Abstract
This paper discusses the "architecture" of data warehouses and briefly describes possible future developments in data warehouses as well as restrictions in data warehouse technology.

Table of Contents
Introduction
Data Warehousing: Brief History
Data Warehouse Architecture
Restrictions
The Present and the Future
Conclusions

From the Paper
"There is little question that many critical enterprises in the world of today are dependent on quick and dependable access to information. From the halls of academia, to the world of business-science to medicine-the ability to readily access critical information within any particular organization or working entity is essential to survival and growth. However, even in today's technology-driven industries, it is often difficult for companies and other organizations to effectively provide the most comprehensive and critical internal information to those who need it."

Tags:store, access, organizations, fail, countless, bytes, accumulate, unconnected, applications, systems, valuable, resources, information

Abstract
The writer looks at different types of data warehouses and shows the development as technology has expanded in the direction of the internet. The paper discusses the types of companies and organizations that use storage mechanisms. It also cites reasons why such warehouses can be security risks when storing confidential information.

From the Paper
"Traditional database management systems are passive; retrieval commands are executed by the database when requested by a user or application program. Active databases, differ in that they offer the ability to monitor and react to specific circumstances and perimeters of relevance to an application. The active database system provides a knowledge model (a description mechanism) and an execution model (i.e., a runtime strategy for supporting reactive behavior based upon the parameters of the software.)"

Tags:internet, storage, organization, system, business

Abstract
Begins by defining data warehouse and describing the business uses for the technology. This is followed by a focus of data warehouse management. Three components of data warehouse management are examined. In addition, a discussion on the assurance of safety and privacy, which are needed to maintain the integrity of the data warehouse, is included. The discussion also focuses on the availability and reliability of the data warehouse. Finally the paper investigates different management tools that are used to maintain the data warehouse.

From the Paper
"Data warehouses are an indispensable part of any global organization. Data warehouses are used to keep track of sales, inventory, and customer spending patterns. ("Data Warehousing") In fact, "a data warehouse may contain very different things, ranging from the traditional financial, manufacturing, order and customer data, through document, legal and project data, on to the brave new world of market data, press, multi-media, and links to Internet and Intranet web sites." (Barker 1998)"

Tags:global, organizations, business, technology, software, searches, retrieval, info, mart, user, interfaces

Abstract
This paper considers key factors regarding data warehousing. It looks at the goal of data warehousing and the differences of data warehousing and relational databases.

From the Paper
"Data warehousing is particularly popular in environments which have complex data requirements and a broad spectrum of data types contained in its database. The goal of data warehousing is to take full advantage of the power of hardware to contain large quantities of data and use the databases to manipulate that data. Although not yet implemented across all computing environments data warehousing is becoming popular as hardware becomes more powerful and cost effective..."

Tags:distributed data warehousing systems, data warehouses

Abstract
The paper explains that data warehousing is a method of bringing together all of a company's data from various computer systems, including those relating to customers, employees, vendors, products, inventory and financials. The data warehouse connects different databases together in order to offer a more comprehensive data set for making decisions. The paper considers how different ways of shaping such warehouses have been developed and how certain organizations have used them to gain control over data and over decision making. The paper concludes that evidence shows how, for organizations that can develop a strong system, data warehousing is worth the cost.

Outline:
Abstract
Introduction
Data Warehousing
Development of Data Warehousing
Examples of Data Warehousing
Conclusion

From the Paper
"Databases have traditionally been used to track individual records, but today's computers can handle data of a much different type that is not easily converted into traditional relational database formats. In many operational information systems, the data represent a structured collection. One record exists for each item and each has the same set of attributes. Information systems also have validation and referential integrity requirements. There should be no duplications, and multiple references for the same classification of data should have the same characteristics (for example, the same address for multiple contacts at a single company in a customer database)."

Tags:database, records, classification, information, computer, system

Abstract
This paper discusses the process of data mining, in which electronic data is collected, stored in data warehouses, and then culled for specific consumer information in order to understand consumer buying habits to increase business profits and tailor advertising strategies to specific individuals and groups. The pros and cons of data mining are discussed, including the potential for identity theft and fraud.

From the Paper
"Data mining is the process of finding connections between various kinds of data within a large pool, or warehouse, of information. This is a useful process for businesses in that computer technology can be used to sift through and find relevant information amongst large stores of data in order to understanding patterns in consumer's buying practices and use of services (Palace); this information can then help businesses, from marketing and advertising firms, to factories, to the research and design departments of manufacturing companies to increase profits and/or cut costs of producing such goods and services. For instance, if a company..."

