...* How the Internet works * Packet-switching technology Actual data transmission takes place sporadically, rather than continuously. Data to be transmitted is divided into small packets of information and labeled to identify the sender and recipient. These are sent over a network and then reassembled at their destination. If any packet did not arrive or was not intact, original sender requested to resend the packet. -> This enables millions to transmit data at the same time. * Understand the importance of standards (protocols) * What problems are and tasks are involved in networks and why are standards important for networks to run? Computers and applications of different kinds need to use the same network. Packets could get altered/lost/out of sequence. Many computers send packets simultaneously. There are lots of different destinations, routes, and sometimes some of them ‘close down’. * Bandwidth Bandwidth is transmission capacity of a computer or communications channel, measured in bits per second (bps). * TCP/IP protocol and layered standards of the Internet * Application layer, network layer (TCP/IP) Applications layer (e.g. HTTP, FTP) specifies how application programs communicate. Network layer (e.g. TCP/IP) consists of transport layer and internet layer. Transport layer (TCP) breaks, reassembles messages into packets. Internet layer (IP) specifies the address a packet is headed to. * Internet addressing and architecture:...
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...Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 What is Cluster Analysis? Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups Intra-cluster distances are minimized Inter cluster Inter-cluster distances are maximized © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2 Applications of Cluster Analysis Understanding – Group related documents p for browsing, group genes and proteins that have similar functionality, or group stocks with similar price fluctuations Discovered Clusters Industry Group 1 2 3 4 Applied-Matl-DOWN,Bay-Network-Down,3-COM-DOWN, Cabletron-Sys-DOWN,CISCO-DOWN,HP-DOWN, DSC-Comm-DOWN,INTEL-DOWN,LSI-Logic-DOWN, Micron-Tech-DOWN,Texas-Inst-Down,Tellabs-Inc-Down, Natl-Semiconduct-DOWN,Oracl-DOWN,SGI-DOWN, Sun-DOWN Apple-Comp-DOWN,Autodesk-DOWN,DEC-DOWN, ADV-Micro-Device-DOWN,Andrew-Corp-DOWN, Computer-Assoc-DOWN,Circuit-City-DOWN, Compaq-DOWN, EMC-Corp-DOWN, Gen-Inst-DOWN, Motorola-DOWN,Microsoft-DOWN,Scientific-Atl-DOWN Fannie-Mae-DOWN,Fed-Home-Loan-DOWN, Fannie Mae DOWN Fed Home Loan DOWN MBNA-Corp-DOWN,Morgan-Stanley-DOWN Baker-Hughes-UP,Dresser-Inds-UP,Halliburton-HLD-UP, Louisiana-Land-UP,Phillips-Petro-UP,Unocal-UP, Schlumberger-UP Technology1-DOWN Technology2-DOWN Financial-DOWN Oil-UP Summarization – Reduce the size of large data sets C uste g precipitation Clustering...
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...& Industrial Engineering Submission Date: 29 October 2011 Copyright © 2011 LCP Mohale Declaration I, LCP Mohale, student no 46059091, hereby declare that the work contained in this report is my own original work and I have acknowledged all additional sources that I have used/quoted directly. This report and all the information it contains may not be reproduced by any means without my written consent. Acknowledgement I am grateful to individuals who have assumed an instrumental role in bringing this project to completion: * My employer, Anglo American management for providing the environment and opportunity to execute this project. * Mr Tumisho Kekana, Commodity Implementation Manager for Underground Mining Equipment and Heavy Mining Equipment at Anglo American, for guidance and selfless mentoring throughout the project * Mr S Chikumba, Senior Lecturer: Mechanical and Industrial Engineering at UNISA, for his guidance and contribution in developing the research concept, and approach. His contribution played an essential role in the foundation and formulation of this project * Mr Vusi Mabena, for continually supporting, motivating and encouraging me at all times to keep on * Zimele Small Business hub managers and facilitators(Secunda, Middleburg, New Vaal and Witbank hub) for tirelessly assisting in with the surveys * All the suppliers who have participated in the projects * Last but not least, my colleague Miss Vuyiswa...
