Exam (elaborations)
BUS M31 Ch. 17
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Exam of 5 pages for the course Body systems at Body systems (BUS M31 Ch. 17)
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BUS M31 Ch. 17Acquisition cost - ANS-the cost of obtaining data that you don't have
Association or affinity patterns - ANS-when two or more database elements tend to
occur together in a significant way
Authentication - ANS-making sure potential users are who they claim to be
Authorization - ANS-granting authenticated users approved access to data, software,
and systems
Bar code - ANS-a visual pattern that represents numerical data by varying the thickness
and pattern of vertical bars
Communication cost - ANS-the cost of transmitting information from one place to
another
Corporate portal - ANS-a hybrid of executive information systems and intranets that
allows managers and employees to use a web browser to gain access to customized
company information and to complete specialized transactions
Data clusters - ANS-when three or more database elements occur together (that is,
cluster) in a significant way
Data encryption - ANS-the transformation of data into complex, scrambled digital codes
that can be decrypted only by authorized users who possess unique decryption keys
Data mining - ANS-the process of discovering unknown patterns and relationships in
large amounts of data
Data warehouse - ANS-a database that stores huge amounts of data that have been
prepared for data mining analysis by being cleaned of errors and redundancy
Decision support system (DSS) - ANS-an information system that helps managers
understand specific kinds of problems and potential solutions
Describe how companies can access and share information and knowledge -
ANS-Executive information systems, intranets, and corporate portals facilitate internal
, sharing and access to company information and transactions. Electronic data
interchange and the Internet allow external groups such as suppliers and customers to
easily access company information. Both decrease costs by reducing or eliminating
data entry, data errors, and paperwork and by speeding up communication.
Organizations use decision support systems and expert systems to capture and share
specialized knowledge with nonexpert employees.
Describe the characteristics of useful information (that is, its value and costs) -
ANS-Raw data are facts and figures. Raw data do not become information until they are
in a form that can affect decisions and behavior. For information to be useful, it has to
be reliable and valid (accurate), of sufficient quantity (complete), pertinent to the
problems you're facing (relevant), and available when you need it (timely). Useful
information is not cheap. The five costs of obtaining good information are the costs of
acquiring, processing, storing, retrieving, and communicating information.
Electronic data interchange (EDI) - ANS-when two companies convert their purchase
and ordering information to a standardized format to enable the direct electronic
transmission of that information from one company's computer system to the other
company's computer system
Electronic scanner - ANS-an electronic device that converts printed text and pictures
into digital images
Executive information system (EIS) - ANS-a data processing system that uses internal
and external data sources to provide the information needed to monitor and analyze
organizational performance
Expert system - ANS-an information system that contains the specialized knowledge
and decision rules used by experts and experienced decision makers so that
nonexperts can draw on this knowledge base to make decisions
Explain the basics of capturing, processing, and protecting information - ANS-Electronic
data capture (using bar codes, radio frequency identification [RFID] tags, scanners, or
optical character recognition), is much faster, easier, and cheaper than manual data
capture. Processing information means transforming raw data into meaningful
information that can be applied to business decision making. Data mining helps
managers with this transformation by discovering unknown patterns and relationships in
data. Supervised data mining looks for patterns specified by managers, while
unsupervised data mining looks for four general kinds of data patterns: association or
affinity patterns, sequence patterns, predictive patterns, and data clusters. Protecting
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