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    big data

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    an era of big data‚ this data-driven world has the potential to improve the efficiencies of enterprises and improve the quality of our lives; however‚ there are a number of challenges that must be addressed to allow us to exploit the full potential of big data. This paper focuses on challenges faced by online retailers when making use of big data. With the provided examples of online retailers Amazon and eBay‚ this paper addressed the key challenges of big data analytics including data capture and

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    Censored data & Truncated data Censoring occurs when an observation or a measurement is outside the range and people don’ t know the certain value. The value is always above or below the range that people set. However‚ truncated data means that because of the limits‚ such as time‚ or space‚ people lose some data. Truncation is to cut off the data. In other words‚ we have collected and use the data‚ but the data is not in the range we have. It is called censored data. We don’t use the data because

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    Thesis Chapter 1-5 Example

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    approximate of 2 to 3 million people in a year. Mostly found on leading stores are the commercialized chemical based mosquito coils. These coils contains high level of toxic chemicals that cause respiratory illness‚ likely occur to children having 5 yrs. old and below of age. Like DEET (N‚ N-Diethyl-meta-toluamide) that are widely used

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    Data Modeling

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    Lecture Notes 1 Data Modeling ADBMS Lecture Notes 1: Prepared by Engr. Cherryl D. Cordova‚ MSIT 1 • Database: A collection of related data. • Data: Known facts that can be recorded and have an implicit meaning. – An integrated collection of more-or-less permanent data. • Mini-world: Some part of the real world about which data is stored in a database. For example‚ student grades and transcripts at a university. • Database Management System (DBMS): A software package/ system to facilitate

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    Team Beta Chapter 1 5

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    Locus of Control as a factor of Tardiness among SJB HS students  Team Beta     Team Members:  Iglesia‚ Hazeleen  Mendoza‚ Clarisse  Luciano‚ Kimberly  Santiago‚ Farou  Santos‚ Millet  Germanes‚ Aldrin     Subject Thesis                1  Locus of Control as a factor of Tardiness among SJB HS students  CHAPTER I  The Problem and Its Background  This chapter discusses the rationale for this study along with a discussion of the specific  questions‚ hypotheses‚ scope and limitations‚ and ends with a listing of defined terms

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    Data Mining

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    Nagham Hamid‚ Abid Yahya‚ R. Badlishah Ahmad & Osamah M. Al-Qershi Image Steganography Techniques: An Overview Nagham Hamid University Malaysia Perils (UniMAP) School of Communication and Computer Engineering Penang‚ Malaysia nagham_fawa@yahoo.com Abid Yahya University Malaysia Perlis (UniMAP) School of Communication and Computer Engineering Perlis‚ Malaysia R. Badlishah Ahmad University Malaysia Perlis (UniMAP) School of Communication and Computer Engineering Perlis‚ Malaysia

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    The Apple computer company describes the results of the company like that: Apple computer company with the Apple II in 1970s ignited the personal computer revolution‚ it was the introduction of and reinvented the personal computer. Through its creative hardware‚ software‚ and Internet technology and equipment‚ Apple company is committed to create the best computer use its network bring to students‚ educators‚ creative experts and ordinary consumers around the world. 1. the external environment

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    Collecting Data

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    Collecting Data Shauntia Dismukes BSHS/405 June 1‚ 2015 Tim Duncan Collecting Data Data collection is the process of gathering and measuring information on variables of interest‚ in an established systematic fashion that enables one to answer stated research questions‚ test hypotheses‚ and evaluate outcomes. In this paper I will define the importance of data collecting in the helping field. While working in the helping field‚ there are many important things that must happen

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    "Data Compression and Data Processing” Please respond to the following: * Explain whether or not you believe there is a discernible difference in efficiency between compressing and decompressing audio data and compressing and decompressing image data. Provide at least three reasons for your argument. There are efficiency differences between a given compressions for audio or image‚ this is due to: 1. The difference in data‚ example bmp‚ jpg‚ gif of the same image‚ mp3‚ wav‚ ogg for

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    Secondary Data

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    Collecting‚ Reviewing‚ and Analyzing Secondary Data WHAT IS SECONDARY DATA REVIEW AND ANALYSIS? Secondary data analysis can be literally defined as second-hand analysis. It is the analysis of data or information that was either gathered by someone else (e.g.‚ researchers‚ institutions‚ other NGOs‚ etc.) or for some other purpose than the one currently being considered‚ or often a combination of the two (Cnossen 1997). If secondary research and data analysis is undertaken with care and diligence

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