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...Privacy Endangerment with the Use of Data Mining An emergent Information Technology (IT) issue that has been rising in the past few years has been data mining. Data mining is utilized to retrieve personal identifiable information provided by individuals through the use of Internet services such as: social media networks, email, and other networks that contain data bases full of personal information. If such data retrieval if not done careful, it can cause ethical issues for the companies that are involved. The ethical issues related to data mining are violation of privacy, confidentiality, and respect of persons’ rights. Issues that required the immediate attention regarding data mining are: What stops corporations from sharing personal identifiable information with other companies?; How effectively and ethically data mining is use by the government?; Is our privacy and confidentiality truly protected? Social network companies such as Facebook, Twitter, and Google provide users agreements upon joining their services. These agreements underline how the information provided by the user will be utilize by the company and it allows the user to understand how to protect their personal identifiable information while utilizing these social network sites. These companies pride themselves in protecting users’ personal information. However, what happens when the company or an unethical company employee violates these agreements? Personal identifiable information is then released...
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...Data Mining I found the topic of data mining very interesting in that it uncovers coveted information needed for improving and refining our daily lives. Information regarding traffic patterns, flight arrivals, consumer purchases, education, is collected and analyzed to improve a particular model. The data mining process is designed to gather information from a targeted sample which will enable companies to refine their business model in order to become more profitable. This process is not engineered to accumulate more information for an organization but to extract more meaningful information and correlate patterns of information that already exists in their data base. The importance of this information will allow companies to better analyze information to make quick effective decisions which will spur productivity. Data mining in turn can monitor and analyze these results to effectively manage assets. Organizations will be able to better predict the results of their decision making. How Data Mining Works A sample size is created by targeting large amounts of relevant information that is small enough to process. The information is then studied to find relationships which were anticipated , analyze trends, and recognize irregularities to gain knowledge for a design. “The data is then modified to transform the variables to focus the model selection process. A model is then selected by using analytical tools to search for a combination of data that reliably...
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...Data Mining Prepared by: Kirsten Sullivan Strayer University CIS 500 Dr. Baab September 9, 2012 Data mining is a concept that companies use to gain new customers or clients in an effort to make their business and profits grow. The ability to use data mining can result in the accrual of new customers by taking the new information and advertising to customers who are either not currently utilizing the business's product or also in winning additional customers that may be purchasing from the competitor. Generally, data are any “facts, numbers, or text that can be processed by a computer.”1 Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes operational or transactional data such as, sales, cost, inventory, payroll, and accounting. Data mining also known as “knowledge discovery”, is the process of analyzing data from different perspectives and summarizing it into useful information- information that can then be used to increase revenue, cuts costs, and continue the goals outlined for the company. Data mining consists of five major elements: “Extract, transform, and load transaction data onto the data warehouse system, store and manage the data in a multidimensional database system, provide data access to business analysts and information technology professionals, analyze the data by application software, present the data in a useful format, such as a graph or table.”2...
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...amounts of information and data. Initially, with the emergence of computers and the means for mass digital storage, we started collecting and saving all sorts of data, counting on the power of computers to help sort through this mix of information. Unfortunately, these huge collections of data stored on structures that are not similar, very quickly became too much. The production of data is expanding at an incredible rate. Expert now point to a 4300% increase in annual data generation by 2020. In 2007 the estimated information content of all human knowledge was 295 exabyte, CSC predicted that in 2020 data production will be 44 times more greater than it was in 2009....
