Role Of Data Warehousing And Data Mining Technology

1. Describe "active" data warehousing as it is applied at Continental Airlines. Does Continental apply active or real-time warehousing differently than this concept is normally described?

The process and choice of classifying information is very important. Data of different types have different values to the owner of the information. Some data may be of more value or critical importance than other data. Certain information is therefore valuable, and if lost could cause great financial loss.

Miller Inc. requires a transaction processing system since the volume of transaction has increase beyond the normal capacity for performing server and disk bound tasks which are associated with querying and reporting. Miller Inc.'s system utilizes servers and disks that are not used by the transaction processing system. Therefore a data warehouse will provide the quickest way to process transactions within a reasonable amount of time. It also allows for less server and disk resources to be utilized which is expected to reduce the transaction processing time considerably and thus help in speeding up the system overall. It is also expected that running queries and reports which normally use up a lot of the limited system resources will be speeded up since the data warehousing architecture uses separate servers and disks for querying and reporting.

Companies and organizations all over the world are blasting on the scene with data mining and data warehousing trying to keep an extreme competitive leg up on the competition. Always trying to improve the competiveness and the improvement of the business process is a key factor in expanding and strategically maintaining a higher standard for the most cost effective means in any business in today’s market. Every day these facilities store large amounts of data to improve increased revenue, reduction of cost, customer behavior patterns, and the predictions of possible future trends; say for seasonal reasons. Data

Enterprise Data Warehouses (EDW) have become the foundation of many enterprises' systems of record, serving as the catalyst of strategic initiatives encompassing Customer Relationship Management (CRM), Supply Chain Management SCM) and the pervasive adoption of analytics and Business Intelligence (BI) throughout enterprises. The role of databases continues to be an ancillary one, supporting the overall structural and data integrity of the EDW and increasing its value to the overall enterprise (Phillips, 1997). The advances made over the last decade in the areas of Extra, Transact & Load (ETL) have made it possible to create EDW frameworks and platforms more efficiently, creating greater accuracy in overall database and data warehouse performance as a result (Ballou, Tayi, 1999). The creation and use of an EDW to further drive an organization to its objectives requires that the differences between databases and data warehouses be defined, in addition to a clear, concise definition of just what data warehouse technologies are. Finally, the relationship between data warehouses and business intelligence (BI) including analytics needs analysis and validation. Each of these three areas are discussed in this analysis.

A data warehouse and business intelligence application was created as part of the Orion Sword Group project providing business intelligence to order and supply chain management to users. I worked as part of a group of four students to implement a solution. This report reflects on the process undertaken to design and implement the solution as well as my experience and positive learning outcome.

In the early '90s, data warehousing applications were either strategic or tactical in nature. Trending and detecting patterns was the typical focus of many solutions. Now, companies are implementing data warehouses or operational data stores which meet both strategic and operational needs. The business need for these solutions usually comes from the desire to make near

Data mining is the process of analysing data to discover meaningful patterns within the data resulting in extracting useful information that may have not been discovered yet. Data mining borrows techniques from a variety of fields such as statistics, machine learning and artificial intelligence. Because of its usefulness, data mining has been used in a range of industries such as, banking, telecommunications, retail, marketing, and insurance.

The hunger for analyzing data to improve delivered needs and to better meet quality measures is spurring a revolution in all the industries like Healthcare, Manufacturing Industry, Insurance Domain, etc. Considering any Industry, the providers are demanding better respective IT systems that allow Information Management and data analytics professionals to filter through large amounts of data and turn it into "information" that can change the business and function of the industry.

The exponentially increasing amounts of data being generated each year make getting useful information from that data more and more critical. The information frequently is stored in a data warehouse, a repository of data gathered from various sources, including corporate databases, summarized information from internal systems, and data from external sources. Analysis of the data includes simple query and reporting, statistical analysis, more complex multidimensional analysis, and data mining. Data analysis and data mining are a subset of business intelligence (BI), which also incorporates data warehousing, database management systems, and Online.

Data mining functions differently from a classic database interrogation in which, the database inquiries ask for the retrieval of stored information. Datamining is performed in static data collections known as data warehouses. Whereas the online operational databases undergo upgradations. Finding patterns in dynamic system involves much complexity rather than in a static system. The usage of datamining is not only limited to the computer field but also emerged into various disciplines.

The purpose of this document is to present a proposal for applying data mining procedures on a Customer Relationship Management System of a company to reduce Churn Rate and identify valuable customer termed in this document as optimal customers. The constantly updated database of the company will be used as the source of the data for the analysis purpose.

Data mining is finding the routines and examples in large databases to guide choices about future exercises. It is normal that data mining tools to get the model with negligible information from the client to identify. Data mining is the utilization of automated data analysis techniques to discover already undetected connections among data things. It regularly determines the

Database servers are expected to reach the needs of the business, market and end users by providing tremendous performance. Since companies are moving towards “big data” technology to support larger audience there is always a need to have a performance enriched data warehouse server running behind to accommodate the needs of end users. A traditional database server is capable of handling gigs of data, providing a minimal amount of performance. This is due to the restricted amount of pre-defined configurations and parameters the servers come with. A traditional database server cannot match up the performance of a data warehouse server running on a massively parallel asymmetrical engine.

In every day live, the word ‘Mining’ refer to the process that discovered a small set of valuable pieces from a great deal of raw material as in mining process of gold from rocks or sand. According to [3] Data Mining, or Knowledge Discovery in Databases (KDD) as it is also known, is the process of extraction of implicit information that previously unknown and potentially useful from database. By using a number of different technical, such as clustering, data summarization, learning classification, finding dependency networks, analyzing changes, and detecting anomalies. Data Mining refers to a variety of techniques that can be used to analyses and observes database in order to find relationships or summarize the data in ways that can be put to use in different areas such as decision making, prediction and estimation and to do that there are a sequence of the process [2] . As show in figure (1.1)