Security Issues Of Hadoop Services

Security Issues Of Hadoop Services
SECURITY ISSUES IN HADOOP SERVICES11
Security Issues in Hadoop Services
Jogendra Chowdari Achanta
Adv Web App Using Web Services– CS 525
Professor Kihyun Kim
04/10/2016
Running head: SECURITY ISSUES IN HADOOP SERVICES1
Abstract
Big Data is creating great opportunities for businesses, companies and many large scale and small scale industries. Hadoop is an open–source cloud
computing and big data framework, is increasingly used in the IT world. The rapid growth of Hadoop and Cloud Computing clearly indicates its
importance as a Big Data enabling technology. Due to the loopholes of security mechanism, the security issues introduced through adaptation of this
technology are also increasing. Hadoop services do not ... Show more content on Helpwriting.net ...
In this current era, many organizations are using the technology to store and analyze petabytes of data that are related to their company, business and
their customers for future requirement. Because of this classification of data becomes even more important. Techniques such as encryption, logging,
and security measures are required for securing this big data. Usage of the Big data for fraud detection looks very interesting and profit making for
many organizations. Big data style analyzing of data can solve the problems like advanced threats, cyber security related issues and even malicious
intruders. With the use of more sophisticated pattern analysis and with the use of multiple data sources it is easy to detect the threats in early stages
of the project itself. Many organizations are fighting with the remaining issues like private issues with the usage of big data. Data privacy is a liability;
thus companies must be on privacy defensive. When compared to security, Privacy should consider as profit making asset because it results in the
selling of unique product to customers which results in making money. We need to maintain balance between data privacy andnational security.
Visualization, controlling and inspection of the network links and ports are required to ensure security. Thus there is a need to put ones in insight the
loop

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NT1210 Unit 1 Assignment 1
3.9Data Processing and analysis.
The unit of analysis was the individual mobile phone service subscriber. Data were analyzed in two major stages, that is, through descriptive statistics
and specific tests of hypothesis. Cross tabulation was used to analyze demographic variability of the respondents. Descriptive statistics including
measures of central tendencies, measures of dispersion, frequencies and percentages were calculated to examine the respondents' characteristics. These
statistics showed the basic characteristics of research variables. The second stage of analysis focused on testing specific hypotheses of this study. To
analyze qualitative data, content analysis was used following the suggestion by SPSS BOSS (2015). As noted by ... Show more content on
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As noted by Zikmund, Babbin, Carr & Griffin (2013), the importance of editing, coding and entering the data is to ensure that errors are checked.
Saunders et al. (2009), on the other hand, note that no matter how carefully data is coded and entered, there will always be some errors which need
to be checked and corrected before the data is analyzed. After this initial processing, the data was analyzed using Statistical Package for Social
Science (SPSS) version 20. In the second step, to get a feel of the data, descriptive statistics was used. As explained by Saunders et al. (2009),
descriptive statistics enable the researcher to numerically describe and compare the variables under study. This includes response rate; measures of
central tendency, notably the mean; measures of dispersion, particularly the standard deviation for Likert scale variables in the questionnaire (Magutu,
2013; Gathenya, 2012). Descriptive statistics covered all response variables and the demographic characteristics of respondents, which were analyzed
using cross tabulation. As explained by Magutu (2013, pp 84 ), 'descriptive statistics provide the basic features of the data collected on the variables
and provide the impetus for conducting further analyses on the

