Questions and Answers
What is a data warehouse?
An integrated, subject-oriented, time-variant, non-volatile database that provides support for decision making.
Which of the following is NOT a characteristic of a data warehouse?
Data in a data warehouse can be removed once it is entered.
False
What are OLAP systems designed to use?
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A data warehouse provides a centralized utility of corporate _____ or information assets.
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What is the purpose of data mining?
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What do KPIs stand for?
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Which of the following represents a schema type in data warehouses?
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Spatial OLAP are spatial data warehouses that provide improved data _____ and manipulation.
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What does a dashboard in data analytics provide?
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Spatial data represents only permanent objects on the Earth's surface.
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Study Notes
Objectives of Data Warehouse Systems
- Understand fundamental concepts of data warehouse systems.
- Review the historical development and achievements in data warehousing.
- Learn about organizing information for decision-making.
- Examine the history of decision support systems.
- Define data warehouses and identify their relevance as a solution.
- Explore spatial and spatiotemporal data warehouses.
- Discuss new domains and challenges in analytics.
Business Intelligence
- Encompasses methodologies, processes, architectures, and technologies transforming raw data into actionable information.
- Aids managers in strategic analysis and decision-making through decision support systems.
Characteristics of Data Warehouses
- Integrated: Centralized database consolidating data from across the organization.
- Subject-Oriented: Data organized to respond to diverse functional queries.
- Time-Variant: Data reflects changes over time and may include future projections.
- Non-Volatile: Data once stored remains unchanged, leading to continuous growth of the warehouse.
Data Warehouse System Features
- Centralized utility of corporate data/assets.
- Managed environments ensuring data integrity and consistency.
- Well-defined processes for loading operational data.
- Open, scalable architecture supporting future data expansions.
- User-friendly tools enabling effective data processing without extensive technical support.
On-Line Analytical Processing (OLAP)
- Advanced data analysis environment enhancing decision-making, business modeling, and operations research.
- Utilizes both operational and warehouse data.
Multidimensional Data Modeling
- Data conceptualized as facts linked to multiple dimensions, allowing for comprehensive analysis.
- Facilitates data aggregation and perspective switching for business analysts.
- Facts represent key analysis areas, often quantified through measures (numerical values).
- Dimensions offer various perspectives on measures, with hierarchies enabling detail exploration.
Data Warehouse Schema Types
- Star Schema: Central fact table connected to multiple dimension tables.
- Snowflake Schema: Normalized refinement of star schema, resulting in smaller dimension tables.
- Fact Constellations: Multiple fact tables sharing dimension tables, resembling a galaxy schema.
Data Analytics
- Process of leveraging data warehouse contents for informed decision-making.
- Employs three main tools:
- Data Mining: Statistical techniques uncovering latent knowledge in data.
- Key Performance Indicators (KPIs): Metrics assessing organizational performance.
- Dashboards: Interactive visual reports summarizing data, including KPIs for decision support.
Spatial and Spatiotemporal Data Warehouses
- Spatial data encapsulates objects and geographic phenomena on Earth.
- Managed via spatial databases or geographic information systems (GIS).
- Essential topological relationships between spatial objects enhance application functionality.
- Spatial OLAP combines spatial database and data warehouse technologies for advanced data analysis, visualization, and manipulation.
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Description
Explore the foundational concepts of data warehouse systems in this chapter. Learn about the historical development of data warehousing, organization methods for information management, and the evolution of decision support systems. Master these key principles to enhance your understanding of data-driven decision making.
