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    Data Structures

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    Data Structures and Algorithms DSA Annotated Reference with Examples Granville Barne Luca Del Tongo Data Structures and Algorithms: Annotated Reference with Examples First Edition Copyright c Granville Barnett‚ and Luca Del Tongo 2008. This book is made exclusively available from DotNetSlackers (http://dotnetslackers.com/) the place for .NET articles‚ and news from some of the leading minds in the software industry. Contents 1 Introduction 1.1 What this book is‚ and what

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    Big Data Challenges

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    The Big Data Challenges Lamin A Tunkara CIS 500 – Information Systems for Decision Making Dr. Edwin OTTO July 16‚ 2013 Abstract The paper explores the challenges of big data and the strategies deployed by Volvo Car Corporation in instituting an information technology infrastructural tool that helped the company tap into its massive data‚ bank to extract information for meaningful process improvements. The paper culled information from Dumbill (2012) who in an article

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    Introduction In the following essay‚ I will write about Adidas Company and will describe the relationship among its various quality costs which are failure costs‚ internal failure costs‚ external failure costs‚ appraisal costs and prevention costs. In addition‚ I will analyze the efforts the company should focus on to further improve the quality of its products/services. Therefore‚ this essay is divided to 3 main points which I think the company should focus to improve its quality. The first point

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    Design and Data

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    There are many key differences that are important to understand between data oriented and process oriented approaches to designing a new system. The system focus of the data views and process views are entirely different. The process view focuses on what the systems supposed to do and when‚ while the data view has a focus on what the system needs to operate. Another noteworthy difference that distinguishes the two views is the design stability. The design stability of a process view is a more limited

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    Big Data

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    billion bytes of data in digital form be it on social media‚ blogs‚ purchase transaction record‚ purchasing pattern of middle class families‚ amount of waste generated in a city‚ no. of road accidents on a particular highways‚ data generated by meteorological department etc. This huge size of data generated is known as big data. Generally managers use data to arrive at decision. Marketers use data analytics to determine customer preferences and their purchasing pattern. Big data has tremendous potential

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    Data Measurement

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    _____83000000_____ μm 41‚692 m = ____.000041692______ mm 110 kilometers = _____110000_____ m = ____110000000________ mm 3.7 hectometers =____370_____ m =_____37000_______ cm 451‚000‚000 μm = ____.000451_____ m = ____.0000451_______ dam 2) Imagine a field is about 100 meters long. If you run a 5K race how many meters is it? Approximately how many “fields” does this equate to? 50 football fields 3) Measure the following objects. A) Your computer screen (in meters) Length______________ Width ______________

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    Statistics and Data

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    Simply use statistics as a tool. You will be given a data. (Next year you will not be given data‚ you will gather data yoruself). 1. Data: one of the variables is dependent and other dependent. Can be multiple. Then do regression analysis. ANOVA for overall significance and Regression equation. And write based on ANOVA there is a significance or not. 2. Some comments on correlation: volume vs. horse power etc. 3. Hypothesis test of one population. I assume that the mean is etc etc. Small paragraph

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    Data Warehousing

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    and Kimball’s definition of Data Warehousing. Bill Inmon advocates a top-down development approach that adapts traditional relational database tools to the development needs of an enterprise wide data warehouse. From this enterprise wide data store‚ individual departmental databases are developed to serve most decision support needs. Ralph Kimball‚ on the other hand‚ suggests a bottom-up approach that uses dimensional modeling‚ a data modeling approach unique to data warehousing. Rather than building

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    Wastewater Data

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    Process in Houston CIVT 301 Outline Wastewater collection data in Houston What is sewage treatment? Where does wastewater come from? Factors that affect the flow of pipelines Industrial wastewater? Storm water/ Data The treatment plant operator Sources of wastewater Why treat wastes Waste water treatment facilities Treatment processes Drinking water What can be done to help? Wastewater collection data in Houston 640 square miles area 3 million citizens served 6‚250 miles

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    data gathering

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    Summary of data gathering There are more cars and trucks going through the intersection of Spring Street and Route 27 in the morning than the afternoon. How to determine cars and trucks Cars are usually used for taking passengers to the destination; in general‚ they are smaller than trucks. As for trucks‚ there are larger spaces to carry items‚ for example‚ gasoline‚ foods‚ and other goods. In addition‚ school buses are important transportations on campus‚ but they are counted as neither cars

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