Hadoop Security Issues
SECURITY ISSUES IN HADOOP SERVICES10
Security Issues in Hadoop Services
Jogendra Chowdari Achanta
Adv Web App Using Web Services– CS 525
Professor Kihyun Kim
04/10/2016
Running head: SECURITY ISSUES IN HADOOP SERVICES1
Abstract
Big Data is creating great opportunities for businesses, companies and many large scale and small scale industries. Hadoop is an open–source cloud
technology are also increasing. Hadoop services do not authenticate ... Show more content on Helpwriting.net ...
With the use of more sophisticated pattern analysis and with the use of multiple data sources it is easy to detect the threats in early stages of the
project itself. Many organizations are fighting with the remaining issues like private issues with the usage of big data. Data privacy is a liability; thus
companies must be on privacy defensive. When compared to security, Privacy should consider as a profit making asset because it results in the selling
of unique product to customers which results in making money. We need to maintain balance between data privacy andnational security. Visualization,
controlling and inspection of the network links and ports are required to ensure security. Thus, there is a need to put the ones in insight the loop holes,
conflicts, and the contents ready for attacks with respect to cloud computing, and we need to develop a platform and infrastructure which are less
protected in attacks.
Literature Work
Hadoop is one of the open source frameworks, is used as extension to big data analytics framework which are used by a large group of vendors.
This type of framework makes work easy for the companies how they?re going to store and can use the data within the digital products as well as
physical products (James, M. et al. 2011). We can analyze data using Hadoop, which is emerging as solution to

Data Mining Project : Using Customer Segmentation
3.0Methodology
Easily customisable, CRISP–DM process model was used to implement this data mining project. With respects to this methodology, this study suggests
to identify high value customers and to profile the customers using customer segmentation techniques and by effectively utilising data mining methods.
There are mainly four phases for this analysis. Figure 3.1 illustrates the research methodology of this study.
3.1Business and Data Understanding
Typically, this phase involves gathering business requirements of the retailer and understanding the available data. As a company which is operating in
the retail industry, the retailer for whom the analysis was performed has to face an immense amount of competition. One of the ... Show more content on
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For each time that the customer is completing a shopping at the retailer, one record will be added to the Transaction table. And per each item that the
customer is purchasing, records will be added to the Item table which is linked to the Item Master table that contains the main information about all the
items sold by the retailer. The branch table gives details about the Branches of the retailer.
3.2Data preparation
Data preparation is one of the most significant and time–consuming phases of data mining projects (Steinbach et al., 2005; Han et al., 2006; Yau,
2011). The data needs to be prepared to be in an appropriate state for analysis, maintaining its representativeness of the real world but in a format that
is appropriate for the data analysis tools. Therefore, data pre–processing techniques like data selection, data cleaning, constructing new data, integrating
data and transformation of data were used in this case study.
3.2.1Data Selection
As mentioned before, from the relational database of the company five main tables were used for the analysis. Out of those five tables Customer
table, Transaction table and Item table were used as the main sources of data and Item master table and Branch table were used as reference tables.
When the customer table is considered, there were records for the online customers as well as for the in–store customers. Some of

Fairever Fruit Fairness Cream
Fairever Fruit Fairness Cream
Date: April 18th, 2008 (Submitted in partial fulfillment of the II semester course
Requirements of MBA 2008–2010 batch)
Submitted by Group NO:10 K. ASWINI NAIR (CBBUP2MBA08043) NANDAGOPAL N (CBBUP2MBA08059) SINDHUJA DURGA R
(CBBUP2MBA08099) SOORYA SOMAN (CBBUP2MBA08101) SOUMIA S (CBBUP2MBA08103) SREEDEVI AJITKUMAR
(CBBUP2MBA08105) 1
CERTIFICATE
This is to certify that we, the students of Amrita School of Business, are submitting the following document as part of the partial fulfillment of the
Marketing Management course requirements of second semester MBA for the batch of 2008–10. The report stands genuine to the best of our
knowledge and a tool to be used, only with the objective of academic ... Show more content on Helpwriting.net ...
4
TABLE OF CO TE TS
1.0 I TRODUCTIO TO THE PROJECT ................................................................... 8 1.1 BACKGROU D OTE– Literature
review......................................................... 9 1.2 Limitations of the research................................................................................... 10 1.3 Structure of the
report.......................................................................................... 11 2.0 Research design .........................................................................................................
13 2.1 Management Decision Problem: .......................................................................... 13 2.2 Market Research Problem:
.................................................................................. 13 2.3 APPROACH TO THE PROBLEM..................................................................... 14 2.4
RESEARCH QUESTIO S A D HYPOTHESIS.............................................. 15 2.5 EXPLORATORY RESEARCH
.......................................................................... 17 2.5.1 Secondary data analysis................................................................................. 17 2.5.2
Focus–group .................................................................................................... 17 2.5.3 Depth Interview

