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Management Systems 200 > Chapter 8 > Flashcards

Chapter 8 Flashcards

1
Q

What is OLTP?

A
  • Online Trnsaction Processing (OLTP)
  • Collects data electronically and processes the transactions online.
  • The backbone of all functional, cross-functional, and inter-orgnaizational systems in an organization.
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2
Q

How does OLTP support decision making?

A

System supports decision making by providing the raw information about transactions and status for organization.

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3
Q

What are 2 different models of transaction Processing?

A
  1. Real-time Processing
  2. Batch Processing
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4
Q

Explain Real-time Processing.

A

Transactions are entered and processed immediately upon entry.

(Ex. Airline reservation system, Banking System)

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5
Q

Explain Batch Processing.

A

System waits until it has a batch of transactions before the data is processed and info is updated.

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6
Q

What is OLAP?

A
  • Oline Analytical Processing (OLAP)
  • Focuses on making OLTP-collected data useful for decision making.
  • Able to perform arithmetic operations on groups of data.
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7
Q

Define a Business Intelligence System.

A

System provides info for improving decision making.

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8
Q

List the four categories of BI systems.

A
  1. Reporting Systems
  2. Data-mining Systems
  3. Knowledge-management (KM) Systems
  4. Expert Systems
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9
Q

Explain Reporting Systems.

A
  • Integrate data from multiple sources.
  • Process data by sorting, grouping, summing, averaging, and comparing.
  • Format results into reports
  • Improve decision making by providing right info to right user at right time.
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10
Q

Explain Data-Mining Systems.

A
  • Process data using aophisticated statistical techniques. (Regression & Tree diagram analysis).
  • Looks for patterns & relationships to anticipate events or predicts future outcomes. (Market Basket analysis; Predict Donations).
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11
Q

Explain Knowledge-Management Systems.

A
  • Create value from intellectual capital
  • Collect & share human knowledge
  • Supported by the 5 components of Information Systems.
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12
Q

Explain Expert Systems.

A
  • Encapsulates the knowledge of human experts in the form of IF/THEN rules.
    • ex, IF condition is true, THEN initiate procedure.
  • Improve diagnosis & decision making.
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13
Q

What is RFM analysis and what are its components?

A

A way of analyzing & ranking customers according to their purchasing patterns.

Technique Considers:

  • How recently (R) a customer has ordered.
  • How frequently (F) a customer orders.
  • How much money (M) the customer spend per order.
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14
Q

What is a Data Warehouse?

A

System that is used to clean & extract data from operational systems and other sources.

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15
Q

What is the purpose of a Data Warehouse, and what does it store in its DBMS?

A
  • Prepares data for BI processing
  • Data-Warehouse DBMS:
    • Stores data
    • May also include data from external sources
    • Metadata concerning data stored in data-warehouse meta database.
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16
Q

What is a Data Mart?

A
  • A data collection created to address particular needs such as; Business functions, problems, oportunities.
17
Q

How does a Data Mart Differ from a Data-Warehouse?

A
  • Smaller than data-warehouse.
  • Addresses a particular component of functional area of the business.
18
Q

Define Data Mining.

A

The application of statistical techniques to find patterns & relationships among data to classify & predict.

19
Q

What are the 2 types of data mining?

A
  1. Unsupervised Data Mining.
  2. Supervised Data Mining.
20
Q

What is Unsupervised Data Mining?

A
  • Analysts do not create model or hypothesis before running the analysis.
  • Hypothesis is created after analysis as explination fro results.
  • ex, Cluster Analysis: Identifying groups of entities that have similar characteristics.
21
Q

What is Supersived Data-Mining?

A
  • Model developed before analysis.
  • Statistical techniques applied to data to estimate parameter of the model.
  • ex, Regression Analysis: Measures the impact of a set of variables on another variable.
  • ex, Neutral Networks: Used to predicts values & make classifications, such as “Good Prospect” or “Poor Prospect” customers.

Management Systems 200 (7 decks)

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