Business IntelligenceTransparencies

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ObjectivesWhat business intelligence (BI) represents.The technologies associated with business

intelligence including: data warehousing, online analytical processing (OLAP), and data mining.

The main concepts associated with a data warehouse.

The relationship between online transaction processing (OLTP) systems and a data warehouse.

The main concepts associated with a data mart.

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ObjectivesDesigning a database for decision-support using a

technique called dimensionality modeling.The important concepts associated with online

analytical processing (OLAP) systems.The main categories of OLAP tools.The main concepts associated with data mining.How a business intelligence (BI) tool such as

Microsoft Analytical Services provides decision-support.

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Business intelligenceThe processes for collecting and analyzing

data, the technologies used in these processes, and the information obtained from these processes with the purpose of facilitating corporate decision–making.

The main technologies associated with business intelligence includes:data warehouse,online analytical processing (OLAP),data mining.

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Data warehouseA database system that is designed to support

decision-making by presenting an integrated view of corporate data that is copied from disparate data sources.

Data held in a data warehouse is described as being subject-oriented, integrated, time-variant, and non-volatile (Inmon, 1993).

The main source of data for the data warehouse are online transaction processing (OLTP) systems.

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Comparison of OLTP with data warehousing

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Typical architecture of a data warehouse

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Data martA subset of a data warehouse, which supports

the decision-making requirements of a particular department or business area.

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Designing databases for decision-support

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Decision-support databases can be designed using traditional database design or specialist techniques such as dimensionality modeling.

Dimensionality modeling aims to build a data model (called dimensional model) that has a consistent and intuitive structure to facilitate efficient multi-dimensional analysis of data.

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Dimensionality modeling

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Creates a dimensional model (DM) called a star schema that has a fact table containing factual data in the center, surrounded by smaller dimension tables containing denormalized reference data.

As the bulk of data is represented as facts, the fact tables can be extremely large relative to the dimension tables.

Dimension tables contain descriptive textual information and are used as the constraints (search conditions) in queries on the fact data.

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Star schema for StayHome DVD sales

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Online analytical processing (OLAP)Stores large volumes of multi-dimensional

data that is aggregated (summarized) to various levels of detail to support advanced analysis of this data.

Multi-dimensional data can be characterized through many different views. For example DVD sales can be viewed by product, customer, and/or sales channel.

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Examples of OLAP applications

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Online analytical processing (OLAP)OLAP tools are categorized according to

the architecture of the underlying database (providing the data for the purposes of online analytical processing).

There are three main categories of OLAP tools: Multi-dimensional OLAP (MOLAP or MD-

OLAP); Relational OLAP (ROLAP);Hybrid OLAP (HOLAP).

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Typical architecture for multi-dimensional OLAP (MOLAP)

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Typical architecture for relational OLAP (ROLAP)

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Typical architecture for hybrid OLAP (HOLAP)

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Cube Browser of Microsoft SQL analytical services

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Data miningThe process of extracting valid, previously

unknown, comprehensible, and actionable knowledge from large databases and using it to provide decision-support.

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Examples of data mining applications

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Data mining toolsImportant features of data mining tools

include; data preparation;selection of data mining operations

(algorithms);product scalability and performance;facilities for understanding results.

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Data Mining Model Browser of Microsoft SQL Analytical Services

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Dependency Network Browser of Microsoft SQL Analytical Services

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Data warehousing and data mining Major challenge to exploit data mining is

identifying suitable data to mine. Data mining requires a single, separate,

clean, integrated, and self-consistent source of data.

A data warehouse is well equipped for providing data for mining.

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