Data Mining and Warehousing Techniques

The use of huge databases that combine all of a company 's data and allow users to access the data directly, create reports, and obtain responses to what-if questions is referred to as:

What information is accessible? The data warehouse offers possibilities to define what’s offered through metadata, published information, and parameterized analytic applications. Is the data of high value? Data warehouse patrons assume reliability and value. The presentation area’s data must be correctly organized and harmless to consume. In terms of design, the presentation area would be planned for the luxury of its consumers. It must be planned based on the preferences articulated by the data warehouse diners, not the staging supervisors. Service is also serious in the data warehouse. Data must be transported, as ordered, promptly in a technique that is pleasing to the business handler or reporting/delivery application designer. Lastly, cost is a feature for the data

An important measure of any database management system is how effectively related data can be collected, organized and presented as reports, and also for business intelligence. What techniques from data warehousing and data mining would be used to facilitate this.

A data warehousing is defined as a collection of data designed to support management decision making. Data warehouses contains a wide variety of data that present a coherent picture of the business conditions at a single point in time. Development of a data warehouse includes development of the systems that extract data from operating systems plus the installation of the warehouse database system that provides managers flexible access to the data. The term data warehousing generally refer to the combination of many different databases across an entire enterprise. (webopidia)

A data warehouse is a large databased organized for reporting. It preserves history, integrates data from multiple sources, and is typically not updated in real time. The key components of data warehousing is the ability to access data of the operational systems, data staging area, data presentation area, and data access tools (HIMSS, 2009). The goal of the data warehouse platform is to improve the decision-making for clinical, financial, and operational purposes.

The data warehousing system will also allow the company to use a data model and server technology that speeds up querying and reporting. This is because these will not be included in the data processing time thus allowing the company to use a modeling technique that does not slow down or complicate the transaction processing system. The data warehouse will also allow the company to use a bit-mapped indexing system as their server technology in order to speed up query and report processing. Technologies for transaction recovery will also be employed to speed up transaction

In its infancy, data mining was as limited as the hardware being used. Large amounts of data were difficult to analyze because the hardware simply could not handle it [1]. The term "data mining" first began appearing in the 1980 's largely within the research and computer science communities. In the 1990 's it was considered a subset of a process called Knowledge Discovery in Databases of KKD [1]. KKD analyzes data in the search for patterns that may not normally be recognized with the naked eye. Today however, data mining does not limit itself to databases,

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

Businesses today continue to strive and grow in the industry to keep up with the never ending changes in the business they need the tools to obtain information that can be used to make decisions for the business. The decisions to make in a business can consist of knowing what geographic region to focus on, which product lines to expand, and what markets to strengthen in the industry. To obtain the type of information that has the proper content and format that can assist with strategic decisions they turned to data warehousing. It became the new paradigm intended specifically for vital strategic information.

Data mining or Knowledge Discovery in Databases (KDD) is discovering patterns from large data groups through methods of artificial intelligence, machine learning ,statistics, and database systems. The aim of data mining process is to extract information from a data group and switch it to an ideal format for future . The data mining process comprise of database and data management aspects, data preprocessing, inference, complexity of discovered structures, and updating.

The concept of data warehousing goes back to the late 80s when IBM researchers Barry Devlin and Paul Murphy developed "business data warehouse". To summarize, the concept of data warehousing was created to provide an architectural model for the flow of collection of data from various operational systems to the decision supporting environments. The concept attempted to solve the various technicalities associated with this flow of data, primarily the high costs associated with it. In the absence of a data warehousing, an enormous amount of redundancy was needed to support multiple decision support environments. In larger organizations it was usual for multiple decision support environments to operate on their own. Even

Furthermore, the Gartner website argues that “BI has become a strategic initiative and is now recognised by chief information officers (CIOs) and business leaders as instrumental in driving business effectiveness and innovation,” (Anon., 2007). Gartner also argues that “BI projects were the number one technology priority for 2007” (Anon., 2007). According to the Bill Inmon, data warehouse is “a subject-oriented, integrated, time variant and non-volatile collection of data used in strategic decision making”. Hammergen & Simon, (2009) define data warehouse more simpler by saying that “ Data warehousing is therefore the process of creating an architected information management solution to enable analytical and information processing despite platform, application, organizational, and other barriers.“ It is important to note that data warehouse system is different from relational database. The reasons of that are: (1) In the data warehouse data is stored for long term; (2) DW is designed for high performance for analytical queries; (3) its OLAP (Online Analytical Processing) technology enables to view data in various form; (4) linking between tables are simple (Tushman, 2014). Databases, in contrast, have a low performance regarding data analysis; joins between tables are

This research paper is about the Comparative analysis of three data mining software’s selected based on four important criteria Performance, Functionality, Usability and Ancillary Tasks support. “Data Mining is a field of study that is gaining importance and is used to explore data in search of patterns or relationships between variables and is applied to new data used for predictions”. (Statistics – Textbook. (n.d.). Retrieved November 17, 2015). Selection of the appropriate data mining tools is critical to any research or business and this could impact the business in terms of money, resources and time. Data experts

Data warehouse is a central repository integrating data from various operating systems for validation of data, prediction etc .Data Warehouse is a relational database used for analysis and query rather than transactional database. It is used to collect historical data from various sources, integrate, analyze a particular subject, report. Data warehouse is time variant i.e one can retrieve any older data and once data enters data warehouse it cannot change [1]. According to Ralph Kimball Data warehouse is “copy of transaction data constructed for analysis and query”[5]. Data is taken from various sources like marketing, sales, ERP etc.

A class of database applications that look for hidden patterns in a group of data that can be used to predict future behavior.