Module 6
Advanced topics
There are two types of ORM:-
- Object-Oriented Data Model (OODM)
- Object-Relational Data Model (ORDM)
These models are used to handle objects:
1) To provide support for complex objects.
2) To provide user extensibility for data types operators and access methods.
3) There should be mechanism to support:
a) Abstract data type.
b) Data of type ‘procedure’.
c) Rules.
Advantages of ORDM:-
- Reuse of sharing standard functions can be implemented centrally, rather than coding it in each application.
- It preserves the existing relational applications.
The term “Data Warehousing” was first stated by Bill Inmon in 1990. Data Warehouse (DW or DWH), also known as enterprise data warehouse (EDW), is a system used for reporting and data analysis. It is considered as a core component of business intelligence.
The data stored in the warehouse is uploaded from the systems such as marketing or sales. The data may pass through as operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting.
The typical Extract, Transform, Load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps uses retrieve data.
The main source of the data is cleansed, transformed, catalogued, and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata.
Database and information technology has been evolving systematically from traditional file processing to sophisticated and powerful database system. The evolutionary path is as shown in Fig. 6.5.
Data management includes data storage, retrieve and database transaction processing.
Fig. 6.5 Evolutional Path
Advance Data Analysis: It deals with data warehouse, OLAP, Data mining techniques. It can be viewed as a result of the natural evolution of information technology.
Data Mining: Data mining is simply process of extracting or “mining” knowledge from huge amount of data. It is very much similar to the mining of pearls from rocks and sands. Data mining deals with the discovery of hidden knowledge, unexpected patterns and new rules from large database.
Data and Information: Data is nothing but facts and figures that is translated in to some meaningful information.
Information and Knowledge: Internet is treasure of information as well as other many sources like books, news paper etc. which encode some knowledge.
Data mining and knowledge discovery carry slightly different meaning. Data meaning is one of the essential steps in knowledge discovery process because it uncovers the hidden patterns of evolution.
References
1. “Principles of Database and Knowledge – Base Systems”, Vol 1 by J. D. Ullman, Computer Science Press.
2. “Fundamentals of Database Systems”, 5th Edition by R. Elmasri and S. Navathe, Pearson Education
3. “Foundations of Databases”, Reprint by Serge Abiteboul, Richard Hull, Victor Vianu, Addison-Wesley
