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Note for Data Mining And Data Warehousing - DMDW by Akash Sharma

  • Data Mining And Data Warehousing - DMDW
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  • Information Technology Engineering
  • B.Tech
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Chapter 19. Data Warehousing and Data Mining Table of contents • Objectives • Context • General introduction to data warehousing – What is a data warehouse? – Operational systems vs. data warehousing systems ∗ Operational systems ∗ Data warehousing systems – Differences between operational and data warehousing systems – Benefits of data warehousing systems • Data warehouse architecture – Overall architecture – The data warehouse – Data transformation – Metadata – Access tools ∗ Query and reporting tools ∗ Application development tools ∗ Executive information systems (EIS) tools ∗ OLAP ∗ Data mining tools – Data visualisation – Data marts – Information delivery system • Data warehouse blueprint – Data architecture ∗ Volumetrics ∗ Transformation ∗ Data cleansing ∗ Data architecture requirements – Application architecture ∗ Requirements of tools – Technology architecture • Star schema design – Entities within a data warehouse ∗ Measure entities ∗ Dimension entities ∗ Category detail entities – Translating information into a star schema • Data extraction and cleansing – Extraction specifications – Loading data – Multiple passes of data 1

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• • • • • • – Staging area – Checkpoint restart logic – Data loading Data warehousing and data mining General introduction to data mining – Data mining concepts – Benefits of data mining Comparing data mining with other techniques – Query tools vs. data mining tools – OLAP tools vs. data mining tools – Website analysis tools vs. data mining tools – Data mining tasks – Techniques for data mining – Data mining directions and trends Data mining process – The process overview – The process in detail ∗ Business objectives determination ∗ Data preparation · Data selection · Data pre-processing · Data transformation ∗ Data mining ∗ Analysis of results ∗ Assimilation of knowledge Data mining algorithms – From application to algorithm – Popular data mining techniques ∗ Decision trees ∗ Neural networks ∗ Supervised learning · Preparing data ∗ Unsupervised learning - self-organising map (SOM) Discussion topics Objectives At the end of this chapter you should be able to: • Distinguish a data warehouse from an operational database system, and appreciate the need for developing a data warehouse for large corporations. • Describe the problems and processes involved in the development of a data warehouse. • Explain the process of data mining and its importance. 2

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• Understand different data mining techniques. Context Rapid developments in information technology have resulted in the construction of many business application systems in numerous areas. Within these systems, databases often play an essential role. Data has become a critical resource in many organisations, and therefore, efficient access to the data, sharing the data, extracting information from the data, and making use of the information stored, has become an urgent need. As a result, there have been many efforts on firstly integrating the various data sources (e.g. databases) scattered across different sites to build a corporate data warehouse, and then extracting information from the warehouse in the form of patterns and trends. A data warehouse is very much like a database system, but there are distinctions between these two types of systems. A data warehouse brings together the essential data from the underlying heterogeneous databases, so that a user only needs to make queries to the warehouse instead of accessing individual databases. The co-operation of several processing modules to process a complex query is hidden from the user. Essentially, a data warehouse is built to provide decision support functions for an enterprise or an organisation. For example, while the individual data sources may have the raw data, the data warehouse will have correlated data, summary reports, and aggregate functions applied to the raw data. Thus, the warehouse is able to provide useful information that cannot be obtained from any individual databases. The differences between the data warehousing system and operational databases are discussed later in the chapter. We will also see what a data warehouse looks like – its architecture and other design issues will be studied. Important issues include the role of metadata as well as various access tools. Data warehouse development issues are discussed with an emphasis on data transformation and data cleansing. Star schema, a popular data modelling approach, is introduced. A brief analysis of the relationships between database, data warehouse and data mining leads us to the second part of this chapter - data mining. Data mining is a process of extracting information and patterns, which are previously unknown, from large quantities of data using various techniques ranging from machine learning to statistical methods. Data could have been stored in files, Relational or OO databases, or data warehouses. In this chapter, we will introduce basic data mining concepts and describe the data mining process with an emphasis on data preparation. We will also study a number of data mining techniques, including decision trees and neural networks. We will also study the basic concepts, principles and theories of data warehousing and data mining techniques, followed by detailed discussions. Both 3

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theoretical and practical issues are covered. As this is a relatively new and popular topic in databases, you will be expected to do some extensive searching, reading and discussion during the process of studying this chapter. General introduction to data warehousing In parallel with this chapter, you should read Chapter 31, Chapter 32 and Chapter 34 of Thomas Connolly and Carolyn Begg, “Database Systems A Practical Approach to Design, Implementation, and Management”, (5th edn.). What is a data warehouse? A data warehouse is an environment, not a product. The motivation for building a data warehouse is that corporate data is often scattered across different databases and possibly in different formats. In order to obtain a complete piece of information, it is necessary to access these heterogeneous databases, obtain bits and pieces of partial information from each of them, and then put together the bits and pieces to produce an overall picture. Obviously, this approach (without a data warehouse) is cumbersome, inefficient, ineffective, error-prone, and usually involves huge efforts of system analysts. All these difficulties deter the effective use of complex corporate data, which usually represents a valuable resource of an organisation. In order to overcome these problems, it is considered necessary to have an environment that can bring together the essential data from the underlying heterogeneous databases. In addition, the environment should also provide facilities for users to carry out queries on all the data without worrying where it actually resides. Such an environment is called a data warehouse. All queries are issued to the data warehouse as if it is a single database, and the warehouse management system will handle the evaluation of the queries. Different techniques are used in data warehouses, all aimed at effective integration of operational databases into an environment that enables strategic use of data. These techniques include Relational and multidimensional database management systems, client-server architecture, metadata modelling and repositories, graphical user interfaces, and much more. A data warehouse system has the following characteristics: • It provides a centralised utility of corporate data or information assets. • It is contained in a well-managed environment. • It has consistent and repeatable processes defined for loading operational data. • It is built on an open and scalable architecture that will handle future expansion of data. 4

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