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Exam > Adbase H2 Midterms > Flashcards

Adbase H2 Midterms Flashcards

1
Q

warehouse is a database designed to enable and support business intelligence (BI) activities, especially analytics.
 intended to perform queries and analysis
 optimized for data retrieval, not for transaction processing
 centralizes and consolidates large amounts of data from multiple sources
 allows organizations to derive valuable business insights from their data to improve decision-making
 can be considered an organization’s “single source of truth”

A

data warehouse

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2
Q

The DW can analyze data about a particular subject or functional area.
 Subjects can be products, customers, departments, regions, etc.
 The functional area can be sales, marketing, finance, distribution, etc.
 Focuses on the data rather than on the processes that modify the data

A

Subject-Oriented

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3
Q

The DW creates consistency among different data types from different sources.

A

Integrated

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4
Q

A student’s level in the database might be defined as “freshman”, “sophomore”, “junior”, or “senior” in the accounting department, and “FR”, “SO”, “JR”, “SR” in the computer information systems department.

A

Integrated

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5
Q

Data in DW represents the flow of data through time. It can be organized weekly, monthly, or annually, etc.

A

Time-variant

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6
Q

Once data is in a data warehouse, it is stable and does not change.

A

Non-Volatile

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7
Q

This is a databank that stocks all enterprise data and makes it manageable for reporting.

A

Data Warehouse Database

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8
Q

 always implemented on the relational database management system (RDBMS) technology like SQL

A

Data Warehouse Database

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9
Q

These tools are used for performing all the conversions, summarizations, and all the changes needed to transform data into a unified format in the data warehouse. These include:
 In case of missing data, populating them with defaults
 Calculating summaries and derived data
 Eliminating unwanted data in operational databases from loading into the data warehouse
 Converting to common data names and definitions

A
  • Extraction, Transformation, and Loading Tools (ETL)
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10
Q

is data about data that describes the data warehouse. It provides the source, transformation, integration, storage, usage, relationships, and history of each data element.

A

Metadata

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11
Q

contains information about the warehouse, which is used by data warehouse designers and administrators.

A

Technical Metadata

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12
Q

contains details that give end-users an easy way to understand the information stored in the data warehouse.

A

Business Metadata

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13
Q

Corporate users generally cannot work with databases directly.

A
  • Data Warehouse Access Tools
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14
Q

help users produce corporate reports for analysis that can be in the form of spreadsheets, calculations, or interactive visuals.

A

 Query and reporting tool

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15
Q

In such cases, custom reports are developed using application development tools when built-in graphical and analytical tools do not satisfy the analytical needs of an organization.

A

 Application development tools

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16
Q

a process of discovering meaningful new correlations, patterns, and trends by mining a large amount of data. Data mining tools are used to make this process automatic.

A

Data mining

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17
Q

allow users to analyze the data using elaborate and complex multi-dimensional views.

A

OLAP tools

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18
Q

a small, single-subject data warehouse subset that provides decision support for the particular user group.

A

Data Marts

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19
Q
  1. Provides consistent information on various cross-functional activities. It is also supporting “blank” reporting and query.
A

ad-hoc

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20
Q

is a data-modeling technique used to map multi- dimensional decision support data into a relational database.

A

star schema

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21
Q

 Star schema has two (2) common components:

A

Facts table
Dimension table

22
Q

data that will be included in reports and used as the basis of business decisions. It contains measurement or facts to the data and foreign key to dimension table.

A

Facts table

23
Q

are attributes that qualify and provide more information about facts. It contains dimensions of a fact and they are joined to fact table via foreign key.

A

Dimension table

24
Q

 a software tool that is used for data analysis and reporting purposes for business decisions
 used by business analysts, managers, and executives. Example: In Netflix, OLAP was used for movie recommendations based on watch history.

A
  • Online Analytical Processing (OLAP)
25
Q

 an operational system that manages the day-to-day transactions of an organization
 used by the Database Administrator (DBA) and Database Professionals
Example: In ATM centers, OLTP is used for money withdrawals, transfers, deposits, and inquiries.

