CASE 10-1: MINNETONKA WAREHOUSE Question 1: For each of the four work team sizes‚ calculate the expected number of trucks in the queue waiting to be unloaded. Size of team Number of trucks in queue 2 3.2 3 .5 4 .27 5 .12 Question 2: For each of the four work team sizes‚ calculate the expected time in the queue—that is‚ the expected time a truck has to wait in line to be unloaded. Size of team Expected time in queue 2 .8 hours 3 .125 hours 4 .067 hours 5 .030 hours
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What is Alpine Warehouse? Alpine Warehouse is a retailer of downhill and cross-country skis and snowboards. The company stocks the skiing gear by major brands such as K2 and Rossignol. Alpine Warehouse also sells outdoor clothing and winter-sports accessories along with a selection of skiing and snowboarding DVDs. Alpine Warehouse is committed to providing outstanding customer service‚ far beyond the expectations of their customers. They also strive to provide the best products and work towards making
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Business Intelligence Definitions • Data mining (knowledge discovery in databases): – Extraction of interesting (non-trivial‚ implicit‚ previously unknown and potentially useful) information or patterns from data in large databases • Data mining helps end users extract useful business information from large databases • Data mining is the exploration and analysis of large quantities of data in order to discover meaningful patterns and rules. • The goal of data mining may be to allow a corporation
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Effective Business Intelligence for SME As smaller firms face competition and grow‚ it’s imperative they make good decisions based on even better information When asked what business intelligence (BI) tools are used to measure their organizational performance‚ the common response by entrepreneurs might include Excel spreadsheets‚ report writers and canned reports. BI can be defined as the ability to extract actionable insight from data available to the organization‚ both internal and external‚
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A COMPARISON OF DATA WARE HOUSE DESIGN MODELS A MASTER’S THESIS in Computer Engineer ing Atilim Univer sity by BERIL PINAR BAŞARAN J ANUARY 2005 A COMPARISON OF DATA WARE HOUSE DESIGN MODELS A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES OF ATILIM UNIVERSITY BY BERIL PINAR BAŞARAN IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN THE DEPARTMENT OF COMPUTER ENGINEERING J ANUARY 2005 i Approval of the Graduate School
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Chapter 3 – Data Visualization Chapter 4 – Summary Statistics Data Mining for Business Intelligence Shmueli‚ Patel & Bruce © Galit Shmueli and Peter Bruce 2010 Data Visualization • “A picture is worth a thousand words” • Data visualization and summary statistics help condense data • Effective presentation • Supports data cleaning (identify missing values‚ outliers‚ incorrect values‚ duplicates) and exploring (combine some groups) • Helps identify suitable variables • Mandatory initial step for
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Graduate Programme – Term IV – AY 20012-13 Business Intelligence And Data Mining Group Assignment on NGO Donations Maximization Abstract The problem is associated to devising a strategy to maximize the profits from a Direct Marketing Campaign to a selected group of customers while minimizing costs . The exercise requires the use of Business Intelligence tools and techniques to build a model ‚ trained and tested on the historical data for the last year’s donation raising campaign
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Kobra Hemmati‚ Ali Bayat Data Warehousing Ofori Boateng‚ Jagir Singh‚ Greeshma‚ P Singh Wavelet Transform‚ Neural Networks and The Prediction of S&P Price Index: A Comparative Study of Backpropagation Numerical Algorithms Salim Lahmiri Dimensions of Spiritual Tourism in Tuiticorin District of Tamil Nadu in India – A Critical Analysis S. Vargheese Antony Jesurajan‚ S. Varghees Prabhu Architecture for Mobile Marketing in Android: Advertising Through Business Intelligence Paulo Renato de Faria Identification
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Business analytics and data mining provided 1-800-Flowers with all of the following benefits except: Select one: a. more efficient marketing campaigns b. increased mailings and response rates c. increased repeat sales d. better customer experience and retention On the commercial side‚ the most common use of data mining has been in ________ sectors. Select one: a. manufacturing and heath care b. online retail and government c. R&D and scientific d. finance‚ retail‚ and health
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Data mining and OLAP are the most common Business Intelligence technologies. The term Business Intelligence refers to computer based methods to identify and extract useful information from business data. Online Analytical Processing commonly known as OLAP provides summary data and generates rich calculations. OLAP is a class of systems that provide answers to multidimensional queries. OLAP is typically used in business reporting for sales‚ marketing and various such domains. OLAP enables the users
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