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Databases

DATABASES

Databases are designed to manipulate large amounts of information by inputting, storing, retrieving, and managing that information. Databases use a table format, with Microsoft Access being one of the most widely used.

Databases consist of rows and columns. Each piece of information is entered into a row, which creates a "record." Databases are commonly used when saving addresses or other types of long lists of information. Once the records are created in the database, they can be sorted and manipulated in a variety of ways that are limited primarily by the software being used.

The word data is normally defined as facts from which information can be derived. For example, "Fred Crouse lives at 2209 Maple Avenue" is a fact. A database may contain millions of such facts. From these facts the database management system (DBMS) can derive information in the form of answers to questions such as "How many people live on Maple Avenue?" The popularity of databases in business is a direct result of the power of DBMSs in deriving valuable business information from large collections of data.

Databases are somewhat similar to spreadsheets, but databases are more powerful than spreadsheets because of their ability to manipulate the data. It is possible to do a number of functions with a database that would be more difficult to do with a spreadsheet. Consider these actions that are possible to do with a database:

  • Perform a variety of cross-referencing activities
  • Complete complicated calculations
  • Bring current records up to date
  • Retrieve large amounts of information that match certain criteria

RELATIONAL DATABASES

Most modern databases are relational, meaning that data are stored in tables, consisting of rows and columns, and that data in different tables are related by the meanings of certain common columns. (The tables in a database are sometimes called files, the rows are called records, and the columns are called fields. Nevertheless, this is an older terminology, left over from the early days of business computer systems.) The following is an example of a simple relational database consisting of three tables: one for customers, one for products, and one for sales:

Customers
customer_no name address phone
1001 Jones 320 Main 555-8811
1002 Smith 401 Oak 555-8822
1003 Brown 211 Elm 555-8833
1004 Green 899 Maple 555-8844
Products
product_no description price
25 Ring 3.25
33 Gasket 1.23
45 Shaft 4.55
Sales
sale_no date customer_no product_no
841 3/11 1002 45
842 3/12 1001 25
843 3/12 1002 45
844 3/13 1004 33
845 3/14 1003 25
846 3/15 1002 33

Suppose one wants to know the customer's name for sale number 845. Looking in the customer number column of the Sales table, one will see that it was customer 1003. Next, one refers to the Customers table and finds customer 1003. Here one see the customer's name is Brown. So, Brown was the customer for sale number 845.

STRUCTURED QUERY LANGUAGE

The foregoing is a simple example of a database query. In a modern database, queries are expressed in a query language, which requires a particular format that can be recognized and interpreted by the DBMS. The standard query language for relational databases, as adopted by the American National Standards Institute (ANSI), is SQL, which is generally understood to be an abbreviation for "structured query language." Here are a few examples of queries expressed in SQL:

Query: Which products have a price over $2?
SQL solution: Select product_no, description
From Products
Where price > 2.00
Result: product_no description
25 Ring
45 Shaft

This query's SQL solution illustrates the SQL format. In general, SQL "statements" have a Select "clause," a From "clause," and a Where "clause." The Select clause lists the columns that are to be shown in the result, the From clause lists the database tables from which data are to be taken, and the Where clause gives the condition to be applied to each row in the table. If a row satisfies the condition, then it is selected, and the values in that row for the columns listed in the Select clause are included in the result.

Query: When have we sold product number 45 to customer 1002?
SQL solution: Select date
From Sales
Where product_no = 45 and customer_no = 1002
Result: date
3/11
3/12

In this example one can see that the condition in the Where clause includes the connector "and," which indicates that both conditions (product_no = 45 and customer_no = 1002) must be fulfilled. In the sample database there are two rows that satisfy this condition, and the query's result yields the dates from those two rows.

The next query gives the SQL solution to the original query discussed above.