Nurses ' Experience Of Power From The Perspectives Of...
Research Topic
Nurses play a very important roles in patient care. They have the power to improve the quality of care, thus improving patient outcomes. According to
Fackler, Chambers, and Bourbonniere (2015), the phenomenon, which is stated in this study, is that the concept of power is usually viewed by nurses '
perception of their work environment; the environment that allows them to take proper care of their patients. Furthermore, based on the social theory
that power is defined as the capacity to achieve goals, the authors believe that if the nurses ' capacity to achieve goals for themselves and their patients
culminates in a positive conclusion, then power becomes an essential phenomenon to explore (Fackler et al., 2015).
Purpose
It was clearly stated that "the purpose of this study was to explore hospital nurses ' lived experience of power" (Fackler et al., 2015). Furthermore,
"this study identifies the meaning and experience of power from the perspectives of hospital clinical nurses" (Fackler et al., 2015).
Research Question "The main questions this study sought to answer included: what is the lived experience of power to hospital clinical nurses; and
what is the meaning of power to hospital clinical nurses in the context of their professional role?" (Fackler et al., 2015). Based on the fact that the goal
of a phenomenological research is to achieve understanding of an experience from the perspective of the participants (Schmidt & Brown, 2015), I
found these

Exploratory Spatial Data Analysis Paper
1.Introduction
The escalating liberalization of international trade that occurred during the decades following World War II under the impulse of various multilateral
agreements and organizations has brought about a dramatic change in the geographic scope of logistics and freight transportation systems. While new
trade ties have emerged with East Asia, long–time trading partners such as the United States and European nations have also intensified their trade
relationships, to the point that the European Union is the largest trading partner of the United States and this trade represents 4% of U.S. gross domestic
product (BEA, 2010).
The intensification of long–haul trade routes has reinforced the critical role of seaports, as gateways to ... Show more content on Helpwriting.net ...
We look at competition and positioning of these ports with respect to shippers and their geographic location on the European continent. We separately
consider their linkages with U.S. ports of entry; finally, we also look at port traffic from the angle of the types of commodity handled. Each analysis
produces a functional space of European forwarding ports from one of the three perspectives. All three perspectives are also brought together in a
synthetic analysis of inter–port competition.
Commodity flow databases constitute a rich and multidimensional data source, but this information will not be useful unless some rules and patterns
are extracted from data in the form of functional space, for instance. Exploratory spatial data analysis methods play an increasing role in analyzing huge
databases. It helps to understand, summarize, and classify the huge unorganized data, and, more importantly, provides new hypotheses about data that
can be subsequently applied in spatial modeling. Multivariate statistical methods such as factor analysis, principle component analysis,
multidimensional scaling, and self–organizing maps deal with data reduction and have been used effectively comprehend functional space, either
implicitly or explicitly. Berry (1966) applied factor analysis approaches to identify India's salient commodity flow patterns. R–mode analysis identifies
clusters of destination locations with similar profiles of incoming flows,

