Data Mining And Warehousing

The aim of this paper is to show the importance of using data warehousing and data mining nowadays. It also aims to show the procedure of data mining and how it can help opinions makers to make better decisions.

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The foundation of this paper was created by doing a literature review on data mining and data warehousing. The most important findings are the phases of data mining proceeds which are highlighted by the developed figure and the importance of data warehousing and data mining.

It can help to get better answers which allow both technical and non-technical users to make much better opinions. Practically data warehousing and data mining is really useful for any organization which has huge amount of data. Data warehousing and data mining help regular operational databases to perform faster. This paper shows the proceed of data mining and how it can be used by any business to help users to get better answers from huge amounts of data.

it shows an alternative way of querying data. Instead of doing regular queries from regular databases, data mining goes further by extracting more useful records

This huge data is created by integrating current and historical data from disparate authority and store them centrally in a special repository called data warehousing. dw is a very important repository especially for the historical data and non-every-day transactions. Data mining dm is a combination of database and artificial intelligent used to add useful records to both technical and non-technical users which will help them to make better decisions. It is usually used as a decision support system. dm is not an easy process. It involves six phases. Data mining can automate the proceed of extracting records. This is why it is used in disparate areas especially science and business where it is important to evaluate huge amount of data. One of the most common use of data mining is web mining.

Data mining is a combination of database and artificial intelligent used to derive useful records from huge amount of datasets to help the users to make better opinions. Data Mining Process: data mining process is not an easy proceed. It is complicated and has feedback loops which make it an iterative process. It also shows that the steps might be repeated and sometimes it is possible to restart the entire proceed from the beginning. Actually the data mining proceed involves six steps: Problem definition: a data-mining project starts with the compassionate of the business problem. The starting point of the data mining proceed is to accept the business problem.

Data Exploration data exploration can use a sequence of manual methods and automated tools such as data visualizations charts and initial reports. Compassionate the data helps to accept the business more which will play an important role in designing the mining mode. Data Preparation: preparing the data for the modeling tool by selecting table’s records and attributes are typical tasks in this phase. Any bad data should be removed and any missing data should be brought before moving to the next phase. Modeling: after completing the data exploration and preparation phases data mining experts can start the modeling phase by selecting modeling approaches and defining the columns of data needed to build a mining structure and then the mining models.

Evaluation before the deployment phase the selected model should be evaluated carefully before deploying it into the production environment. Deployment: after evaluating the best figure it can be deployed into the production environment .after the deployment the data mining tasks can be done. For example prediction tasks which help the business to make superior opinions. DM is used in different areas to help to extract useful records then make better decisions. for example dm can be used for marketing techniques e.g. association analysis check all the historical related marketing data and compare the sales to add informative reports to be used by the decision makers then increase the future sales. The most common use of data mining is the web mining. As terabytes of data added every day in the internet makes it necessary to find a better way to analyze the web sites and to extract useful records.

A data warehousing is proceed for collecting and managing data from varied sources to provide meaningful business insights. A data warehouse is typically used to connect and evaluate business data from heterogeneous sources. The data warehouse is the core of the bi system which is built for data analysis and reporting. A data warehouse works as a central repository where records arrives from one or more data sources. Data flows into a data warehouse from the transactional system and other relational databases. Data may be: Structured Semi-structured Unstructured data

A data warehouse merges information coming from disparate authority into one comprehensive database. Data warehousing makes data mining possible. Data mining is looking for patterns in the data that may lead to higher sales and profits. Types of Data Warehouse Three main types of data warehouses are: Enterprise Data Warehouse: is a centralized warehouse. It provides opinions support service across the enterprise. It offers a unified approach for organizing and representing data. Operational Data Store: which is also called ODS are nothing but data store required when neither data warehouse nor oltp systems support organizations reporting needs. In ODS data warehouse is refreshed in real time. Hence it is widely preferred for routine activities like storing records of the employees

Data Mart is a subset of the data warehouse. It specially designed for a particular line of business such as sales finance sales or finance. In an autonomous data mart data can collect directly from sources. Data Warehousing Usage: Airline: in the airline system it is used for operation purpose like crew assignment etc. banking: it is widely used in the banking sector to manage the resources available on desk effectively. Healthcare: healthcare sector also used data warehouse to strategize and generate medical aid services etc. public sector: in the public sector data warehouse is used for intelligence gathering. It helps government agencies to maintain and analyze tax records for every individual. Telecommunication: a data warehouse is used in this sector for brand promotions sales opinions and to make distribution opinions.

Data mining is a combination of database and artificial intelligent used to extract useful records from huge amount of datasets to help the users to make better opinions. data mining proceed is not an easy proceed. it is complicated and has feedback loops which make it an iterative proceed. a data warehousing dw is process for collecting and managing data from varied sources to provide essential business insights Data mining has become an important tool which can extract useful information from the huge amount of data we have nowadays. It also may help to excerpt record from the internet which becomes part of our life. It is a complicated process. It involves six phases. Data mining provides a smart way of analyzing and querying data. Data warehousing is not a new phenomenon. All large organizations already have data warehouses but just don’t manage them. For new products and innovations coming out constantly data warehousing will expand exponentially over the next few years.

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Data Mining And Warehousing. (2022, Dec 21). Retrieved from

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