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The select operation selects tuples that satisfy a given predicate We use the lowercase Greek letter sigma ( ) to denote selection The predicate appears as a subscript to The argument relation is in parentheses after the Thus, to select those tuples of the loan relation where the branch is Perryridge, we write branch -name = Perryridge (loan) If the loan relation is as shown in Figure 36, then the relation that results from the preceding query is as shown in Figure 310 We can nd all tuples in which the amount lent is more than $1200 by writing amount>1200 (loan) In general, we allow comparisons using =, =, <, , >, in the selection predicate Furthermore, we can combine several predicates into a larger predicate by using the connectives and ( ), or ( ), and not ( ) Thus, to nd those tuples pertaining to loans of more than $1200 made by the Perryridge branch, we write branch-name = Perryridge amount>1200 (loan) loan-number L-15 L-16 Figure 310 branch-name amount Perryridge 1500 Perryridge 1300

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Social Security numbers are assigned by the federal government so the university does not have to assign them In other cases, primary values are assigned by an organization For example, customer numbers, product numbers, and employee numbers are typically assigned by the organization controlling the underlying database In these cases, automatic generation of unique values is required Some DBMSs support automatic generation of unique values as explained in Appendix 3C Entity Integrity Variations

Result of branch-name = Perryridge (loan)

The McGraw Hill Companies, 2001

$17,500

Candidate keys that are not primary keys are declared with the UNIQUE keyword The Course table (see Table 36) contains two candidate keys: CourseNo (primary key) and CrsDesc (course description) The CourseNo column is the primary key because it is more

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Classi cation deals with predicting the class of test instances, by using attributes of the test instances, based on attributes of training instances, and the actual class of training instances Classi cation can be used, for instance, to predict credit-worthiness levels of new applicants, or to predict the performance of applicants to a university There are several types of classi ers, such as Decision-tree classi ers These perform classi cation by constructing a tree based on training instances with leaves having class labels The tree is traversed for each test instance to nd a leaf, and the class of the leaf is the predicted class Several techniques are available to construct decision trees, most of them based on greedy heuristics Bayesian classi ers are simpler to construct than decision-tree classi ers, and work better in the case of missing/null attribute values Association rules identify items that co-occur frequently, for instance, items that tend to be bought by the same customer Correlations look for deviations from expected levels of association Other types of data mining include clustering, text mining, and data visualization Data warehouses help gather and archive important operational data Warehouses are used for decision support and analysis on historical data, for instance to predict trends Data cleansing from input data sources is often a major task in data warehousing Warehouse schemas tend to be multidimensional, involving one or a few very large fact tables and several much smaller dimension tables Information retrieval systems are used to store and query textual data such as documents They use a simpler data model than do database systems, but provide more powerful querying capabilities within the restricted model Queries attempt to locate documents that are of interest by specifying, for example, sets of keywords The query that a user has in mind usually cannot be stated precisely; hence, information-retrieval systems order answers on the basis of potential relevance Relevance ranking makes use of several types of information, such as: Term frequency: how important each term is to each document Inverse document frequency Site popularity Page rank and hub/authority rank are two ways to assign importance to sites on the basis of links to the site Similarity of documents is used to retrieve documents similar to an example document Synonyms and homonyms complicate the task of information retrieval.

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The McGraw Hill Companies, 2001

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