...The challenges for providing effective HIV prevention program to young adult are pretty similar for women and children, which we discussed in the last week. But there are some differences. Young adults always refer to teenagers, such as college students. Teenagers have high-infected rate of HIV, 10 times than others. And one of the biggest challenges face to young adults is that many teenagers are lack of knowledge about HIV/AIDS. Take Chinese students as an example, many students do not have sex education during high school. They only know the term “HIV”, but they do not know how it transmits and the ways to prevent it. In fact, this situation happens in many poor and developed countries due to the insufficient education. The second challenge is that young adults or teenagers are tending to have sex more frequently than elder adults because teenagers are in puberty. Puberty is the thing teenagers cannot avoid, and it is part of the live. According to Avert.org, it mentions, “teenager years are the time of great change, your body develops and changes during puberty as you become an adult, and these changes often go hand in hand with lots of emotions” (“Being Young And Positive”). So teenagers need to learn how to manage themselves during puberty, and the way correctly using condoms. The third challenge is financial constrain. Many countries do not have enough finance support to promote the HIV prevention programs because those programs are very expensive and required many resources...
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...| |Basic Outlining Format Guide for Chapter Outlines | Title of the Chapter I. Topic of First Main Section of the chapter (include definitions, explanations, details and page numbers) A. First Main Point under the First Main Section of the chapter (include definitions, explanations, details and page numbers) 1. Subpoint under the Main point a. Detail and/or definition for the subpoint 2. Subpoint under the Main point a. Detail and/or definition for the subpoint 3. Subpoint under the Main point a. Detail and/or definition for the subpoint B. Second Main Point under the First Main Section of the chapter (include definitions, explanations, details and page numbers) 1. Subpoint under the Main point a. Detail and/or definition for the subpoint 2. Subpoint under the Main point a. Detail and/or definition for the subpoint 3. Subpoint under the Main point a. Detail and/or definition for the subpoint C. Third Main Point under the First Main Section of the chapter (include definitions, explanations, details and page numbers) 1. Subpoint under the Main point a. Detail and/or...
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...STUDY GUIDE EXAM 2 HDFS 210 CHAPTER 6: THEORIES AND METHODS 1. Piaget a. Concrete operations i. What defines this stage? ii. How do children in concrete operations differ from the preoperational stage in terms of conservation tasks and overall thinking? b. Formal operations i. What defines this stage? ii. How do children in this stage differ from concrete operations? 2. Information Processing Theory a. How does this theory view cognitive development? What do these theorists focus on? b. What is metacognition and why is it useful/important? c. How do memory strategies develop with age? What types of strategies do children use? 3. Types of intelligence a. Gardner’s Theory of Multiple Intelligences (9 types) b. Other non-traditional aspects of intelligence (i.e. emotional intelligence) c. IQ—what is it? How is it traditionally measured? Why is it a useful measure? i. How does heredity and environment affect IQ? d. Horizon video on multiple intelligences as examples of the above…. 4. Academic Skills a. What are the components of skilled reading? b. As children develop how do their writing skills improve? Key words: Mental operations Conservation tasks Deductive reasoning Metacognition Organization Elaboration Metamemory Intelligence quotient (IQ) Emotional Intelligence ...
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...REVOLUTION ANALYTICS WHITE PAPER Advanced ‘Big Data’ Analytics with R and Hadoop 'Big Data' Analytics as a Competitive Advantage Big Analytics delivers competitive advantage in two ways compared to the traditional analytical model. First, Big Analytics describes the efficient use of a simple model applied to volumes of data that would be too large for the traditional analytical environment. Research suggests that a simple algorithm with a large volume of data is more accurate than a sophisticated algorithm with little data. The algorithm is not the competitive advantage; the ability to apply it to huge amounts of data—without compromising performance—generates the competitive edge. Second, Big Analytics refers to the sophistication of the model itself. Increasingly, analysis algorithms are provided directly by database management system (DBMS) vendors. To pull away from the pack, companies must go well beyond what is provided and innovate by using newer, more sophisticated statistical analysis. Revolution Analytics addresses both of these opportunities in Big Analytics while supporting the following objectives for working with Big Data Analytics: 1. 2. 3. 4. Avoid sampling / aggregation; Reduce data movement and replication; Bring the analytics as close as possible to the data and; Optimize computation speed. First, Revolution Analytics delivers optimized statistical algorithms for the three primary data management paradigms being employed to address...
