Business Intelligence Lecture 2 Summary

Study Notes

Business Intelligence - Lecture 2 Summary

Model Definition: Models represent portions of a business process, enabling precise formulation of analytical questions.
Representation Function: Crucial for realizing the model's representation.
Model Language: Enables the proper formulation of representations.
Models of Phenomena: These models portray aspects of business processes relevant for analytical purposes.
Phenomena: Features within the business process of interest for analysis.
Caricatures: Models simplify phenomena for practical use.
Idealized Models: Models based on simplified processes like business operations, treatments, or course designs.
Analogical Models: Employ ideas from other scientific domains e.g., gravity model.
Phenomenological Models: Statistical models, like regression, are used.
Models of Data (Machine Learning/Data Mining): Learning the most appropriate models are critical for data analysis.
Models of Theories: Models representing formal systems (ontologies)
Understanding of formal systems: Defining theories from data instances.
Languages for Models: Specific languages like BPMN for process flow modeling exist.
Formulation of Models: Each language has specific semantic for defining model elements in a specific problem.
Model Structures (Syntax/Semantic):
- Syntax: Defines basic elements and their combination rules.
- Semantic: Defines the meaning of model elements independent of the domain.
- Notation: Provides a clear way to communicate model expressions.
- Model Elements: Specific components to represent the business process.
- Generic Questions: Analytical inquiries regarding model elements.
Modeling (Conceptual Modeling): Mapping domain semantics into suitable model configurations.
Model Configuration: Allow for the formulation of analytical questions (admissible expressions).
Connections with Observations: Model configurations should align with business data observations.
Model Variability: Addresses the nuances and variations of real-world data. Statistical variability can affect data precision.
Model Quality (Assessment):
- Correctness: Model structure and domain interpretation are correct.
- Relevance: Model function meets intended purposes.
- Economic Efficiency: Balance between model's complexity and cost.
- Clarity: Model's ease of understanding.
- Comparability: Model's ability to function within the overall analysis framework.
- Objectivity: Results aren't affected by the user's biases.
- Reliability: Consistent results.
- Validity: Practical usefulness.
- Content Validity: Model accurately reflects the essential features.
- Criterion Validity: Correlation with other known methods.
- Construct Validity: Ability to derive new insights from the model.
Models and Patterns:
- Patterns: Describe local behaviors.
- Models: Describe broader behaviors.
Graph Structures (Model Language):
- Syntax: Includes nodes, directed and undirected edges (connections).
- Notation: Numerical (e.g., adjacency matrices) and visual representations are employed.
- Model Elements: Various graph structures (trees, networks, bipartite graphs).
- Generic Questions: Queries about graph properties.
Data Generation:
- Data Types: Transactional, administrative and web data are pertinent.
- Population Definition: Defining the group or entity represented by the data (e.g. customers, reviews etc.).
- Measurements: Precisely quantifying data qualities.