BUS211f(2) ANALYZING BIG DATA I1 Spring 2014—MW 8:00–9:20 am Location: Sachar 116 (International Hall) Prof. Bharatendra Rai 313-282-8309 (mobile) brai@brandeis.edu Office: Sachar 1C Hours: MW, 9:30 – 10:15 and by appointment TA: TBD This is a two credit module that examines the opportunities and industry disruption in an era of massive, high velocity, unstructured data and new developments in data analytic. We treat some strategic, ethical, and technical dimensions of big data. The technical foci
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marketing data and consumer privacy and what consumer really think. It shows that more than half are concerned that their private information is used for marketing purposes. Contradicting are that most people do not use “do not track” features. 40 % would give up personal information in exchange for free products. More than half of people do not read privacy policies online before making a purchase but most people expect consumer protection groups to monitor how brands use their personal data We talked
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Data Warehouse Design for E-Commerce Environment Il-Yeol Song and Kelly LeVan-Shultz College of Information Science and Technology Drexel University Philadelphia, PA 19104 (Song, sg963pfa)@drexel.edu ABSTRACT Data warehousing and electronic-commerce are two of the most rapidly expanding fields in recent information technologies. In this paper, we discuss the design of data warehouses for e-commerce environment. We discuss requirement analysis, logical design, and physical design issues in e-commerce
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Big data spans four dimensions: Volume, Velocity, Variety, and Veracity. Volume: Enterprises are awash with ever-growing data of all types, easily amassing terabytes—even petabytes—of information. Turn 12 terabytes of Tweets created each day into improved product sentiment analysis Convert 350 billion annual meter readings to better predict power consumption Velocity: Sometimes 2 minutes is too late. For time-sensitive processes such as catching fraud, big data must be used as it streams
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THE BIG DATA CHALLENGES by William Thomas Info System Decision-Making Professor Raied Salman July 21, 2014 Volvo was founded in 1927 and, is one of the more popular brands of vehicles with over 400,000 cars sold in 2013. Their largest markets are the United States, Sweden, China, Germany and the UK. Volvo has taken vehicle diagnostics to another level by using the data transferred from their vehicles to improve the quality and performance of their overall fleet on the street and on the assembly
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Introduction Today there is great interest in the technology class BIG DATA, connected with the continued growth of data that have to operate large companies. The accumulated information for many organizations is an important asset, but handle it and benefit from it every day becomes more and more expensive. In my essay, I will try to describe the concept of "big data" and explain how it works. What "Big data" means? The concept of big data is rapidly gaining popularity. However, the exact idea of what
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Challenges and Opportunities with Big Data A community white paper developed by leading researchers across the United States Executive Summary The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of ``Big Data.’’ While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is currently a wide gap between its potential and its realization
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The Impact of Big Data By Ijaaz Lagardien Group 3A 214167542 1|Page Contents Plagiarism Declaration ....................................................................................................................................... 3 Abstract ............................................................................................................................................................. 4 Keywords ....................................................................
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Table of Contents 1.0 Background description of Amazon.com 3 2.0 What is Big Data? 4 2.1 The Business View 5 2.2 Technical View 5 3.0 Amazon.com and Big Data 6 4.0 Identification of Amazon.com’s "Big Data" needs 6 5.0 Big Data problems to be solved and Big Data solutions 6 6.0 What are AWS (Amazon.COM web services) problems and what is the solution for it 8 6.1 Kinesis other advantages 9 7.0 Conclusion 10 8.0 References 11 1.0 Background description of Amazon.com
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INTRODUCTION Big data is described as complex data collections that has very large size beyond the capability of traditional tools to capture, manage and process it efficiently. It is also use in many ways where it usually refers to both of data that being managed and used to store and process it by the technology which it helps to deduce different types of results using the same data that being stored intentionally or unintentionally. There are three characteristics of big data which are volume
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