Data Mining Chapter 4 Part 2
Data Mining Quiz: Chapter 4 Part 2
Test your knowledge on Data Mining concepts and data warehousing practices with this comprehensive quiz! Dive into scenarios about OLAP servers, data cube materialization, and knowledge discovery.
- 41 multiple choice questions
- Assess your understanding of data mining methodologies
- Perfect for students, educators, and data enthusiasts
.….. Allows the selection of the relevant information necessary for the data warehouse.
The top-down view
The data warehouse view
The data source view
The business query view
.….. Exposes the information being captured, stored, and managed by operational systems.
The data source view
The top-down view
The data warehouse view
The business query view
.….. Includes fact tables and dimension tables.
The data source view
The top-down view
The data warehouse view
The business query view
.….. Starts with experiments and prototypes. This is useful in the early stage of business modeling and technology development.
Top-down approach
Bottom-up approach
Combined approach
None
.….. Is the data perspective in the data warehouse from the end-user’s viewpoint.
The data source view
The business query view
The top-down view
The data warehouse view
.….. Starts with overall design and planning. It is useful in cases where the technology is mature and well known, and where the business problems that must be solved are clear and well understood.
Top-down approach
Bottom-up approach
Combined approach
None
In the ….….. An organization can exploit the planned and strategic nature of the top-down approach while retaining the rapid implementation and opportunistic application of the bottom-up approach.
Bottom-up approach
Top-down approach
Combined approach
None
.….. Provide functions to define and edit metadata repository contents (e.g., schemas, scripts, or rules), answer queries, output reports, and ship metadata to and from relational database system catalogs.
Planning and analysis tools
Data warehouse development tools
None
Both
.….. Study the impact of schema changes and of refresh performance when changing refresh rates or time windows.
Planning and analysis tools
Data warehouse development tools
None
Both
.….. Supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts, or graphs.
Information processing
Analytical processing
Data mining
None
.….. Supports basic OLAP operations, including slice-and-dice, drill-down, roll-up, and pivoting. It generally operates on historic data in both summarized and detailed forms.
Information processing
Data mining
None
Analytical processing
.….. Supports knowledge discovery by finding hidden patterns and associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools.
Analytical processing
Data mining
Information processing
None
.….. Method is popular in OLAP products because it allows quick searching in data cubes.
Bitmap indexing
Join indexing
Both
None
.….. Method gained popularity from its use in relational database query processing.
Bitmap indexing
Join indexing
Both
None
.….. These are the intermediate servers that stand in between a relational back-end server and client front-end tools.
Relational OLAP (ROLAP) servers
Hybrid OLAP (HOLAP) servers
Multidimensional OLAP (MOLAP) servers
Specialized SQL servers
.….. These servers support multidimensional data views through array-based multidimensional storage engines.
Multidimensional OLAP (MOLAP) servers
Hybrid OLAP (HOLAP) servers
Relational OLAP (ROLAP) servers
Specialized SQL servers
.….. combines ROLAP and MOLAP technology, benefiting from the greater scalability of ROLAP and the faster computation of MOLAP.
Relational OLAP (ROLAP) servers
Hybrid OLAP (HOLAP) servers
Specialized SQL servers
Multidimensional OLAP (MOLAP) servers
.….. Provide advanced query language and query processing support for SQL queries over star and snowflake schemas in a read-only environment.
Multidimensional OLAP (MOLAP) servers
Hybrid OLAP (HOLAP) servers
Relational OLAP (ROLAP) servers
Specialized SQL servers
.….. Provides a concise and succinct summarization of the given data collection.
Characterization
Classification
Discrimination
Selection
.….. Provides descriptions comparing two or more data collections.
Characterization
Discrimination
Selection
Classification
.….. The set of relevant data in the database is collected by query processing and is partitioned respectively into a target class and one or a set of contrasting classes.
Data collection
Data Characterization
Data Selection
Data Classification
If there are many dimensions, then ….….. Should be performed on these classes to select only the highly relevant dimensions for further analysis.
Dimension relevance development
Dimension relevance selection
Dimension relevance analysis
None
.….. Refers to the computation of all of the cuboids in the lattice defining a data cube.
Full materialization
Partial materialization
None
No materialization
.….. Is the selective computation of a subset of the cuboids or sub cubes in the lattice.
Partial materialization
Full materialization
None
Materialization
.….. Do not precompute any of the “non base” cuboids.
No materialization
Partial materialization
Full materialization
None
.….. Do not precompute any of the “non base” cuboids.
Partial materialization
Full materialization
No materialization
None
Data warehouses and OLAP tools are based on a ….…...
Multidimensional data model
Relational data model
Hybrid data model
None
.….. Should be performed before attribute-oriented induction.
Data Selection
Data Classification
Data Characterization
Data focusing
.….. Is based on the following rule: If there is a large set of distinct values for an attribute of the initial working relation, but either (case 1) there is no generalization operator on the attribute or (case2) its higher-level concepts are expressed in terms of other attributes.
Attribute removal
Attribute generalization
Both
None
.….. Is based on the following rule: If there is a large set of distinct values for an attribute in the initial working relation, and there exists a set of generalization operators on the attribute, then a generalization operator should be selected and applied to the attribute.
Attribute removal
Attribute generalization
Both
None
.….. Is a Common approach which is useful to control a generalization process.
Generalized relation threshold control
Both
None
Attribute generalization threshold control
Building and using a data warehouse is a complex task because it requires business skills, technology skills, and program management skills
True
False
The top-down approach allows an organization to move forward at considerably less expense and to evaluate the technological benefits before making significant commitments
True
False
The design and construction of a data warehouse may consist of the following steps: planning, requirements study, problem analysis, warehouse design, data integration and testing, and finally deployment of the data warehouse.
True
False
Online analytical processing comes a step closer to data mining because it can derive information summarized at multiple granularities from user-specified subsets of a data warehouse.
True
False
OLAP is a data summarization/aggregation tool that helps simplify data analysis.
True
False
Data mining allows the automated discovery of implicit patterns and interesting knowledge hidden in large amounts of data
True
False
The concepts in the contrasting class(es) are generalized to the same level as those in the prime target class relation, forming the prime contrasting class(es) relation.
True
False
The selection of dimensions and the application of OLAP operations (e.g., drill-down, roll-up, slicing, and dicing) are primarily directed and controlled by users.
True
False
Concept hierarchies organize the values of attributes or dimensions into gradual abstraction levels. They are useful in mining at multiple abstraction levels.
True
False
OLAP involves more automated and deeper analysis than data mining.
True
False
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