Data Warehousing and Mining Overview
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Data Warehousing and Mining Overview

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Questions and Answers

What is the primary purpose of a data warehouse?

  • To increase the speed of data entry
  • To provide comprehensive historical and analytical insights (correct)
  • To store real-time transactional data
  • To eliminate all types of data redundancy

    • How does data purging improve a database?

  • By increasing the size of the database
  • By modifying existing data structures
  • By adding new data sources
  • By eliminating unnecessary NULL values and junk data (correct)

    • Which statement accurately defines a fact table?

  • The central table that contains measurements of business processes (correct)
  • A table containing the primary keys of the database
  • A table with minimal data that supports dimension tables
  • A table dedicated to storing historical data only

    • What role does data mining play in business decision-making?

    <p>It helps automate the identification of patterns within large datasets</p> Signup and view all the answers

    What does a dimension table in a data warehouse primarily store?

    <p>Attributes that describe the objects in a fact table</p> Signup and view all the answers

    What is the main objective of data mining?

    <p>To identify hidden patterns and relationships in data</p> Signup and view all the answers

    Which of the following is NOT a benefit of data mining?

    <p>Enhancing the performance of data entry systems</p> Signup and view all the answers

    What characterizes a data warehouse as opposed to a traditional database?

    <p>It focuses primarily on historical data storage for analytics</p> Signup and view all the answers

    Which of the following best describes OLAP?

    <p>Utilizes long transactions and complex queries.</p> Signup and view all the answers

    What is the primary function of ETL in data management?

    <p>Extracting, transforming, and loading data.</p> Signup and view all the answers

    How does a data mart differ from a data warehouse?

    <p>Data marts are focused on the needs of specific departments.</p> Signup and view all the answers

    Which statement accurately describes the star schema?

    <p>It contains one fact table and multiple dimension tables.</p> Signup and view all the answers

    What is the main purpose of using a snowflake schema in data warehousing?

    <p>To normalize data by linking dimension tables.</p> Signup and view all the answers

    Which command is primarily associated with OLTP systems?

    <p>INSERT for capturing new records.</p> Signup and view all the answers

    What role does metadata play in data management?

    <p>It describes and gives context to the data.</p> Signup and view all the answers

    In which scenario would you most likely use OLAP?

    <p>For analysis involving historical data and complex queries.</p> Signup and view all the answers

    What is the primary purpose of the Decision Tree Algorithm?

    <p>To perform classification using a tree-like structure</p> Signup and view all the answers

    Which of the following best describes the Naïve Bayes Algorithm?

    <p>It is based on Bayes theorem and is effective for text classification.</p> Signup and view all the answers

    What is the main objective of clustering algorithms?

    <p>To group data sets with similar characteristics into clusters.</p> Signup and view all the answers

    Which statement about the Star Schema is true?

    <p>It consists of a fact table surrounded by dimension tables.</p> Signup and view all the answers

    How does the Snowflake Schema differ from the Star Schema?

    <p>It involves a more complex database design with normalized structures.</p> Signup and view all the answers

    What is the goal of Association Rule Mining?

    <p>To identify strong correlations and patterns in data sets.</p> Signup and view all the answers

    What characteristic is typical of clustering algorithms?

    <p>They iteratively redefine groupings based on relationships.</p> Signup and view all the answers

    In which scenario is the Naïve Bayes algorithm most effective?

    <p>Text classification involving high-dimensional input.</p> Signup and view all the answers

    What is the primary focus of a data warehouse?

    <p>Modeling and analyzing data for decision making</p> Signup and view all the answers

    Which of the following is a characteristic of a data warehouse?

    <p>Non-volatile data maintenance</p> Signup and view all the answers

    What does time variance in a data warehouse signify?

    <p>Data is identified with a specific time period</p> Signup and view all the answers

    Which technique is used to analyze the relationship between variables in data mining?

    <p>Regression</p> Signup and view all the answers

    What is the purpose of the clustering technique in data mining?

    <p>To understand similarities and differences among data</p> Signup and view all the answers

    How does integration in a data warehouse benefit data analysis?

    <p>It allows for effective analysis of data from various sources</p> Signup and view all the answers

    Which of the following statements describes non-volatile data in a data warehouse?

    <p>Data can only be added but not modified</p> Signup and view all the answers

    What do association rules in data mining help uncover?

    <p>Hidden patterns between items</p> Signup and view all the answers

    Study Notes

    Data Warehousing

    • A Data Warehouse (DW) is a process for collecting and managing data from various sources to provide insightful business information.
    • A data warehouse stores an organization's historical data to support reporting, analyzing, data mining, and knowledge discovery.
    • Data purging removes junk data and unnecessary NULL values from a database to manage data size.
    • Dimension tables store attributes that describe objects in a fact table, used in star and snowflake schemas.
    • Fact tables are central to star and snowflake schemas and contain measurements of business processes with foreign keys referencing dimension tables.

    Data Mining

    • Data mining analyzes large datasets to reveal patterns and relationships for solving business problems.
    • Data mining helps analysts make faster business decisions, understand patterns, and identify hidden predictive info.

    OLAP & OLTP

    • OLAP (Online Analytical Processing) handles historical data from various sources, enabling complex queries for reporting and data aggregation.
    • OLTP (Online Transaction Processing) manages current operational data with short transactions and simpler queries.

    ETL

    • ETL (Extract, Transform, Load) is a software process that reads data from sources, transforms it using rules and lookups, and loads it into a target database.

    Data Mart

    • A data mart is a subset of a data warehouse tailored to a specific team, department, or section for easier access to key insights.
    • Data marts prevent departments from interfering with each other's data and enhance quick access to relevant information.

    Data Warehouse Differences

    • OLAP is used to analyze data, manage aggregations, and is part of the Data Warehouse process, while a data warehouse is the storage location for all data.

    Data Warehousing Schemas

    • Star Schema is a simple architecture with a central fact table and radiating dimension tables.
    • Snowflake Schema is an extension of star schema with normalized data, where dimension tables can be linked to other dimension tables.

    Data Mining Techniques

    • Classification: Retrieves data and metadata to classify data into different categories.
    • Clustering: Groups data with similar characteristics for understanding similarities and differences.
    • Regression: Analyzes relationships between variables to predict the likelihood of a variable based on other variables.
    • Association Rules: Discovers hidden patterns in datasets by identifying relationships between items.

    Data Warehouse Characteristics

    • Subject-oriented: Focuses on specific business areas like products, customers, or sales.
    • Integrated: Combines data from various sources like databases and files.
    • Time-variant: Data is associated with specific time periods, offering historical analysis.
    • Non-volatile: Previous data remains available even when new data is added.

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    Related Documents

    data-warehouse-mining-viva-questions.pdf

    Description

    This quiz covers key concepts in Data Warehousing, Data Mining, and their related processes. Learn about data cleansing, dimension and fact tables, and the different functions of OLAP and OLTP. Test your understanding of how these components work together to support business intelligence.

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