Summary: Intelligent Data-Analyse

Read the summary and the most important questions on Intelligent Data-analyse

• Lecture 1; Introduction

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• What is Inductive Bias? And its first question?

  Your view determines what you see. Whereby the first question in data mining is: What kind of information (pattern) do you want to get out of the data?

• What is Business Intelligence [BI] (1.0)?

  
  1.Data warehousing (from existing databases)
  2.(Basic) BusinessAnalytics
  3.(advanced) Data, Text, Web, IoT (Internet of Things), … Mining
  4.Data & Information Visualization
  5.(Business) Performance Management (BPM)

• What are the properties of a data warehouse?

  - Subject-oriented: customers, sales, products
  - Integrated: consistent data given a unified view of the data to its users
  - Time-variant: updates at regular intervals
  - Non-volatile: data not change, new data are just added

• Lecture 2; Data warehouses

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• Where does OLTP stand for?

  On-Line Transaction Processing System

• What is the main difference between a Data warehouse and a OLTP?

  OLTP                                 Warehouse
  holds current data            Historical data
  Detailed                             Summarized
  Dynamic                            Static
  Transaction-driven           Analysis driven
  Large number users         Low number users

• What is the difference between a dependent and independent data mart?

  Dependent is a subset created directly from a DW and an independent data mart is a small DW designed for strategic business unit.

• What is the general architecture of an DW?

  1. Operational data Store: temporal repository of current and integrated operational data for simple analysis and reporting
  2. Load manager: handles the integration of data into the DW (extraction, transformation, cleaning, load, refresh)
  3. Warehouse manager: includes a (relational) DBMS for detailed data and OLAP servers to provide acces to summarized data
  4. Query manager: is the interface with applications and users   
  5. Executive Information Systems (EIS): focus on nowadays on all levels of management

• What is the most dominant DW design?

  Dimensionality Modeling

• Where is a Star Schema consisting of?

  A Dimensional Model (DM)
  •A retrieval-based system with a logical design that supports high-volume/high-performance query access (it determines what type data are analyzed)
  •Each DM is composed of one so-termed
  •Fact Table (with a composite primary key) surrounded by a set of Dimension Tables
  •Dimension Tables: contain reference data that address how data will be analyzed: each dimension table has a simple (non-composite) primary key that corresponds exactly to one of the components of the composite key in the fact table

• What are two variants of the star schema?

  •Snowflake schema is a variant of the star schema where dimension tables do not contain denormalized data (i.e., just normalized data)
  •Starflake schema is a hybrid structure that contains a mixture of star (denormalized) and snowflake (normalized) schemas. Allows dimensions to be present in both forms to cater for different query requirements