Competitive Environment And Maintaining Customer...
1.0Introduction
In today's competitive environment, maintaining customer relationships is a key to business success because customers are considered as important
strategic resources of a business. However, all business markets contain many subgroups of customers that behave differently, that have different
ambitions, and have different purchasing behaviours. Hence, each subgroup must be treated differently in order to build strong customer relationships.
To compete with rival businesses, it is vital for a business to know enough about their customers and to know the wants and needs of the customers.
Britain 's largest employee–owned retailer, the John Lewis Partnership owns Waitrose, a chain of British supermarkets, which comprise the... Show
more content on Helpwriting.net ...
This is twice as the penetration in the US.
Due to these new challenges, Waitrose is interested in analysing their customer data to gain an overall understanding about their online customers, to
group them into categories and to identify the attributes of the high value online customers for their business. This helps them to be able to better
optimize marketing programs, satisfy customers and to increase profits. The main objective of this analysis is to use the data mining techniques to
analyse the customer purchasing data and to discover patterns, solutions and to find implicit but potentially useful information to answer business
questions of Waitrose. In this analysis, these data mining techniques will be used to define the "High value customer profile" for Waitrose.
1.1About Waitrose
Waitrose is a chain of British supermarkets, which creates the food retail division of the John Lewis Partnership, which is the biggest employee–owned
retailer in Britain. Its head office is situated in Bracknell, Berkshire, England.
Waitrose has established an influential position in the UK's retail food industry and it is extensively acknowledged as the prominent supermarket wine
merchant and as a pioneer in the area of food traceability and organic products. Newspapers as like The Telegraph and The Guardian has been
describing Waitrose, as having an

A Research Study On Data Collection Method
Data Collection Method According to (Swanson, 2005), qualitative research demands a lot from the researcher, it would also be important for the
researcher to acknowledge the words studied in the analysis are influenced by ourselves. This would be the case since the researcher is using surveys
to conduct the research. The independent variable in the case of this research would be the affordable housing programs because the researcher wants to
determine if there is enough housing and are there changes. The dependent variable would be the behavioral changes that would take affect if people
were able to successfully gain housing and be able sustain themselves. The results would be better health, maintaining employment and children doing
better in schools.
The researcher has developed the following procedures and methods that will look to gain a more insightful look at understanding and the importance
of the research topic. The goal of the method is to gather information that will provide more detailed information and the importance in addition to
bringing more awareness of building, rehabilitating and maintaining affordable housing.
Participants
The participants that would be included for the study will be those that live in various communities that have affordable housing whether it is a home or
apartment complex, individuals and families that have previously lived in some type of affordable housing.
Materials
To conduct the research for this topic the researcher will look at

Loyalty and Customer Relationship Management in Tesco Plc.
MBA
RESEARCH METHODS
Dissertation Proposal
Submission Date:12/10/2009
LOYALTY AND CUSTOMER RELATIONSHIP MANAGEMENT IN TESCO PLC.
CONTENT
PROVISIONAL TITLE........................................................................4
1. INTRODUCTION............................................................................4 1.1. The Importance Of Customer Relationship Management.........4 1.2. Retail
Industry.....................................................................5 1.3. Tesco.................................................................................7
2. LITERATURE OF VIEW..................................................................7 2.1. Customer service................................................................8 2.2. Tesco's
Concept Of Relationship Management Begins And Ends With The Customer...................................................................8
2.3. Customer Strategies in TESCO..............................................9
2.4. Rhetoric vs. Reality..............................................................9
3. RESEARCH ... Show more content on Helpwriting.net ...
(Effect of CRM)
Loyal Customer can mean a consistent source of revenue over a period of many years. However, this loyalty cannot be taken for granted. It will
continue only as long as the customer feels they are receiving better value than they would obtain from another supplier. There are many approaches
companies can usefully adopt to measure loyalty and many companies keen to help with various tools and techniques. A large number of retailers
have become so focused on customer basket analysis, profitability and driving down operating costs that they have forgotten to listen to the real voice

of the customer. Indeed in a Bain Company survey in 2005, 80% of companies believed they were delivering a superior customer experience; only 8%
of their customers agreed.
[pic]
Figure 1.1. Effect of CRM
1.2. Retail Industry
Though global retail industry began humbly, it went onto become a business worth billions. Trade was more of a barter system once, where the
surplus produce was swapped for another. Cretans were the earlier traders, who carried on the trade in the Mediterranean region. Then came the
Phoenicians, who not only traded but also distributed the goods across various regions. Romans followed and they developed a sophisticated form of
retailing. Many small shops came up and it is said that the Romans built the first department store. It is these people who developed the retailing
methods we know today. But with the fall of Roman Empire, retailing

