Business Intelligence TU/e - € 3.99
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Business Intelligence TU/e

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Summary Business Intelligence
2016-2017 Q3
Sjuul
Contents
Lecture 1: Introduction to business intelligence .................................................................................... 2
Lecture 2: Introduction to data analysis ................................................................................................. 7
Lecture 3: Experimental Setup .............................................................................................................. 11
Lecture 6: Introduction to fuzzy sets .................................................................................................... 14
Lecture 7: fuzzy interference systems .................................................................................................. 18
Lecture 10: introduction to neural networks........................................................................................ 23
Lecture 12: genetic algorithms ............................................................................................................. 26
Lecture 13: ant colony optimization ..................................................................................................... 30

Lecture 1: Introduction to business intelligence
Conventional decision support:
Model Data Decision
Business intelligence:
Data Model Decision

Emphasize on deduction
Emphasize on induction

Business intelligence Business Intelligence is composed of methods that enhance efficiency and
facilitate decision making by integrating information and processes with the use of tools that
transform data into useful and actionable information.
Business Intelligence systems:


A High-level architecture of business intelligence:

Two types of input:
- Unstructured data conversations, graphics, text, webpages etc.
- Structured data Online Analytical Processing (OLAP), Data Warehouse (DW), Data Marts
(DM), Executive Information Systems (EIS), Enterprise Resource Planning (ERP), Decision
Support Systems (DSS)
Common BI functions:
- Reporting (Summarization, visualization, dashboards, monitoring, definitions of KPIs)
- OLAP
- Analytics
- Data mining
- Process mining
- Complex event processing
- Business performance management
- Benchmarking
- Text mining
- Predictive analytics
Big data is characterized by the 5 Vs:
- Volume
- Velocity
- Variety
- Veracity
- Value
Data mining identifying patterns in data
Process mining identifying patterns in operations
Knowledge extraction when one knows the information is there and how to get to it


Knowledge discovery when one does not know the information is there, but has means to analyze
the data.

Data mining Mine Patterns
- Unclear which attributes are important
- Too much data
- Polluted data
- Results dont make sense
Knowledge discovery
- Select which attributes could explain the phenomenon
- Reduce
- Clean duplicates, outliers, noise
- Mine decision tree learning/neural networks/fuzzy rule-based systems/evolutionary
systems
- Evaluate patterns are not necessarily significant, use common sense, use performance
metrics, use visualizations, iterate


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