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...Big Data Big Data and Business Strategy Businesses have come a long way in the way that information is being given to management, from comparing quarter sales all the way down to view how customers interact with the business. With so many new technology’s and new systems emerging, it has now become faster and easier to get any type of information, instead of using, for example, your sales processing system that might not get all the information that a manger might need. This is where big data comes into place with how it interacts with businesses. We can begin with how to explain what big data is and how it is used. Big data is a term used to describe the exponential growth and availability of data for both unstructured and structured systems. Back in 2001, Doug Laney (Gartner) gave a definition that ties in more closely on how big data is managed with a business strategy, which is given as velocity, volume, and variety. Velocity which is explained as how dig data is constantly and rapidly changing within time and how fast companies are able to keep up with in a real time manner. Which sometimes is a challenge to most companies. Volume is increasing also at a high level, especially with the amount of unstructured data streaming from social media such as Facebook. Also including the amount of data being collected from customer information. The final one is variety, which is what some companies also struggle with in handling many varieties of structured and unstructured data...
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...De-Identified Personal Health Care System Using Hadoop The use of medical Big Data is increasingly popular in health care services and clinical research. The biggest challenges in health care centers are the huge amount of data flows into the systems daily. Crunching this BigData and de-identifying it in a traditional data mining tools had problems. Therefore to provide solution to the de-identifying personal health information, Map Reduce application uses jar files which contain a combination of MR code and PIG queries. This application also uses advanced mechanism of using UDF (User Data File) which is used to protect the health care dataset. Responsibilities: Moved all personal health care data from database to HDFS for further processing. Developed the Sqoop scripts in order to make the interaction between Hive and MySQL Database Wrote MapReduce code for DE-Identifying data. Loaded the processed results into Hive tables. Generated test cases using MRunit. Best-Buy – Rehosting of Web Intelligence project The purpose of the project is to store terabytes of log information generated by the ecommerce website and extract meaning information out of it. The solution is based on the open source Big Data s/w Hadoop .The data will be stored in Hadoop file system and processed using PIG scripts. Which intern includes getting the raw html data from the websites, Process the html to obtain product and pricing information, Extract various reports out of the product pricing...
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...beginning, so the computing does not fail), low cost (Use commodity hardware to store data), and scalability (More nodes, more storage, and little administration.). Apache Hadoop is the standard Hadoop distribution. It is open source project, created and maintained by developers from all around the world. Public access allows many people to test it, and problems can be noticed and fixed quickly, so their quality is reliable and satisfied. (Moccio, Grim, 2012) The core components are Hadoop Distribution File System (HDFS) as storage part and MapReduce as processing part. HDFS is a simple and robust coherency model. It is able to store large amount of information and provides steaming read performance. However, it is not strong enough in the aspect of easy management and seamless integration with existing enterprise infrastructure. And HDFS and Mapreduce are still rough in manner, and it is still under single master which requires care and may limit scaling. More importantly, HDFS, designed to fit high capacity, lacks the ability to efficiently support the random reading of small files. MapR and Cloudera originates from Apache by adding new functionality and/or improving the code...
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...NameNode HDFS stores the metadata on a dedicated server called NameNode. The HDFS namespace is a hierarchy of Files and Directories. Files and Directories are represented on the NameNode as inode. Inode records attributed like permission, modification, namespace and disk space quota. Name stores the Namespace tree and the mapping of the blocks to DataNodes. DataNode The file content is split into large blocks and each block of the file is independently replicated at multiple DataNodes. A cluster can have thousands of DataNodes and ten thousands of HDFS clients per cluster, as each DataNode can handle multiple tasks concurrently. Each block replica on DataNode has two files in the local native file system. One file contains the actual data and the other file contains the blocks metadata, including checksums for data and the generation stamp. The actual size of the data file is the length of the block....