The Stata And Hlm Software
The HSLS data was collected through a stratified two stage sampling process. That is, 944 schools (including public, private, and charter schools) were
sampled in the first stage and in the second stage 25, 206 students selected in 50 states in the District of Columbia (Ingels et al., 2011, 2014). There
were approximately 27 students selected per school. To cater for the complex sampling, the data include weights (students weights, parent weights,
teacher weights, counselor weights, and school weights) that should be used to account for the sampling and non–response at the two stages of the
data collecting process. For these reasons STATA and HLM software will be used in the data analysis process because these software can be used with
weights and correction of standard error. The HSLS has missing data that has been cleaned and the analysis process made easier because the data
includes variables in which imputation methods (weighted sequential hot–deck [WSHD] were used to replace the missing data (see Ingels et al., 2011,
2014). To answer the first research question that examines the trends in (i) algebra achievement, (ii) OTL across the three time points analysis using
descriptive statistics to compare differences by race, location, States, SES, and school type will be used and presented using a combination of line
graphs and histograms. The algebra achievement used will be the probability of proficiency scores, which is criterion referenced, to show the gains in

History Of Retro Back 1970 ( Rb70 )
Introduction
Retro Back 1970 (RB70) is the newest hair care product of the Johnson Product line. The Johnson Product hair line of products was well known in the
1970's for having the best products for African–Americans. Johnson Products had two divisions in their hair line. One was Ultra Sheen, the other was
the natural hair line marketed as Afro–Sheen. The slogan was, soul food for the natural.
However, there is not a lot of statistical data to support what is known. The sale of relaxers is on the decline. Since hair maintenance is not optional,
it is safe to assume that if relaxers are on the decline, then natural hair products are on the rise. This is a booming market. The research firm Mintel
estimates the total sale of Black hair products totaled $2.7 billion in 2015. (Mintel, 2016)
RB70, the new perspective, is the organic hair line that has been designed with concerns of hair ingredients and health in mind. The research team is
composed of cosmetic chemists, and highly trained trichologists to ensure the best, and safest products are produced. The marketing department will be
working with Ashley Webelhuth to establish our brand. In addition, business analysts have been interviewed, a team ofbusiness analyst have been
organized to move forward. The analyst will bring their expertise from other companies.
The team of business analysts come from various backgrounds. Business analytics will be used to gather data. This data will include, demographics,

Results and Conclusion of the Research Process
Results and Conclusion of the Research Process HCS/465
Results and Conclusion of the Research Process The title of this research study is Stressful life events and the tripartite model: Relations to anxiety and
depression in adolescent females. The study was investigated by Jeremy Fox, Leslie Halpern, Julie Ryan, and Kelly Lowe (2011). This paper will
expand on previous information reported, which included the background and methodology of the research study. This paper will include the results:
including data collection methods and data analysis procedures, and the conclusion: including the study findings, weaknesses, strengths, limitations,
and whether the findings support the hypotheses.
Results: ... Show more content on Helpwriting.net ...
Data Analysis Procedure: Ways the data analysis procedures are appropriate for testing the hypotheses The researchers proposed three hypotheses: 1)
High stressful life events and high NA should be related to symptoms of both anxiety and depression. Low PA, however, should be uniquely related to
symptoms of depression. 2) NA should moderate the relationship between stressful life events and symptoms of both anxiety and depression. 3) PA
should moderate the relationship between stressful life events and symptoms. (Fox et al., 2010, p. 46) After translating the collected data into facts and
studying the association between the variables, the hypotheses were tested. It was found that the data analysis procedures to be appropriate to help
answer the hypotheses. However, it was found there was mixed evidence for the first hypotheses, which was concerning tripartite models structure. The
data supported the other two hypotheses (Fox et al., 2010).
Data: Key distinctions between qualitative and quantitative data Qualitative data deals with descriptions, which it can be observed not measured.
Qualitative data analysis is more for exploratory purposes and usually is done through interviews (Henninger, 2009). Quantitative data deals with
numbers, data that can be measured. Qualitative data analysis allows researchers to test their hypotheses and is done through surveys (Henninger,
2009). This was a quantitative research study. However,

