...Adelman MBA 511 Report – Webcast 8/13/14 on Data Mining SAS (Statistical Analysis System) was originally developed as a project to analyze agriculture from 1966-1976 at North Carolina State University. As demand for such software grew, SAS Institute was founded in 1976. SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it. SAS provides a graphical point-and-click user interface for non-technical users and they provide more advanced options through the SAS programming language. On August 13 2014, SAS sponsored a web seminar titled “Analytically Speaking” the topic of the webcast was data mining techniques. Michael Berry and Gordon Linoff were the featured speakers, they have written a leading introductory book (on data mining) titled “Data Mining Techniques”. They discussed a lot of the current data mining landscape, including new methods, new types of data and the importance of using the right analysis for your problem (as good analysis is wasted doing the wrong thing). They also briefly discussed using ‘found data’ – text data, social data and device data. Michael Berry is the Business Intelligence Director at TripAdvisor and co-founder of Data Miners Inc. Gordon Linoff is co-founder of Data Miners Inc. and a consultant to financial, media and pharmaceutical companies. Data mining is the analysis step of the “KDD” (Knowledge Discovery in Databases). Data mining is an interdisciplinary...
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...www.pwc.co.uk The direct economic impact of gold October 2013 www.pwc.co.uk The work carried out by PricewaterhouseCoopers LLP ("PwC") in relation to this report has been carried out only for the World Gold Council and solely for the purpose and on the terms agreed between PwC and the World Gold Council. The report does not constitute professional advice. No representation or warranty (express or implied) is given as to the accuracy or completeness of the information contained in this report and, to the extent permitted by law, PricewaterhouseCoopers LLP, its members, employees and agents do not accept or assume any liability, responsibility or duty of care for any consequences to anyone acting, or refraining to act, in reliance on the information contained in this report or for any decision based on it. © 2013 PricewaterhouseCoopers LLP. All rights reserved. In this document, "PwC" refers to PricewaterhouseCoopers LLP (a limited liability partnership in the United Kingdom), which is a member firm of PricewaterhouseCoopers International Limited, each member firm of which is a separate legal entity. The direct economic impact of gold Contents Foreword ........................................................................................................................................................................1 Executive summary ...........................................................................................................................................
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...Top Data Management Terms to Know Fifteen essential definitions you need to know Fifteen Essential Data Management Terms We know it’s not always easy to keep up-to-date Contents with the latest data management terms. That’s why we have put together the top fifteen terms and definitions that you and your peers need to know. OLAP (online analytical processing) Star schema What is OLAP (online analytical processing) Fact table OLAP (online analytical processing) is computer processing that enables a Big data analytics Data modeling Ad hoc analysis user to easily and selectively extract and view data from different points of view. For example, a user can request that data be analyzed to display a spreadsheet showing all of a company's beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September, and then see a comparison of other product sales in Data visualization Extract, transform, load (ETL) Florida in the same time period. To facilitate this kind of analysis, OLAP data is stored in a multidimensional database. Whereas a relational database can be thought of as two-dimensional, a multidimensional database considers each data attribute (such as product, geographic sales region, and time Association rules (in data mining) Relational database period) as a separate "dimension." OLAP software can locate the intersection of dimensions (all products sold in the...
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...Information Analysis and Data Management Trends in Information Analysis and Data Management Over the last decade, advancements in digital technology have enabled companies to collect huge amounts of new information. This data is so large in scope, it has traditionally been difficult to process and analyze this information using standard database management systems such as SQL. The commoditization of computer technology has created a new paradigm in which data can be analyzed more efficiently and effectively than ever before. This report analyzes the some of the most important changes that are currently taking place within this new paradigm. The first part of this report covers trends in database analysis by analyzing the field of data mining. The report covers the topic of data mining by providing an explanation of it, and then by providing examples of real-world examples of data mining technology. Benefits and challenges of data mining are then provided. The second part of the report outlines an even more recent trend in data science, which is the increasing usage of noSQL databases to analyze “big data,” also referred to web-scale datasets. The most recent and major technological developments in the industry are then provided and described. Data Mining Background & Definition Data mining involves the process of discovering and extracting new knowledge from the analysis of large data sets. This is most often done through the use of data mining software, which identifies...
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...Project Title Use of Data mining by government agencies and practical applications (Describe the Data Mining technologies, how these are being used in government agencies. Provide practical applications and examples) Compiled By:- Sneha Gang (Student # - 84114) Karan Sawhney (Student # - 85471) Raghunath Cherancheri Balan (Student # - 86088) Sravan Yella (Student # - 87041) Mrinalini Shah (Student # - 86701) Use of Data mining by government agencies and practical applications * Abstract (Sneha Garg) With an enormous amount of data stored in databases and data warehouses, it is increasingly important to develop powerful tools for analysis of such data and mining interesting knowledge from it. Data mining is a process of inferring knowledge from such huge data. It is a modern and powerful tool, automatizing the process of discovering relationships and combinations in raw data and using the results in an automatic decision support. This project provides an overview of data mining, how government uses it quoting some practical examples. Data mining can help in extracting predictive information from large quantities of data. It uses mathematical and statistical calculations to uncover trends and correlations among the large quantities of data stored in a database. It is a blend of artificial intelligence technology, statistics, data warehousing, and machine learning. These patterns...