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...2. Data mining search parameters A data mining algorithm is a set of heuristics and calculations that creates a data mining model from data. To create a model, the algorithm first analyzes the data you provide, looking for specific types of patterns or trends. The algorithm uses the results of this analysis to define the optimal parameters for creating the mining model. These parameters are then applied across the entire data set to extract actionable patterns and detailed statistics. The mining model that an algorithm creates from your data can take various forms, including: * A set of clusters that describe how the cases in a dataset are related. * A decision tree that predicts an outcome, and describes how different criteria affect that outcome. * A mathematical model that forecasts sales. * A set of rules that describe how products are grouped together in a transaction, and the probabilities that products are purchased together. Microsoft SQL Server Analysis Services provides multiple algorithms for use in your data mining solutions. These algorithms are implementations of some of the most popular methodologies used in data mining. All of the Microsoft data mining algorithms can be customized and are fully programmable using the provided APIs, or by using the data mining components in SQL Server Integration Services. You can also use third-party algorithms that comply with the OLE DB for Data Mining specification, or develop custom algorithms that can be...
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...recognize the importance of data, the management issues that relate to it, and its life cycle. Other objectives include relating data management to multimedia and document management, explaining the concept of data warehousing, data mining, analytical processing, and knowledge discovery management. An Overview Section 12.1 – The Need for Business Intelligence – The section serves as an overview of Business Intelligence and its use in business. It discusses the problems associated with disparate data stores where data are not integrated into a single reporting system. The section discusses the technologies involved in Business Intelligence and the vendors involved. It also talks about predictive analytics, alerts and decision support. Section 12.2 – BI Architecture, Reporting and Performance Management – This section discusses the modes of data extraction and integration into a standardized, usable and trustworthy one. It also discusses the different types of reporting systems available to organizations, data mining, query and analysis. The section provides an insight into Business Performance Management (BPM) as a way for business managers to know if their organizations are achieving their strategic goals Section 12.3 – Data, Text and Web Mining and BI Search – This section discusses data mining technology, tools, and techniques. Information types, data mining applications, text mining, and web mining are explored. There is also a discussion of the failures of data mining. Section...
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...Data Mining Professor Clifton Howell CIS500-Information Systems Decision Making March 7, 2014 Benefits of data mining to the businesses One of the benefits to data mining is the ability to utilize information that you have stored to predict the possibilities of consumer’s actions and needs to make better business decisions. We implement a business intelligence that will produce a predictive score for those consumers to determine these possibilities. Predictive analytics is the business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization. (Impact, 2014) The usefulness of predictive scoring is obvious. However, with no predictive model and no means to score your consumer, the possibility of gaining a competitive edge and revenue is also predictable. To discover consumer buying patterns from a transaction database, mining association rules are used to make better business decisions. However because users may only be interested in certain information from this database and do not want to invest a lot of time in searching for what they need, association discovery will assist in limiting the data to which only the end user needs. Association discovery will utilize algorithms to lessen the quantity of groupings of item sets or sequences in each customer...
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...Grading Rubric, n.d., p. 2) 2. “Managing complex, cooperative interactions among network partners” (FLA Grading Rubric, n.d., p. 2) 3. “Aligning incentives among networked partners to have a reason to stay connected” (FLA Grading Rubric, n.d., p. 2) 4. “Managing the strategic network and controlling its operations” (FLA Grading Rubric, n.d., p. 2) These areas should help catapult your company to the status of a fortune 500 company. We would be known worldwide as one of the companies in America that has what it takes to not only compete in a global market, but be sustainable in a global market. “Fortune magazine takes into account the businesses' growth, as measured by stock earnings and investment returns, assets, revenue and profit when compiling the list.” (Tran, n.d., para. 3) Strategic Outsourcing If order to businesses to remain competitive, they must walk a fine line between costs and quality. Outsourcing plays an intricate part in this process. First off what is outsourcing? “Outsourcing has evolved beyond being viewed as a purely tactical exercise to reduce costs and increase operational efficiencies.” (Singhal, n.d., para. 1) SCF plans on utilizing strategic outsourcing to “adapt flexibly to business change, improve quality and productivity, respond quickly to competition, and penetrate new markets”. (Singhal, n.d., p. 1) This service can encompass anything from “Application Development and Maintenance (ADM) to business process outsourcing to setting up turnkey...