Questions On Financial Review Boards
Instrumentation Salkind (2010) defined instrumentation as "the tools or means by which investigators attempt to measure variables or items of interest
in the data–collection process" (para. 1). In this study will use an interview questions as guidance through the focus group. The instrumentation will
include open–end questions to give the participants freedom to explain the issue or the reason without leading them to the answer. The interview
questions will seek to answer this study research question.
Confidentiality of Participants and Data (IRB requirements) The participants' personal information will be totally anonyms to protect their identity.
Institutional review boards (IRB) is the board that is responsible to seek permission for the studies that involve human subjects (Creswell, 2013). This
study will seek IRB approval before begins the data collection to make sure there is no harm to the participants.
Data Collection Procedures The planned data collection method is a focus group, which emerged in the 1940s by Merton and Fiske (Cohen &
Crabtree, 2006). This study will use focus group as a primary data collection (Millward, 2012). Cohen and Crabtree (2006) defined focus groups as
"data collection method, [where] data are collected through a semi–structured group interview process." According to Greenbaum (1993), the focus
group has been used since the late1960s. A focus group is "a qualitative research (...) you do not obtain results with percentages, statistical

The Patient Population Of Interest Essay
Data was collected in the health center, after the permission to conduct the study with the patient population of interest was secured from the
administrator of the health center. Data was collected from January 5th, 2017 to March 5th, 2017. Patients in the waiting room were handed flyer
invitation to participate by the researcher, who goes over the contents individually with prospective participants to determine eligibility for the study
based on the criteria. The flyer explains the purpose of the study, the population of interest, including how to participate, and once a patient is eligible
he or she is given a cover letter written in both English and Spanish that explains that participation is voluntary, and that submitting the survey amounts
to giving a consent to participate in the study. The cover letter also explains that there are no benefits to the study, but the results will be posted in the
health center where the patients were recruited. After reading the cover letter and agreeing to participant in the study, the patient is handed paper
versions of both bilingual surveys along with writing instruments. When a participant is done and has submitted their completed surveys, he or she
will be given a copy of correct responses to the DKQ–24 in both English and Spanish.
VariablesвЂЁ
The variables in this study are diabetes education preference, knowledge, and ethnicity. There are two dependent variables and one independent
variable. Education preference is the first

An Analysis on the Effectiveness of Malaysia Current...
1.0 Introduction
1.1 Background
Macmillan English Dictionary defines recycling as "the process of treating waste materials such as newspaper and bottles so that they can be used
again". Beside paper and bottles there have lots of recyclable items such as aluminum cans, scrap metals, plastic and printer cartridges. Recycling saves
energy and reduces pollution and would help slow down global climate change.
The history of recycling is started nearly 4000 years ago, there was a recovery and reuse system of bronze scrap in operation in Europe and there is
evidence that composting was carried out in China. In 1898, The Association of Cleansing Superintendents is established, which today has evolved into
the Institute of Wastes Management. ... Show more content on Helpwriting.net ...
2003, p.306). Solid domestic waste is one of the major environmental problems faced by most municipalities in the country particularly in urban
areas. The generation rate of waste is 1.0kg/day per person and 80 percent of the 230 landfills in the country have only two years of lifespan left.
Despite rigorous campaigns of recycling, the attainment of recycling rate has increased from 1–2 percent in 1997 to merely 5 percent in 2001 (Aini et
al. 2002, cited in Said et al. 2003, p.306).
Studies have shown that the most significant factor affecting nature is not the official government policy but public awareness (Chukwuma 1998,
cited in Said et al. 2003, p.306) or public concern for environment and the readiness to bear the cost of minimizing the adverse impacts of their
activities (Ramsey and Rickson 1976, cited in Said et al. 2003, p.306).
2.2 Malaysia Current Recycling Campaign
Accordingly, various policies and strategies are currently developed and implemented by the government in order to ensure sustainable development of
the nation. The root causes of environmental problems are related to human activities, consumption and production patterns, many of which are
unaffected by environmental policies. Studies by various scholars have demonstrated that the quality of the environment depends