Query: What is the customer's name for sale number 845?
SQL solution:
Select name
From Customers, Sales
Where sale_no = 845 and Sales.customer_no =
Customers.customer_no
Result: Brown

This query illustrates how one can query more than one table at once in SQL. First, one lists all tables needed to answer the query. In this case then, one lists the Customers and the Sales tables. Then in the Where clause, one states two conditions:

sale_no = 845 and Sales.customer_no = Customers.customer_no

The first condition indicates that the sale_no column must have a value of 845. Because there is only one row in the Sales table having that value, one has limited one's query to that single row. The second condition indicates that one wants only that row in the Customers table which has the same value for its customer_no column as the Sales row has for its customer_no column. This condition then limits one's result to the joining together of one row from the Sales table and one row from the Customers table. Finally, the Select clause,

Select name tells one that one should give the value from the name column as one's result. As shown before, the resulting customer name is "Brown."

Queries can also be used to perform calculations:

Query: What is the average price of our products?
SQL solution: Select Avg (price)
From Products
Result: 3.01

SQL also provides statements that can be used to make changes to data in the database. For example, suppose one wanted to increase the price of one's products by 3 percent. Then the following statement can be used:

Update Products
Set price = 1.03 * price

This statement will cause the price of every product in the Products table to be increased by 3 percent. Note that it does not matter whether one has 3 products, as shown in the sample database, or 300,000 products. A single statement will update the prices of all products. Of course, if one wants to change only the prices of selected products, one can do that, too:

Update Products
Set price = 1.03 * price
Where product_no = 33

This statement will change only the price of product number 33. SQL also provides statements to Insert new rows into tables and to Delete rows from tables.

These queries show only a very small number of the capabilities of SQL. The Where clause can be used to select rows based on where names are in the alphabet, whether dates are before or after certain other dates, based on averages, and based on many other conditions.

SMALL AND LARGE DATABASES

Databases can be single-user or multiuser. A single-user database exists on a single computer and is accessible only from that computer. Many single-user databases exist, and there are a number of commercial database manufacturers that address this market. A multiuser database may exist on a single machine, such as a mainframe or other powerful computer, or it may be distributed and exist on multiple computers. Multiuser databases are accessible from multiple computers simultaneously.

With the rise of the Internet, many databases are publicly accessible. For example, the holdings of university libraries are maintained on databases that can be browsed from remote locations. A person interested in locating a book in a library can enter the book's title, author, or subject, and a database query will be automatically performed. Information on the desired book or list of books will be returned to the person's computer.

SELECTING A DATABASE SYSTEM

A person or business seeking to purchase a DBMS for use in managing a database should consider the following factors:

Relational:

Virtually all major commercial DBMSs are relational, because the desirability of relational databases is well-accepted in the database community.

SQL:

In addition, because the ANSI has adopted SQL as it standard for relational databases, the desired DBMS should support SQL.

Capacity:

As noted above, DBMSs are designed for a variety of environments. Some are designed to be single-user systems, while others are designed for medium-sized businesses, while still others are designed for large businesses. The system selected should naturally be one that has been shown to be successful in and appropriate for the environment for which it is chosen.

Disaster recovery capability:

More sophisticated systems are more capable of recovering from power outages, computer hardware failure, and the like than are the single-user systems. They use sophisticated logging and database locking facilities that make such recovery possible. Often, these facilities are unnecessary for single-user systems.

SUMMARY

Databases and DBMSs are central to modern business information systems. Relational databases using SQL provide substantial logical power to help businesses make informed decisions based on their own data. Database systems can be small and handled by a single user, or they can be large and available to multiple users. They are even publicly available through the Internet. DBMSs can be sophisticated and expensive, and consequently their purchase requires careful, informed consideration.

see also Information Technology; Software

bibliography

About Databases Guide Site. http://databases.about.com

Dunham, Jeff (1998). Database performance tuning handbook. New York: McGraw-Hill.

Groff, James R., and Weinberg, Paul N. (2002). SQL: The complete reference (2nd ed.). Berkeley, CA: Osborne/McGraw-Hill.

Hansen, Gary W., and Hansen, James V. (1996). Database management and design (2nd ed.). Upper Saddle River, NJ: Prentice Hall.

Kroenke, David M. (2006). Database processing: Fundamentals, design, and implementation (10th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

Post, Gerald V. (2005). Database management systems: Designing and building business applications (3rd ed.). Boston: McGraw-Hill/Irwin.

Rob, Peter, and Semaan, Elie (2004). Databases: Design, development and deployment (2nd ed.). Boston: McGraw-Hill.