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...CHAPTER ONE INTRODUCTION 1.1 Background of the Study The mining industry in Ghana, is an important sector for the socio economies of countries that have substantial gold deposits. To achieve rapid economic development, many countries resort to various activities to exploit natural resources. One of such activities is mining. Consequently, mining is an important economic activity which has the potential of contributing to the development of areas endowed with the resource. In North America, raw mineral production in 1998 was valued at approximately US$ 70 billion. The industry employs approximately 1 million people (Mbendi Profile, 2005). In Peru, the mining sector accounts for 50% of the country’s annual export earnings. During 1993, the mining industry’s contribution to the Peruvian economy was represented by $240m paid in taxes; $400m spent on local purchases; $280m in imported goods and accounted for over 11% of GDP (Acheampong, 2003). In South Africa, where gold is the largest mineral foreign income earner, gold mining alone contributes 27.4% in mineral revenues. The gold industry is also responsible for 56% of South Africa’s mine labour force (Mbendi, 2005). In Ghana, the sector plays a vital role in the development of the economy. In 2000, minerals accounted for 38.96% of total export earnings, followed by cocoa (22.51%) and timber (9.03%) (ISSER, 2001). The mining sector now contributes 41% to the country’s foreign exchange and is the leading foreign exchange earner. Of...
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...Data Mining/Data Warehousing Matthew P Bartman Strayer University Ibrahim Elhag CIS 111– Intro to Relational Database Management June 9, 2013 Data Mining/Data Warehousing When it comes to technology especially in terms of storing data there are two ways that it can be done and that is through data mining and data warehousing. With each type of storage there are trends and benefits. In terms of data warehousing there are 5 key benefits one of them being that it enhance business intelligence. What this means is that business processes can be applied directly instead of things having to be done with limited information or on gut instinct. Another benefit of data warehousing is that it can also save time meaning that if a decision has to be made the data can be retrieved quickly instead of having to find data from multiple sources. Not only does data warehousing enhance business intelligence and save time but it can also enchance data quality and consistency.This is accomplished by converting all data into one common format and will make it consistent with all departments which ensures accuracy with the data as well. While these key benefits another one is that it can provide historical intelligence which means that analayze different time periods and trends to make future predictions. One other key benefit is that it provides a great return on investment. The reason being that a data warehouse generates more revenue...
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...Management Information Systems are important and how the data from these systems can help drive positive business outcomes. The proposal will provide you with reasons as to why analytics should be used and how they will help support growth and stability within the company. It will also provide you with some drawbacks and challenges that might be faced with using analytics, but will also look at ways that those issues can be avoided if the processes is implemented and used properly. This proposal will provide you with sound reasoning as to the benefits that business analytics can provide for the company and how it will help it to grow into the future. It will also review techniques and tools that are used for the gathering and processing of data. How this data can be used to the benefit of the organization providing it with information that will help the business with long-term positive outcomes. Implementation Plan The Art Institutes is a system of schools that provides educational services to students in several areas including fashion, photography, culinary, interior design and media. It recent years there has been a decline in the enrollment of student and in students that stays in the program. By using business analytics the school could gather information and utilize it in making improvements in their enrollment and with their retention of students. By using business analytics to look at different scenarios and what there outcomes would be...
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...exploration of an organization’s data with emphasis on statistical analysis. It describes the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics is used by companies committed to data-driven decision making. It focuses on developing new insights and understanding of business performance based on data and statistical methods. BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Business analytics makes extensive use of statistical analysis, including explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. Data-driven companies treat their data as a corporate asset and leverage it for competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business and an organizational commitment to data-driven decision making. Once the business goal of the analysis is determined, an analysis methodology is selected and data is acquired to support the analysis. Data acquisition often involves extraction from one or more business systems, cleansing, and integration into a single repository such as a data warehouse or data mart. The analysis is typically...
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