Abstract Begins by defining datawarehouse and describing the business uses for the technology. This is followed by a focus of datawarehouse management. Three components of datawarehouse management are examined. In addition, a discussion on the assurance of safety and privacy, which are needed to maintain the integrity of the datawarehouse, is included. The discussion also focuses on the availability and reliability of the datawarehouse. Finally the paper investigates different management tools that are used to maintain the datawarehouse.
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)"
Abstract This paper discusses the "architecture" of datawarehouses and briefly describes possible future developments in datawarehouses as well as restrictions in datawarehouse technology.
Table of Contents
Introduction
Data Warehousing: Brief History
DataWarehouse 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."
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, datawarehouses
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 datawarehouse 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 The writer looks at different types of datawarehouses 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 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 datawarehouse 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."
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 datawarehouses 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."
Abstract Many authors have provided an enormous amount of literature on data warehousing concepts, processes, and characteristics. However, the key to a successful datawarehouse is proper implementation. Previous publications have come up with different ideas and methods to implement a datawarehouse 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 DataWarehouse 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)."
Abstract The paper explains that data mining is a process whereby enterprises or organizations in any industry approach their respective data and databases in a more constructive and targeted manner to produce actionable business strategies. As some researchers have observed, data mining and data warehousing are becoming more prevalent because of the large quantities of data stored in various systems and the number of business decisions made based on the data.
From the Paper "Thus, data mining and data mining techniques have risen to prominence with the elevation in importance of databases and, more recently, the development of data warehouses that have changed the complexion of industry in all sectors. Data mining and data warehousing solutions have been especially important in customer relations management (CRM) and in the healthcare industries for example."
A study proposal to further explore the degree to which data warehousing has been effective in assisting companies with the process and activities of forecasting, as well as in gaining competitive advantage.
Abstract This paper presents a study that aims to further establish the degree to which data warehousing has been used by organizations in achieving greater competitive advantage within the industries and markets in which they operate. In chapter One of this paper, an introduction of the study is provided, with the overall aims and objectives of the research proposal discussed. Chapter Two involves literature review on the subject. Chapter Three explains the research methodology, and Chapter Four uses this proposal on four case studies. Finally, Chapter Five provides a discussion and a review of the results.
Table of Contents
Introduction
Aims of the Study
Objectives of the Study
Significance of and Justification for the Study
Literature Review
Data Warehousing: Background
Deployment Obstacles
DataWarehouse Design
Benefits and Disadvantages Associated with Data Warehousing
Conclusions
Research Methodology
Research Design
Data Collection
Data Analysis
Results of the Study
Case Study One: Godrej Consumer Products Limited
Case Study Two: Safeway
Case Study Three: Wachovia Corporation
Case Study Four: Standard Chartered Bank
Discussion
Review of the Results
References
From the Paper "Three of the companies were in periods of ongoing growth in relation to the evolution of data warehousing and its use within the companies while one company was still in the initiation-early deployment phase. While it would appear that some were in the maturity stage, most had specific plans for using the data warehouse as the basis for launching new business activities and strategies. On the basis of this evidence, it is particularly important to note that even during the initiation phase, it was possible for companies to begin to recognize gains in competitive advantage, which further supports the potential for data warehousing to aid businesses in gaining competitive ground."
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 datawarehouses 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."
Abstract The paper discusses that the integral role of warehouse management in the overall supply chain of any organization makes the optimal performance critical for any company to achieve business objectives. The paper confirms that warehouse management systems (WMS) must optimize incoming inventory and outgoing product movement, while compensating for their physical and financial characteristics.
The paper states that companies need to be as efficient and economical as possible in managing their supply chains and warehouse planning and optimization is critical for their ability to compete globally.
From the Paper "While in previous generations of WMS systems, the main dynamic forcing change has been the need for controlling costs and for accounting for inventory, the state-of-the-art WMS today is being used for making an organization more capable of responding quickly and accurately to the needs of customers. This demand-driven aspect of WMS implementations is also being increasingly built on existing facilities that are being re-designed to better support optimization logic of these WMS systems. Another dynamic forcing the growth of state-of-the-art WMS systems is the need for increased visibility to all warehouse activities, including inventory and order status. With the increasingly strong level of analytics available from software vendors, many organizations are opting to create scorecards to measure the performance of their WMS systems and benchmark them over time. A side-benefit of this high level of quantification is the ability to track warehouse employee productivity over time, find those processes that need to be better managed so warehouse employees will be more efficient and overall, and cut down on the level of turnover in warehouse operations."