Dorothy Maxwell

Gary Hansen

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NDIS, FBI Database

NDIS, FBI Database

The National DNA Index System, or NDIS, is a United States Federal Bureau of Investigation (FBI ) DNA database that facilities the electronic comparison and exchange of DNA profiles between participating local, county, state, and federal law enforcement agencies and forensic laboratories. First made operational in 1998, the NDIS is a highly valued instrument that is used by law enforcement professionals in order to better coordinate and communicate information related to serial violent crimes committed across the United States. Authorization to establish the NDIS came about from the DNA Identification Act of 1994.

The NDIS is a critical component of the Combined DNA Index System (CODIS ), an FBI software support program developed in 1990, which uses DNA (deoxyribonucleic acid) technology to generate leads in crimes where forensic evidence is recovered from crime scenes. In its role, the NDIS enables participating organizations to compare DNA profiles on a national level in order to more efficiently investigate crimes. Managed by the FBI as the nation's DNA database, DNA profiles typically are generated at the local level, transferred to state and national levels, and uploaded electronically through the Internet at the state level to the NDIS. At this point, the data is compared to determine if a convicted offender can be associated with a previous or current crime, or if two or more crimes can be joined together.

An actual example that shows how the NDIS works involves the unsolved (and previously unconnected) rape and murder cases of a college professor in Flint, Michigan, in 1986; and of a flight attendant in Romulus, Michigan, in 1991. With access to CODIS in 2001, Michigan State Police submitted DNA from the 1986 case to the NDIS. When the sample was matched with DNA from the 1991 case, latent fingerprints from the 1986 case were sent to the FBI's Latent Fingerprint Unit. While searching through the FBI's Integrated Automated Fingerprint Identification System (IAFIS), one of the prints was identified. Based on this information, the Flint Police Department followed the suspect, recovered a restaurant napkin used by the suspect, and after the material found on the napkin was forensically matched with evidence left at both homicide scenes, the suspect was arrested and charged with murder.

From its beginnings on October 13, 1998, to today, the NDIS has gained participants and now includes over 130 federal, state, and local laboratories representing all fifty states, the District of Columbia (the FBI Laboratory), Puerto Rico, and the U.S. Army. On June 12, 2002, the NDIS achieved a major milestone when the Florida Department of Law Enforcement contributed the one millionth DNA profile to the program. As of December 2004, the total number of DNA profiles within the NDIS is 2,132,470; the total number of convicted offender profiles is 2,038,470; and the total number of forensic profiles is 93,956.

see also CODIS: Combined DNA Index System; DNA; DNA databanks; DNA profiling; FBI (United States Federal Bureau of Investigation); FBI crime laboratory; Integrated automated fingerprint identification system; Serial killers.

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Global Database on National Nutrition Policies and Programmes

Global Database on National Nutrition Policies and Programmes

Hunger and malnutrition occur throughout the world, though the knowledge and resources exist to eliminate them. The challenge lies in changing political will, developing realistic policies, and taking determined actions both nationally and internationally. These are the basic beliefs of the Global Database on National Nutrition Policies and Programmes (GDNNPP). GDNNPP was created by the World Health Organization (WHO) in 1995 to monitor and evaluate the progress of implementation of the 1992 World Declaration and Plan of Action for Nutrition, which states that all people should have access to safe and nutritious food and be free from hunger.

GDNNPP plays a large role in improving nutrition status globally by compiling data from six regions of the world: Africa, the Americas (in conjunction with the Pan-American Health Organization), the Eastern Mediterranean, Europe, South-East Asia, and the Western Pacific. Policies and programs vary from country to country according to population needs.

GDNNPP provides a global review and comparative analysis of national nutrition policies and plans of action. It identifies the priority nutrition issues of various countries, as well as key elements for developing and implementing effective and sustainable nutrition policies and programs. It also evaluates each country's progress in developing, strengthening, and implementing national nutrition policies and programs, and it serves as a guide to creating better national nutrition policies and programs through authoritative standards and guidelines, research, and collaboration. GDNNPP is designed to help enforce the health objectives, strategies, and activities of the WHO, which also provides technical and financial support to participating WHO countries.