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...Rexer Analytics 4th Annual Data Miner Survey – 2010 Survey Summary Report – For more information contact Karl Rexer, PhD krexer@RexerAnalytics.com www.RexerAnalytics.com Outline • Overview & Key Findings • Where & How Data Miners Work • What’s Important to Data Miners • Data Mining Tools: Usage & Satisfaction • Overcoming Challenges & Optimism about the Future • Appendix: Where do Data Miners Come From? • Appendix: Rexer Analytics © 2011 Rexer Analytics 2 Overview & Key Findings © 2011 Rexer Analytics 3 2010 Data Miner Survey: Overview Vendors Corporate • Fourth annual survey NGO / Gov’t • 50 questions • Data collected online in early 2010 Academics Consultants • 10,000+ invitations emailed, plus promoted by newsgroups, vendors, and bloggers • Respondents: 735 data miners from 60 countries Note: Data from tool vendors (companies making data mining software) was excluded from many analyses. © 2011 Rexer Analytics Central & South America (4%) • Columbia 2% • Brazil 1% Asia Pacific • India 4% • Australia 3% • China 2% Middle East & Africa (3%) • Israel 1% • Turkey 1% North America • USA 40% • Canada 4% Europe • Germany 7% • UK 5% • France 4% • Poland 4% 4 Key Findings • FIELDS & GOALS: Data miners work in a diverse set of fields. CRM / Marketing has been the #1 field in each of the past four years. Fittingly, “improving the understanding of customers”...
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...ITT TECHNICAL INSTITUTE DATA MINING REPORT ONE AND TWO Shirly Mu, Todd Hughson, James Wall PROBLEM SOLVING THEORY ONSITE COURSE GS1140 INSTUCTOR LJILJANA MORRIS Today we are doing a project report on Costco. For Sol and Robert Price in 1976 they asked friends and family to help out with an opening price of two point five millon to open Price Club on July twelfth, they open their shop in an air hanger on Boulevard in San Diego, California. They were originally going to serve only small business. Mr. Price found out that it will be more beneficial to serve select customers. Costco was founded by James Sinegal and Jeffery H. Brotman. Costco opened its doors in 1983 in Seattle, Washington. Price Club and Costco later merged and renamed the business PriceCostco. And in 1997 due to its success the name was changed again to Costco Wholesale. (About History, 2014) Costco Wholesale stores headquarters are located in Issaquah, WA. The mission statement for Costco is to continually provide our members with quality goods and services at the lowest possible prices. (Farfan, 2014) There are three types of membership cards at Costco; Executive membership, Business membership and Gold membership. The one I have is executive membership this cost about one hundred and twenty dollars sounds like a lot but I’ll be able to get two percent back in my shopping. If I do not get back more than fifty five dollars back for the entire year they will give me that amount or the two percent of...
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...computing, middleware, and industry standards as relating to the enterprise data repository. Data warehousing, data mining, and data marts are covered from an enterprise perspective. Policies Faculty and students will be held responsible for understanding and adhering to all policies contained within the following two documents: • • University policies: You must be logged into the student website to view this document. Instructor policies: This document is posted in the Course Materials forum. University policies are subject to change. Be sure to read the policies at the beginning of each class. Policies may be slightly different depending on the modality in which you attend class. If you have recently changed modalities, read the policies governing your current class modality. Course Materials Coronel, C., Morris, S., & Rob, P. (2011). Database systems: Design, implementation and management (9th ed.). Mason, OH: Cengage Learning. Eckerson, W. W. (2011). Performance dashboards: Measuring, monitoring, and managing your business (2nd ed.). Hoboken, NJ: John Wiley & Sons, Inc. Hoffer, J. A., Ramesh, V., & Topi, H. (2011). Modern database management (10th ed.). Upper Saddle River, NJ: Pearson. Linoff, G. S., & Berry, M. J. A. (2011). Data mining techniques: For marketing, sales, and customer relationship management (3rd ed.). Indianapolis, IN: Wiley Publishing, Inc. Ponniah, P. (2010). Data warehousing: Fundamentals for IT professionals (2nd ed.). Hoboken, NJ:...