Abstract Data mining has become a very important concept today and is used by companies all over the world to increase their profits and target the right market. The paper talks about the different aspects of data mining, tools used, and future trends in data mining. Data mining benefits are discussed in detail, and an entire discussion related to the trends in data mining is presented.
1-Background
2-Introduction
3-Data Mining Growth and Tools
4-The Data Mining Process
5-Data Mining Market Place Trends
6-The Data in Data Mining and Meta Data 7-Types of Data Mining Problems
8-Privacy and Ethical Sensitivity in Data Mining Results
9-Future Prospects of Data Mining
10-Works Cited
From the Paper "Data, particularly in the vast diversity and immense quantity that it is available to modern business, was till recently almost very hard to find and understand. Yet, the comprehension of data is the most crucial step to extracting the knowledge that it contains. The scenario has drastically changed today where data is much more easily available and has become more "meaningful" with the utilization of Data Mining. Today, technology offers business managers powerful new tools for gleaning knowledge from data-the essentials of data mining. Data mining has become increasingly important to mainstream companies to become more competitive both in their workings and their customer based relationships. Data mining, as such is of great interest because it is imperative for organizations to grasp the competitive value of information contained within their data repositories. There are a number of pertinent benefits of data mining. First of all, data mining provides the tools and techniques that are essential for optimization of customer relationships. Secondly, data mining provides an automatic method of discovering patterns in data. Thirdly, but not the least, data mining tools can identify the relationships that are actually present in historical data."
Abstract The paper discusses how the many advances in data and text mining are already revolutionizing the librarian profession. The paper explores how the ability of data mining tools to extract, transfer and load (ETL) massive amounts of data at a single time, is changing how all tasks in an organization get completed.
Outline:
Executive Summary
Content Integration Is Key
Data Mining
i) Principles of Data Mining
ii) Data Mining Timeline
Data Mining Implications for Librarianship
Text Mining
i) Text Mining Timeline
ii) Data Mining versus Text Mining
iii) Mining Blogs: An Example of How Text Mining Works
Text Mining Implications for Librarianship
Conclusion
From the Paper "At the intersection of text mining, linguistic analysis, statistical analysis, and latent semantic indexing techniques (Wikipedia Latent Semantic Indexing 2006). is the future of text mining that has the power to discover and report trending in highly unstructured content. At the center of text-mining's' rapid growth is the increasing sophistication of Natural Language Processing (CRM Buyer 2005). IBM and their significant research efforts in natural language processing are well documented on their website, as are the efforts and investments Microsoft is making."
Tags: latent, semantic, indexing, clustering, hosted, applications, Island, Data, Attensity
Abstract This paper describes that data structure is a way of storing data in a computer so that it can be used efficiently. The paper then goes on to describe how computers store the said data in terms of binary data presentations using Boolean logic. Furthermore, the paper describes data in the form of bits, along with converting binary data into decimals. Lastly, the paper talks about a computer's physical memory, which is based on one of two systems: (1) Random access memory (RAM), or (2) Read-only memory (ROM), and goes on to talk specifically about different coding systems.
From the Paper "Data directly supported by CPU are called primary data type or machine data type computers. CPUs also process complex data type such as string, array, text files, databases, and image data such as MP3, jpeg, and mpeg. However, 64-bit and the 128-bit use different math functions in order to maintain portability. In each case, there is a signed and unsigned integer type associated with each. Excess notation is a format that is used to represent a signed integer and represents numbers in order and at the transition point; the high-order bit is set at zero. This represents the excess number. Positive numbers are above in order, negative below (Burd 78). Zero represents the excess identifier therefore; the excess 16 notation shows the value for zero is the bit pattern for 16 that is 10000."
Tags: RAM systems, data structure, software, boolean logic, memory