Tags:data mining, data warehouses, marketing

Abstract
This paper relates that the use of data mining, its adjunct technologies for text mining and the ability to interpret, analyze and create linguistic models from unstructured content is revolutionizing the concept of data mining away from being purely used for structured content in data warehouses to now encompass unstructured content found throughout organizations globally.
The paper then provides insights into various areas of data mining, and the currently high levels of growth analytics use and applications software are experiencing as a result.

Outline:
Executive Summary
Using Data Mining in Business Research
Exploring the principles of Data Mining in Business Research
Predictive Methods in Data Mining

From the Paper
"A second predictive approach is called deviation detection. The purpose of this method is to discover the most significant changes in data from previously measured or median values. An example of the type of use for this predictive approach would be the development of strategies for selling tickets to frequent flyers who booked months in advance versus those that consistently book within a few weeks of their departure. A third approach to using data mining to predict future outcomes is using the classification approach, or technique. This predictive approach of classification uses a collection of records (training set) -- each record contains several attributes, one of them is the class (Ng & Han, 10). The task is to find a model for the class attribute as a function of other attributes, so, after that, previously unknown records can be assigned a very accurate class."

Tags:significant, changes, descriptive, analytics, data, modeling

Abstract
The paper examines the data warehousing project Wal-Mart implemented in June, 2007 to streamline business analytics and reporting from its stores, in addition to synchronizing demand with suppliers through its retail link system. The paper emphasizes that, for Wal-Mart, information is what makes its entire supply chain and retail operations work. The paper concludes that the use of data warehouses significantly contributes to Wal-Mart's ability to stay ahead of the trends and costs that influence its business while being better aligned with customers' needs.

Outline:
Introduction
Wal-Mart's Selection of Data Warehousing Strategies
Conclusion

From the Paper
"For Wal-Mart, creating data warehouses that provide demand visibility back to its supply chain is critical if the key measures of its internal efficiency are going to be attained. These include inventory turns and delivering orders that are flawless in execution and quality. The timeline for Wal-Mart's data warehousing project began in 2005 when the CIO and VPs in the IT Division realized that there were several hundred stores that were not specifically having their data read into the Master Data Management (MDM) hierarchy that had been created. Further, these retail locations, many of them superstores only had limited visibility into what products would be available for delivery in the next 72 hours (Duff, 14) which made the many processes required for forecasting demand quite time consuming and imprecise (Foote, Krishnamurthi, 15). All of these factors combined with the growing reliance on the Retail Link system in Wal-Mart for planning new store expansions made an enterprise-level data warehousing project a high priority."

Tags:business, analytics, demand, suppliers, efficiency

Abstract
Data mining is the extraction of hidden predictive information from large databases. This paper examines the effect that data mining has on the current corporate climate. It defines data mining and examines the scope of its existence and effects on overall industry and the rest of the world. The paper also explains the basics of the technology behind data mining and how these tools will interact with localized software. Examples of how data mining technology can be profitably used, as well as how it will use the data warehouse architecture to evolve existing software to develop new ways to collect and interpret information, is also looked at.

From the Paper
"Model building itself is not a new technology; it is in fact something that has been around for a very long time. Since the beginning of computer technology, modeling has been a method to finding solutions. Computers work just as humans do by collecting information from a variety of differing situations and attempting to put it together in such a way that makes sense. With computers, there are more resources as well as faster integration of the information so the model building process is easy, fast and efficient. It also is much more complex than anything that a human can build which means the answer is in more depth and more accurate."

Tags:technology, gigabyte, algorithms, warehouse

Abstract
Many authors have provided an enormous amount of literature on data warehousing concepts, processes, and characteristics. However, the key to a successful data warehouse is proper implementation. Previous publications have come up with different ideas and methods to implement a data warehouse successfully. Managers don?t have enough time to go through all these readings This paper provides an integration of the various implementation guidelines with practical examples ranging from the FBI to Wal-Mart.

I. Introduction
II. Basic Definitions and Concepts of Data Warehousing
III. Brief History of Data Warehousing
IV. Data Warehousing Characteristics
V. Drivers of Data Warehousing
VI. Data Warehousing Process
VII. Current Issues and Practices of Data Warehousing
VIII. Guidelines in Implementing a Data Warehouse
IX. Conclusions, Limitations, and Future Research Guidelines
X. References

From the Paper
"Data warehousing is one of the hottest developments of the 1990s. In 1998, the expenditure on data warehousing was $14 600 million (META Group 1996). It is estimated that 95% of the Fortune 1000 either have a data warehouse or are planning to develop one (META Group 1996). A data warehouse may help increase a company's sales by supporting decision-making and understanding consumer behavior. For example, Office Depot sales increased by $117 million after investing on data warehousing (Anthes 2003)."

Tags:warehousing, fbi, walmart