of maximum load curve atness at constant output power allows calculation under a set of constraints. The major consumer interest is to decrease demand during the peak period so as to reduce the cost of the energy consumed. On the other side, from a utility company point of view, there is, in addition to reduction in the maximum demand, a possibility of increasing the ef ciency of the distribution network, due, for example, to a decrease in overall losses. The optimization rationale is based on limitations of load curve changes. Load can only be decreased during the load peak period (say, three hours) and can increase with the use of an electrolyzer only during nightly runs (say, a six-hour period). With this purpose, atness of the load curve can be quanti ed by a form factor de ned for every distribution transformer as [16] kf Pqav Pav 7:9

The tuples, or occurrences of the entity, are the two records on A Smith and W Jones The links among the data, and among tables, are implicit, as they are not necessarily physically linked in a storage device but are implicitly linked by the design of the tables into rows and columns This property of implicit links provides perhaps the strongest bene t of the relational model exibility in relating data Unlike the hierarchical and network models, where the only links are those rigidly built into the design, all the data within a table and between tables can be linked, related, and compared This ability gives the relational model much more data independence than the hierarchical and network models That is, the logical design of data into tables can be more independent of the physical implementation This independence allows much more exibility in implementing and modifying the logical design.

When you create a PivotTable, you may find it useful to expand your analysis by performing calculations on summary totals. To demonstrate this, create the PivotTable shown in Figure 4-28. This analysis calculates total revenue and total count of transactions. Upon reviewing these results, you determine that you need to get an average dollar per transaction. Go to the Design tab and select Formulas Create Calculated Total. This sets off two events. First, a new field called New Total appears in your PivotTable as shown in Figure 4-29. Second, the Properties dialog box for this field activates.

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