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...Hadoop Cluster Setup Hadoop is a framework written in Java for running applications on large clusters of commodity hardware and incorporates features similar to those of the Google File System (GFS) and of the MapReduce computing paradigm. Hadoop’s HDFS is a highly fault-tolerant distributed file system and, like Hadoop in general, designed to be deployed on low-cost hardware. This document describes how to install, configure and manage non-trivial Hadoop clusters ranging from a few nodes to extremely large clusters with thousands of nodes. Required Software Required software for Linux and Windows include: 1. Java 1.6.x, preferably from Sun, must be installed. 2. ssh must be installed and sshd must be running to use the Hadoop scripts that manage remote Hadoop daemons. Installation Installing a Hadoop cluster typically involves unpacking the software on all the machines in the cluster. Typically one machine in the cluster is designated as the NameNode and another machine the as JobTracker, exclusively. These are the masters. The rest of the machines in the cluster act as both DataNode and TaskTracker. These are the slaves. The root of the distribution is referred to as HADOOP_HOME. All machines in the cluster usually have the same HADOOP_HOME path. Steps for Installation 1. Install java 1.6 Check java version: $ java –version 2. Adding dedicated user group $ sudo addgroup hadoop $ sudo adduser --ingroup hadoop hduser 3. Install ssh $ su - hduser Generate...
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...Abstract— Apache’s Hadoop is an open source implementation of Google Map/Reduce which is used for large data analysis and storage. Hadoop decompose a massive job into number of smaller tasks. Hadoop uses Hadoop Distributed File System to store data. HDFS stores files as number of blocks and replicated for fault tolerance. The block placement strategy does not consider data placement characteristics. It stores file as block randomly. The block size and replication factor are configurable parameters. An application can specify number of replica of file and it can be changed later. HDFS cluster has master/slave architecture with a single Name Node as master server which manages the file system namespace and regulates access to file by clients. The slaves are called to the number of Data Nodes. File is divided into number of one or more blocks and stores as set of blocks in Data Nodes. Opening, renaming and closing file and directory all operations are done by Name Node and Data Node are responsible for read and write request from Name Node. Hadoop uses Hadoop distributed File System (HDFS) which is an open source implementation Google File System for storing data. HDFS is used in Hadoop for storing data. Strategic data partitioning, processing, replication,...
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...Rack is collection of 30-40 nodes. Collection of Rack is Cluster. Hadoop Architecture Two Components * Distributed File System * Map Reduce Engine HDFS Nodes * Name Node * Only one node per Cluster * Manages File system, Name Space and Metadata * Single point of Failure but mitigated by writing to multiple file systems * Data Node * Many per cluster * Manages blocks with data and serves them to Nodes * Periodically reports to Name Node on the list of blocks it stores Map Reduce Nodes * Job Tracker * Task Tracker PIG – A high level Hadoop programing language that provides data flow language and execution framework for parallel computation Created by Yahoo Like a Built in Function for Map Reduce We write queries in PIG – Queries get translated to Map Reduce Program during execution HIVE : Provides adhoc SQL like queries for data aggregation and summarization Written by JEFF from FACEBOOK. Database on top of Hadoop HiveQL is the query language. Runs like SQL with less features of SQL HBASE: Database on top of Hadoop. Real-time distributed database on the top of HDFS It is based on Google’s BIG TABLE – Distributed non-RDBMS which can store billions of rows and columns in single table across multiple servers Handy to write output from MAP REDUCE to HBASE ZOO KEEPER: Maintains the order of all animals in Hadoop.Created by Yahoo. Helps to run distributed application and maintain them...
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... Word Count: 3,477 CONTENTS I- Executive summary 3-4 II- Introduction 4 III- Company Profile 4-5 IV- Problem and Processes Description 5-8 1- Problem definition 5-6 2- Patients flow model at hdf 6-8 V- patient Flow and Capacity Theories 9-12 3- Process mapping 10 4- theory of constraints 10-11 5- lean tools 11-12 VI- HDF processes evaluation from the capacity theories perspective 12-16 VII- Proposed changes and their effects on customer service 16-18 VIII- Final Recommendations and conclusion 19-21 IX- REFERENCE I- Executive Summary: Reducing operational inefficiencies is paramount to healthcare providers across the globes, who are exasperated with their self-defeating...
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