Delores C. S. James

Internet Resources

Pan American Health Organization. "Nutrition and Food Protection: Current Health Topics." Available from <http://www.paho.org>

World Health Organization. "Global Database on National Nutrition Policies and Programmes." Available from <http://www.who.int/nut_pol.htm>

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database management system

database management system (DBMS or dbms) A software system that provides comprehensive facilities for the organization and management of a body of information required for some particular application or group of related applications. This implies some overall logical view of the database in terms of some particular data model, and database management systems are conventionally categorized according to the data model they implement. The system will provide a database language in which schemas and subschemas (user views) can be specified and retrieval and update programs written. There will be facilities to specify and modify the storage schema, for logging, rollback, and recovery. A major objective of a DBMS is to provide data independence within the constraints of its data model. More modern DBMS provide a higher degree of data independence than earlier products where significant schema modification requires the database system to cease to be operational and for the data to be reorganized and reloaded. This can be impractical for large operational systems, a main reason for them becoming out of date and requiring redevelopment.

Well-known DBMS include the relational database systems ORACLE, INGRES, SYBASE, and INFORMIX, and the earlier systems IMS, IDMS, and ADABAS, still widely used in practice. Some of these products have versions with more limited facilities for the PC environment, for which specialist products such as Access and dBASE IV have also been developed. See also object-oriented database.

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database

database
1. Normally and strictly, a body of information held within a computer system using the facilities of a database management system. All accessing and updating of the information will be via the facilities provided by this software as will be the recording of information on the log file, database recovery, and multiaccess control.

2. Occasionally and colloquially, a collection of data on some subject however defined, accessed, and stored within a computer system. (This nontechnical use of the term can cause confusion to the nonspecialist as in: “we don't use a database management system for our database, just straightforward VSAM files.”)

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databank

databank A system that offers facilities for the deposit and withdrawal of data to a community of users on a particular topic (e.g. biological species, trade statistics, commodity prices). While it need not be an open public facility, the usual implication is that the user community is widespread. Access to a databank may be, for instance, via a videotex facility, or via any other form of network, or even via the postal service. The data itself may be organized as a database or as one or more files.

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database

database Collection of data produced and retrieved by computer. The data is usually stored on magnetic disk or tape. A database program enables the computer to generate files of data and later search for and retrieve specific items or groups of items. For example, a library database system can list, on screen, all the books on a particular subject and can then display further details of any selected book.

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database

da·ta·base / ˈdatəˌbās; ˈdā-/ • n. a structured set of data held in a computer, esp. one that is accessible in various ways.

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databank

databankankh, bank, blank, clank, crank, dank, drank, embank, flank, franc, frank, hank, lank, outflank, outrank, Planck, plank, point-blank, prank, rank, sank, shank, shrank, spank, stank, swank, tank, thank, wank, yank •sandbank • piggy bank • mountebank •fog bank • mudbank • Bundesbank •databank • riverbank • Burbank •greenshank • sheepshank •scrimshank • Cruikshank •think tank • Franck • Eysenck •bethink, blink, brink, chink, cinque, clink, dink, drink, fink, Frink, gink, ink, interlink, jink, kink, link, mink, pink, plink, prink, rink, shrink, sink, skink, slink, stink, sync, think, wink, zinc •rinky-dink • Humperdinck • iceblink •cufflink • bobolink • Maeterlinck •lip-sync • countersink • doublethink •kiddiewink •tiddlywink (US tiddledywink) •hoodwink

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database

databaseabase, ace, apace, backspace, base, bass, brace, case, chase, dace, efface, embrace, encase, enchase, enlace, face, grace, interlace, interspace, in-your-face, lace, mace, misplace, outface, outpace, pace, place, plaice, race, space, Thrace, trace, upper case •airbase • freebase • wheelbase •database • steeplechase • paperchase •paleface • typeface • whiteface •boldface • coalface • interface •staircase • briefcase • slipcase •packing case • doorcase • showcase •notecase • pillowcase • suitcase •bookcase • nutcase • marketplace •anyplace • everyplace • showplace •shoelace • bootlace • someplace •Lovelace • fireplace • commonplace •workplace • birthplace • tenace •airspace • aerospace • hyperspace •carapace • workspace • ratrace •millrace • Fuentes • rosace

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