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• Pyramide des données• Définition du Data Mining• Motivations pour le Data Mining• Objectifs du Data Mining• Communautés impliquées• Domaines d’applications des Data Mining• Processus du Data Mining• Paramètres du Data Mining• Classification• Clustering  
• Règles d’association• Recherche de séquences• Détection de déviation• Quelques défis en Data Mining 
• Data– Mots isolés, hors tout contexte, non organisés• Information– Mots dans un contexte spécifique, structurés, organisés et utiles• Knowledge (Connaissance)– Informations mises dans un contexte de règles• Wisdom– Connaissances utiles pour l’aide à la décision, acquises après une expérience et une bonne compréhension du monde réel• Pyramide des données et Data Mining–Arriver aux connaissances à partir des données et des informations–Data Mining = fouille des données pour extraire des connaissances utiles  

Chapitre 1 : Introduction au Data Mining





What is Data Warehouse?

·       Known as an enterprise data warehouse (EDW).
·       It is a system used for reporting and data analysis.
·       A data warehouse is a centralized repository that stores data from multiple information sources and transforms them into a common multidimensional data model for efficient querying and analysis.
Is used to collect and combine data from other databases it updates  this data on regular basis depending on a company's information needs .


Examples: Annual reports.


What are the specifications of DWs?

·       Subject-oriented : Meaning that the data in the database is organized so that all the data elements relating to the same real-world event or object are linked together .
·       Integrated : meaning that the database contains data from most or all of an organization's operational applications, and that this data is made consistent.
·       Time varying : meaning that the changes to the data in the database are tracked and recorded so that reports can be produced showing changes over time .
·       Non-volatile : meaning that data in the database is never over-written or deleted, once committed, the data is static, read-only, but retained for future reporting.


What is Data Mart?

·       A database, or collection of databases, designed to help managers make strategic decisions about their business.
·       Data marts are usually smaller than data warehouses and focus on a particular subject or department.

Why Do We Need a Data Mart?

·       To partition data in order to Force access control strategies.
·       To speed up the queries by reducing the size of data to be scanned.
·       To structure data in a suitable form for a user access tool.
Data warehouse or Data Mart?

Data Warehouse
Data Mart
Holds multiple subject areas
Often holds one subject area EX:(Finance,Sales)
Holds very detailed information
May holds more summarized data
Works to integrate all data sources
Concentrates on integrating information from set of source
Does not necessarily use a dimensional model but feeds dimensional models
Is built focused on a dimensional model

 

What is Data Mining?

·       Data Mining is defined as extracting information from huge sets of data.
·       we can say that data mining is the procedure of mining knowledge from data.
What is the Business intelligence (BI)?

·       Business intelligence (BI) technology infrastructure for gaining maximum information from available data for the purpose of improving business processes.
·       Business Intelligence produces analysis and provides information to help making effective and high quality business decisions.
·       The most common kinds of Business Intelligence systems are:
·       EIS - Executive Information Systems
·       MIS - Management Information Systems
·       GIS - Geographic Information Systems
·       DSS - Decision Support Systems
·       OLAP - Online Analytical Processing .
·       CRM - Customer Relationship Management

·       Business Intelligence tools:
·       Oracle - Siebel Business Analytics Applications
·       SAP - Business Objects XI
·       IBM - Cognos 8 BI
OLTP vs. OLAP:
·       OLTP (On-line Transaction Processing) is characterized by a large number of short online transactions (INSERT, UPDATE, and DELETE).
·       OLTP very fast query processing, maintaining data integrity in multi-access environments .


·       OLAP (On-line Analytical Processing) is characterized by relatively low size of transactions.
·       Queries are often very complex and involve aggregations. (magmo3at)
·       OLAP applications are widely used by Data Mining techniques.
·       In OLAP database there is aggregated And Historical data , stored in multi-dimensional schemas (usually star schema).

What is Big Data?

·       It is a collection of large datasets that cannot be processed using traditional computing techniques.

Benefits of Big Data:

·       Using the information existing in the social network like Face book,

·       Using the data about the previous medical history of patients, hospitals are providing better and quick service .

// Big Data Technologies

There are various technologies in the market from different vendors including Amazon, IBM, Microsoft, etc., to handle big data. While looking into the technologies that handle big data, we examine the following two classes of technology.
What is No SQL?
Is an approach to data management and database design that's useful for very large sets of distributed data.
// Where to use No SQL?
When project has unstructured big data that require real-time or offline analysis or web/mobile application.
Example: Social Network app, Analytics app.
Advantages and Disadvantages of NoSQL DB.
Advantages of NoSQL:
  • Big Data
  • Economics
  • Flexible data models

//Disadvantages of No SQL:
  • Maturity
  • Support
  • Analytics and business intelligence
  • Administration
  • Expertise


//What is Hadoop?


Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation.

What is the Map Reduce?

Map Reduce is a software framework that allows developers to write programs that process massive amounts of unstructured data in parallel across a distributed cluster of processors or stand-alone computers.

What is PL/SQL?

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PL/SQL is a combination of SQL along with the procedural features of programming languages. It was developed by Oracle Corporation in the early 90's to enhance the capabilities of SQL.
·       PL/SQL is one of three key programming languages embedded in the Oracle Database, along with SQL itself and Java.
·       Short for Procedural Language/SQL, Oracle  procedural extension to the industry-standard SQL. By adding procedural constructs to SQL, such as encapsulation, function overloading, information hiding, block structure, conditional statements, loop statements, variable types, structured data and customized error handling, the PL/SQL language takes on characteristics of object-oriented programming languages.
What is the Meta Data ?

What is Metadata?

Metadata is simply defined as data about data.
The data that is used to represent other data is known as metadata. For example, the index of a book serves as a metadata for the contents in the book. In other words, we can say that metadata is the summarized data that leads us to detailed data. In terms of data warehouse, we can define metadata as follows.
·       Metadata is the road-map to a data warehouse.
·       Metadata in a data warehouse defines the warehouse objects.
·       Metadata acts as a directory. This directory helps the decision support system to locate the contents of a data warehouse.

Note: In a data warehouse, we create metadata for the data names and definitions of a given data warehouse. Along with this metadata, additional metadata is also created for time-stamping any extracted data, the source of extracted data.

Data Warehouse

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