Outline For Qualitative Research Essay
Qualitative Research is a systematic process by which we attempt to answer a question or resolve a problem of a phenomenon. Qualitative research is
non–experimental in its approach to conducting research. Qualitative research stays within the phenomenological paradigm meaning it is subjective in
nature, uses inductive analysis method. Its purpose is to explore, explain, and describe a problem and uses multiple strategies of inquiry to collect data.
Also, Qualitative research is concerned with finding the answers to questions that begin with why? How? In what way? For example; 1) Why people
behave the way they do 2) How opinions and attitudes are formed 3) How people are affected by the events that go on around them.
The three purposes/types ... Show more content on Helpwriting.net ...
Explanatory – to explain the patterns related to the phenomenon in question. Identify any plausible relationships shaping the phenomenon. It explain
the following general research questions: a) What events, briefs, attitudes, or policies shape the phenomenon? b) How do these forces interact to result
in the phenomenon?
3. Descriptive – to document and describe the phenomenon of interest. It describe the following general research questions a) What are the most
important actions, events, beliefs, attitudes, and social structures and processes occurring in this phenomenon?
The three qualitative research design is the blueprint for the collection, measurement, and analysis of data.
1.Qualitative (Phenomenological – is the study of subjective experiences which are personal experiences, interpretation, points of views, emotions and
judgments. Exploring and understanding the meaning of individuals or groups ascribe to as a social or human problem. It involves emerging questions
and procedures, data is collected in the participants setting, data analysis inductively building from particular to general themes, researcher makes
interpretation of the meaning of the data. Qualitative is a non–experimental data collection

The Clustering Is A Data Mining Technique
Abstract: The Clustering is a data mining technique used to place data elements into related groups without advance knowledge of the group
description, which is a division of data into groups of similar objects. The data representing by fewer clusters necessarily loses certain fine details, but
achieves generalization. It models data by its clusters. The data modeling puts clustering in a historical perspective rooted in statistics, numerical
analysis and mathematics. In this paper represents the performance of three clustering algorithms such as EM, DBSCAN and SimpleKMeans are
evaluated. The Diabetes dataset is used for estimating and evaluating the time factor for predicting the performance of the algorithms by using clustering
... Show more content on Helpwriting.net ...
This paper presents comparison is made to find out which analysis option is the best for clustering algorithm called EM, DBSCAN and
SimpleKMeans. The test option there are four kinds of parameter like supplied test set, training set, percentage spilt and class to clusters evaluation.
The training set is used to calculate the data set values. This paper uses the Diabetes dataset for comparison of those algorithms.. The section 2
describes the literature review, Section 3 describes the methodology for the Diabetes dataset and Section 4 describes the experimental result. Finally
Section 5 gives the Conclusion and Future work. 2. Literature Review: J.M. Pena et al., proposed to perform the optimization of the BN parameters
using an alternative approach to the EM technique. We provide experimental results to show that our proposal results in a more effective and efficient
version of the Bayesian Structural EM algorithm for learning BNs for clustering [2]. C. Ambroise et al., choosing a clustering algorithm that is
well–suited for dealing with spatial data. In this algorithm, derivative from the EM algorithm has been designed for penalized likelihood estimation in
situations with unobserved class labels and very satisfactory empirical results lead us to believe that this algorithm converges [3]. Miin–Shen Yang et
al., proposed