A
  • Online Transaction Processing (OLTP)
26
Q

Data is processed and viewed as part of a multi-dimensional structure.

A
  • Multi-dimensional data analysis techniques
27
Q

To deliver efficient decision support, OLAP tools must have the following:
 Access to many kinds of DBMSs, flat files, and internal and external data sources
 Rapid and consistent query response times
 Support for very large databases because the data warehouse could easily and quickly grow to multiple terabytes in size

A
  • Advanced Database support
28
Q

permit the user to navigate the data in a way that simplifies and accelerates decision making or data analysis with easy-to-use graphical interfaces

A
  • Easy-to-use end-user interfaces
29
Q

 Works directly with relational databases
 Fact and dimension tables are stored as relations.

A
  • Relational OLAP (ROLAP)
30
Q

 extends OLAP functionality to multi-dimensional database management systems (MDBMS)
 best suited to manage, store, and analyze multi-dimensional data

A
  • Multi-dimensional OLAP (MOLAP)
31
Q

an extension of the GROUP BY clause that is used to create subtotals and grand totals for a set of columns

A
  • ROLLUP operator
32
Q

Like ROLLUP, this generates subtotals for all the combinations of grouping column s specified in the GROUP BY clause.

A
  • CUBE operator
33
Q

allows you to write a cross-tabulation, which means you can aggregate your results and rotate rows into columns

A
  • PIVOT operator
34
Q

Using the “BLANK” operator, we will display the total number of students enrolled in specific campuses and the grand total of students enrolled in all campuses.

A

ROLLUP

35
Q

Using the “BLANK operator, we will turn the unique values/rows in the
Program column into multiple columns.

A

PIVOT

36
Q

refers to analyzing massive amounts of data in a data warehouse or other sources to uncover hidden trends, patterns, and relationships. This explains the past and predicting the future for analysis.

A

Data mining

37
Q

In this step, the goals of the businesses are set, and the important factors that will help in achieving the goal are discovered.

A

Business Understanding

38
Q

This step will collect the entire data and populate the data in the tool (if using any tool).

A

Data Understanding

39
Q

This step involves selecting the appropriate data, cleaning, constructing attributes from data, integrating data from multiple databases.

A

Data Preparation

40
Q

Selection of the data mining technique such as decision-tree, generate test design for evaluating the selected model, building models from the dataset, and assessing the built model with experts to discuss the result is done in this step.

A

Modeling

41
Q

This step will determine the degree to which the resulting model meets the business requirements. The model is reviewed for any mistakes or steps that should be repeated.

A

Evaluation

42
Q

In this step, a deployment plan is made. The strategy to monitor and maintain the data mining model results to check for its usefulness is formed. Final reports are also made, and a review of the whole process is done to check any mistake and see if any step is repeated.

A

Deployment

43
Q

used to retrieve important and relevant information about data and metadata.

A

Classification

44
Q

used to identify data that are like each other. This process helps to understand the differences and similarities between the data.

A

Clustering

45
Q

used to identify and analyze the relationship between variables.

A

Regression

46
Q

used to help find the association between two or more Items. It discovers a hidden pattern in the data set.

A

Association Rules

47
Q

used to observe data items in the dataset that do not match an expected pattern or expected behavior.

A

Outer detection

48
Q

used to discover or identify similar patterns or trends in transaction data for a certain period.

A

Sequential Patterns

49
Q

used to combine other data mining techniques like trends, sequential patterns, clustering, classification, etc. It analyzes past events or instances in the right sequence for predicting a future event.

A

Prediction

50
Q
  • Helps with the decision-making process
  • Helps companies to get knowledge-based information
  • Facilitates automated prediction of trends and behaviors as well as the automated discovery of hidden patterns
  • The speedy process which makes it easy for the users to analyze a huge amount of data in less time
A

Benefits of data mining

Exam (6 decks)

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