Building A Data Warehouse For Suite Spot

Data mining is a very useful business strategy that allows companies to improve their business model and overall

To begin with, Dell software an information technology enterprises describes Data Mining as “an analytic process designed to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the

Data Mining. It is the process of discovering interesting knowledge that are gathered and significant structures from large amounts of data stored in data warehouse or other information storage.

Data mining is another concept closely associated with large databases such as clinical data repositories and data warehouses. However data mining like several other IT concepts means different things to different people. Health care application vendors may use the term data mining when referring to the user interface of the data warehouse or data repository. They may refer to the ability to drill down into data as data mining for example. However more precisely used data mining refers to a sophisticated analysis tool that automatically dis­ covers patterns among data in a data store. Data mining is an advanced form of decision support. Unlike passive query tools the data mining analysis tool does not require the user to pose individual specific questions to the database. Instead this tool is programmed to look for and extract patterns, trends and rules. True data mining is currently used in the business community for market­ ing and predictive analysis (Stair & Reynolds, 2012). This analytical data mining is however not currently widespread in the health care community.

Having data is not valuable but using data is. Analytic insights are changing the way corporates strategize and also redefining customer expectations. Analytics is the new differentiator between success and failure in the cut throat e-commerce and internet services based industry. The huge proportions of data generated from the increasing number of smart phones, the social networks and the ever more penetrating internet are automating customer centric marketing and other services. The idea is to predict what a customer may want to buy even before the customer realizes what they need. The techniques to achieve these results are broadly classified as Predictive Analytics.

Data mining uses computer-based technology to evaluate data in a database and identify different trends. Effective data mining helps researchers predict economic trends and pinpoint sales prospects. Data mining is stored in data warehouses, which are sophisticated customer databases that allow managers to combine data from several different organization functions.

What is data mining? Data mining is the deriving new information from massive amounts of data in databases (Sauter, 2014, p. 148). Chowdhurry argues that data mining is part of KDD. KDD is knowledge discovery in databases, it is a process that includes data mining. In addition to data mining, KDD includes data preparation, modeling and evaluation of KDD. KDD is at the heart of this research field. This research field is multidisciplinary and includes data visualization, machine learning, database technology, expert systems and statistics. Overall, the use of a case based reasoning and data mining tools within an information system would create a CBR system to solve new problems with adapted solutions and could be used in many industries such as education and healthcare (Chowdhurry,

Data mining is a class of database applications that looks for hidden patterns in a group of data that can be

As coined in an article in the St. Louis Post-Dispatch by Aisha Sultan, “Data is the new world currency.” Data mining is the process of analyzing data from different perspectives and then summarizing it into useful information. In essence is it applying all different types of what if scenarios on large swaths of data to get possible results to aid in better decision making. This sort of decision making isn’t something new, it’s the technology aiding the decision making that is new. This has reduced the amount of time it takes in the decision making process and given the

Although data mining is still in its infancy, companies in a wide range of industries –

Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

Data Base 2 (DB2) Warehouse is IBM’s BI software. The BI software focuses on data warehousing, consolidating data from unrelated sources and forms a “single version of the truth” (IBM.com, 2007), available to users through a variety of analyses. DB2 Warehouse is built to manage mixed workload, run queries concurrently, and also pre-aggregate related data for improved query performance. DB2 Warehouse capabilities include modeling, data mining and visualization, and embedded analytics and database management tools. DB2 has an integrated compression that has proven a savings of 45-69% disk space, and a workload control that automatically prioritizes and schedules queries. In addition to basic data warehousing and mining, IBM has prepackaged solutions for specific industries: banking, retail, insurance, healthcare, telecommunications and law enforcement. DB2 Warehouse will serve clients running Microsoft® Windows® XP or 2000, and run on servers with any of the following operating systems: IBM AIX® 5L™, Red Hat Enterprise Linux® 3 and 4, SUSE Linux Enterprise Server 9, Sun Solaris 9, Microsoft® Windows® Server 2003. It is compatible with two web browsers: Microsoft® Internet Explorer and Mozilla Firefox.

Data mining: is a process of discovering patterns in large data involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems with the aim of extracting information and transforming it into an understandable structure for further use.

Data mining is when a financial analyst gathers consumer information and looks for patterns that a business can exploit. A simplified data mining example is when a restaurant manager knows the local yearly convention schedule based on experience. The manager can cross-reference that information with historical sales results to predict such things as forecasted profit or labor demand. With this information, the manager can estimate an advertising budget or hire temporary staff to handle anticipated work load. When medium to large-sized businesses use data mining, they uncovering these same information points; however, revenue gains can range from millions to billions of dollars. There are several techniques that firms frequently employ to find gold in information.

Chapter 2 describes the Avalon data warehouse functionality and chapter 3 the scope of the Avalon data warehouse. Chapter 4 lists the motivation for supporting the Avalon data warehouse implementation. In chapter 5, the process of collecting and processing business requirements is described. At chapters 6 and 7, there is a technical description of the architecture and data security, and Chapter 8 outlines the implementation plan.