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...Data Mining for Predictive Analytics Stanley Kenton Marks December 11th, 2012 Abstract Simply collecting data for research is nearly a faux pas in today’s competitive web-market. Analysts are now looking toward the predictive analytics of association discovery in web and data mining, to find Business Intelligence of clustering sub=populations while eliminating errors to keep collected data valid. In the midst this data crunch are fears of lost privacy. Do not fear. Creative innovations are bringing mash-ups to our diversity. Data Analytics Report Useful information, knowledge and finding some unexpected results can “strike it rich” with added creative thinking. Data mining supplies analysts, investors, and traders with customers buying patterns, historical trading rules, even fraudulent behavior for insurance claims. Predictive analytics is used in web mining by analyzing user’s movements from one web content to another. Collecting the data of where a user browses and the content they are seeking can become knowledge if the analyst understands the patterns (Turban & Volonino, 2011). An Association Discovery Algorithm is a tool of data mining where new rules are discovered such that if one item is present then another will also be found. This type of knowledge benefits analyst’s predictability of future probabilities and is very useful to the marketing department, (Ranjan, 2008). A traditional example you...
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...MSc. Information System Management Kyaw Khine Soe (3026039) Data Mining and Business Analytics Boston Housing Dataset Analysis. Table of Contents Introduction 3 Problem Statement 3 The associated data of Boston 5 Data pre-processing / Data preparation 8 Clustering Analysis 11 Cluster segment profile 17 Regression Analysis 18 Predictive analysis using neural network node 19 Decision tree node 21 Regression node analysis 23 Model Comparison 24 The recommendation and conclusion 26 Bibliography 27 Introduction This report included part of assignment for the Data Mining and Business Analytics. This report based on the Boston Housing Dataset to describe prediction, cluster analysis, neural networks and decision tree nodes. Boston Housing is a real estate related dataset from Boston Massachusetts. This is small dataset with 506 rows can show prediction of housing price and regressing using decision trees and neural networks over this dataset. This report shows analysis of the property price over the size, age of property, environment factor such as crime rate, near the river dummy, distanced to employment centers and pollution. Problem Statement In relation to housing intelligence, real estate are usually concerned with following common business concerns: 1. Which area are high rates of crime? How crimes rates effected on housing price? How can reduce the crime? 2. Which area is most/lease house price base on rooms in house/ area and...
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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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...According to Lee, the most popular definition is a data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process (2014). Basically a data warehouse is a copy of transaction data specifically structured for query and analysis. According to Frand, 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, cut costs, or both (1997). There are many benefits of data warehousing. Yes, it will cost large amounts of money from businesses to have a data warehouse but, in the long run it is worth it to have in a corporation. One benefit is that data warehouses stores and presents information in a way that allows management to make important decisions (Prathap, 2014). Management and even executives can look at the business as a whole instead of by each department. According to Prathap, another benefit of data warehouses is their ability to handle server tasks connected to querying which is not used in most transaction systems (2014). Creating queries and reports can take time and with data warehousing, the server can handle the tasks in a timely fashion. Again, according to Prathap, one of the most important benefits of data warehouses is that they set the stage for an environment where a small amount of technical knowledge about databases...
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...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 12...
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...Data Mining for Fraud Detection: Toward an Improvement on Internal Control Systems? Mieke Jans, Nadine Lybaert, Koen Vanhoof Abstract Fraud is a million dollar business and it’s increasing every year. The numbers are shocking, all the more because over one third of all frauds are detected by ’chance’ means. The second best detection method is internal control. As a result, it would be advisable to search for improvement of internal control systems. Taking into consideration the promising success stories of companies selling data mining software, along with the positive results of research in this area, we evaluate the use of data mining techniques for the purpose of fraud detection. Are we talking about real success stories, or salesmanship? For answering this, first a theoretical background is given about fraud, internal control, data mining and supervised versus unsupervised learning. Starting from this background, it is interesting to investigate the use of data mining techniques for detection of asset misappropriation, starting from unsupervised data. In this study, procurement fraud stands as an example of asset misappropriation. Data are provided by an international service-sector company. After mapping out the purchasing process, ’hot spots’ are identified, resulting in a series of known frauds and unknown frauds as object of the study. 1 Introduction Fraud is a million dollar business and it is increasing every year. ”45% of companies worldwide have fallen victim...
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...Helping mining and mineral companies make the grade Improving your throughput and material grade Mining is simple, right? It is all about getting more product out of the ground, processing, and transportation. Whether you’re producing gold, diamonds, platinum, coal, iron ore, aluminium, or copper – the more you dig and process, the more you sell. But how much control and monitoring do you have over your work in process? How visible is your material as it flows through stockpiles, smelters and concentrators? Do you have visibility of progress toward your throughput goals and objectives? These are important questions mining operators address every day. These questions are imperative to success, allowing mining organisations to be competitive in global markets, increase shareholder returns, increase operational efficiencies, and reduce energy consumption. > What is Ampla Inventory? Ampla Inventory is a module of Ampla MES software solution. The total Ampla solution is working today for some of the world’s largest mining organisations. Ampla bridges communication between the plant floor and corporate or as they say, it is at the coalface of the operation. Ampla provides mining organisations with the competitive edge to realise business and operational targets, ensuring increases in market share and shareholder return. + Growing demand for raw materials and minerals. = Real-time visibility of your stockpiles and work...
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