Data warehousing and mining PDF

Data Warehousing And Data Mining PDF Notes Download

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Data warehousing consists of the usage of data warehouses and also has a constructing process. It is constructed by having all the multiple different types of sources which will be helpful in supporting the analytical report, and also useful in decision-making purposes. To know more about Data Warehousing And Data Mining, keep reading this article till the end.

It includes data cleaning, data integration, data consolidations.

The data which is available in the data warehouse is used by taking the help of decision support technologies. Warehouse can be used effectively and quickly by using these technologies. Gathering of data and analyzing them and making decisions depending on the information present in the warehouse.

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Tuning production strategies:

Products are repositioned and product portfolios are managed by tuning the product strategies very well it can be done by comparing the sales yearly or by quarterly.

Customer analysis:

Customer’s buying preferences are analyzed and their buying time and cycles of the budget all these are analyzed in customer analysis.

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Operation analysis:

It makes environmental corrections and manages customer relationships, all this information will be helpful in analyzing the business operations.

We have two methods for integrating heterogenous databases:

1.query-driven approach

2. update-driven approach

First one is query driven approach: in this we integrate the different types of databases. this is useful for building the wrappers and integrators which are on many different types of databases.  These integrators are known as mediators.

Query driven approach process:

At client side, when there is a query, a dictionary known as metadata , dictionary ,it will translate the query into some proper form which is useful for many different types of sites that are involved. Local query processor will receive these mapped queries. The different types of sites results will be integrated to an answer set which is global.

The disadvantages of query driven approach:

  • It will need integration that is complex and processes should be filtered.
  • It is an inefficient approach.
  • For frequent queries it is very expensive.
  • The queries that require aggregations will also be so expensive.

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Now let us know about update-driven approach:

Traditional approach’s alternative is this approach. Update-driven approach is followed by data-warehouse. It does not use traditional approach. before , the information present in multiple different types of sources used to be integrated into advance and they were stored in warehouse. And this information is useful for querying directly and also useful for analysis.

I have given the download link of Data Warehousing And Data Mining PDF Notes below. You can Download and share with your friends and classmate

Advantages of this approach are:

There is a high performance in this approach.

The copied data and the processed data and integrated data are stored in semantic data store.

There is no need of an interface for the query to be processed at local sources.

Data ware house tools functions and their utilities:

Data extraction: it consists of gathering the information which are taken from multiple different types of sources.

Data cleaning: it consists of finding and correction of the errors that are encountered in the data.

Data transformation: converts the data from old format to the format warehouse.

Data loading: it consists of summarizing, sorting and checking for integrity in the data and consolidating and also useful for creating the indices.

Refreshing: the data from the data source is updated to warehouse.

The important steps of data warehousing are data transformation and data cleaning which will improve the data’s quality and also gives data mining results.

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Now let us know about data mining:

There are basic, advanced topics in data mining. Data mining is defined as information is extracted from the large sets of data and there is a lot of useless information in the industry sectors and we have to make use of all these information so that it wont get just wasted and also helps the business in taking proper decisions so data mining will be helpful for us in extracting the useful information that is present in large sets of data, it means the data gets mined from the knowledge. Data mining is also known as KDD which means Knowledge Discovery in Database. The process of knowledge discovery consists of data cleaning, data transformation, data integration, data selection, data mining, pattern evaluation and knowledge representation.

We have a lot of datamining applications, data mining tools and social media data mining data mining techniques , data mining clustering concept. And also the challenges present in data mining.

We will investigate the patterns that are hidden and also the information which are in various aspects which is useful for categorization and collection and assembling in some areas like data warehouses, efficient analysis, and helpful for decision making process and helps in data mining algorithm and it will make less costs and generates the revenue.

In data mining, we will search for large sets of information which will be helpful in finding the trends and also patterns which are not the simple procedures. It will use very difficult mathematical algorithms which are useful for segments of the data and also helpful in evaluating the events of future’s probability. Data mining is also known as KDD.

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For extracting the specific type of data it is used in organizations from large data bases which are helpful in solving the business issues and problems. It will turn the raw data into some useful data.

The data mining process consists of text mining, web mining, audio and video mining, and also image mining which is called as pictorial mining and also social media mining. Data mining has a software and it does all these tasks using that software. All the works will be done quickly and the costs will also be less when we use data mining. Many different platforms has huge sets of information and they have very little knowledge. So the big problem is identifying the data which is useful from the huge sets of data and extracting the useful information from those huge sets of data and there are very powerful instruments and techniques that would help me in extracting the information from huge sets of data and we can do that better.

The data mining has types:

Relational database, data warehouses, data repositories, object-relational databases and transactional databases.

1.relational databases: many data sets will be organized in the form of tables, columns and records and we can access the data in many different ways and we don’t need to recognize the database tables. Tables will give information and the information is shared through tables, It will facilitate the searchability of the data and used in reporting and organizations.

2. data warehouses: from heterogenous types of sources, the data is collected which will provide the business insights that are meaningful. from marketing and finance the huge amount of data will come, and the data that is extracted is used for analytical purposes and also helpful in making the decisions which is useful for business organizations. Analysis of data is only done in data warehouse but not transaction processing.

3. data repositories: it stores the data in a destination. It consists of databases group, in which an organization will have various kinds of information.

4.object-relational database: object-oriented database model and relational database model combined and it forms the object-relational will support the classes, objects and all object oriented concepts.

It will close the gap that is in between the relational database and the object-oriented model and they are sued in many programming languages like “c++,java and c#” etc

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5.transactional database: it will undo the transaction in database when the operations are not performed correctly. Transactional databases are supported by the relational database systems.

Data mining Advantages :

Organizations will be able to access the knowledge which is based on the data.

It will helpful in performing the operations and productions perfectly and appropriately.

Data mining has less costs when we compare it with other statistical data applications.

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It is helpful in making the decisions for the organization.

Hidden patterns are automatically discovered and also the patterns and behaviors and also trends are discovered.

New users will be able to analyze the big amounts of data in a very less time, it makes it easy for everyone who is leaning data mining.

There are disadvantages in data mining:

There may be a problem where the organization may also sell the information that is useful to other organizations for their benefits that will lead to bigger problems for the company.

The software may be hard to operate and it may need an advance to work very properly.

As different algorithms are used, different mining technique instruments will perform or function in different ways so the selection will become tough.

There are no precise data mining techniques which may result in hard consequences.

Applications of data mining:

This is all about data warehousing and data mining.

Many organizations will use data mining with customer demands and also have communication with customers and financial and marketing companies will use data mining and they will determine the prices and preferences of the customer are seen and product positioning and also have impact on the sales and they also consider customer satisfaction and corporate profits. Data mining is useful in healthcare, fraud detection, market basket analysis, lie detection, CRM, financial banking, education, manufacturing engineering, etc.

I have given the download link of Data Warehousing And Data Mining PDF Notes below. You can Download and share with your friends and classmate

I hope you have read and understood about the data warehousing and data mining.

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