Which Areas Of London Are Positive?
Which areas of London are Positive? – New A Spatio–Temporal study of Positive Word use in tweets Vidyaa Shyam, MSc Data Science, City
University, August 2016 Abstract–This study analyses the positive word usage in social media tweets and investigate if the tweets originate from
areas where the people are more happy and satisfied. It aims to find the distribution of these tweets in both space and time in order to uncover
recurrent themes & abnormal behavior. This is achieved by analysing a large spatio–temporal dataset of London tweets using scatter plots, frequency
graphs, linked interactive visualisation and computional methods. It makes use of traditional spatio–temporal analysis methods and clustering to group
data by similarity. It compares the pattern against a survey to find any themes. Index Terms–twitter, tweets, spatio–temporal, visual analytics, Happiness
score, positive words INTRODUCTION Twitter is an online social media platform that allows people to create and share information and messages.
Currently there are 310 million active twitter users with 1 billion unique visitors every month [1]. Each information posted is a tweet which has a
maximum length of 140 characters. According to internet live stats more than 500 million tweets are posted every day worldwide. This vast source of
human generated data is being used for various applications such as Traffic analysis and management, Disaster analysis and recovery, Urban
development, spread of diseases, event

Managing And Analyzing Big Data
Managing and analyzing big data is a huge task for all organizations of all sizes and across all industries. If a business's plan to implement a data
management tools there is a need for a more realistic way of capturing information about their customers, products, and services. Mining data is often in
the terabytes and organizations need to be able to quickly analyze that data and then pull appropriate information needed to make managerial decisions.
Further, with the insurgence of social media, smart devices and click–stream, data is generated daily on global networks through interactions. The use
of data management technologies allow a company to interface unstructured data and structured data to gleam information that is usable for
business managers to make sound business decisions, improve sales and to decrease operating costs. Big data integration and analysis has evolved
for organizations to store, manage, and manipulate vast amounts of data then provide the appropriate information when it's needed to meet business
objectives. Big data is an element that allows companies to leverage high volume data effectively and not in isolation. Big data needs to be quickly
accessible and have the ability to be analyzed. Data stores or warehouses are one way data is managed that is persistent, protected and available as long
as the data is needed. The forefather to data stores is relational data bases, relational data bases put in place decades ago are still in use today

Hadoop Security Issues And Recommendations
HADOOP SECURITY ISSUES AND RECOMMENDATIONS
ABSTRACT
Big Data is creating great opportunities for businesses, companies and many large scale and small scale industries. Hadoop, as an open–source cloud
technology are also increasing. Hadoop services do not authenticate users or other services. As a result, Hadoop is subject to security risks. Big Data
is already a prime target for vulnerable attacks due to the valuable information it holds. In this paper, ... Show more content on Helpwriting.net ...
Because of this classification of data becomes even more important. Techniques such as encryption, logging, and security measures are required for
securing this big data. Usage of the Big data for fraud detection looks very interesting and profit making for many organizations. Big data style
analyzing of data can solve the problems like advanced threats, cyber security related issues and even malicious intruders. With the use of more
sophisticated pattern analysis and with the use of multiple data sources it is easy to detect the threats in early stages of the project itself. Many
organizations are fighting with the remaining issues like private issues with the usage of big data. Data privacy is a liability; thus companies must be
on privacy defensive. When compared to security, Privacy should consider as profit making asset because it results in the selling of unique product to
customers which results in making money. We need to maintain balance between data privacy andnational security. Visualization, controlling and
inspection of the network links and ports are required to ensure security. Thus there is a necessity to invest ones in understanding the loop holes,
challenges, and components prone to attacks with respect to cloud computing, and we need to develop a platform and infrastructure which is less
protected to

Techmetrics Of Dat Project Code And Designs
Exploratory impacts show that the calculation has unbelievable general execution and adaptability, and that it's far a healthy contrasting option to
established connection manage mining calculations while the utilizer need to control the assortment of guidelines engendered [5]. Full complete
flow of this algorithm would be just like explain in figure 5.2 Figure 5.2. Process Flow 6.Project Code & Designs Following are the steps that we
follow in order to generate graphs of data: 6.1.Code Code that we used in R language. Load CSV
tab=read.csv(file.choose(),header=T,sep=",",stringsAsFactors=FALSE) Put CSV File In Data Table tabs <
– as.data.table(tab) Top Calling ggplot(tabs[,.
N, by=calling] [order(–N)] [1:20], aes (reorder... Show more content on Helpwriting.net ...
Graphs Following are the graphs that were generated from R language: Graph 1: Top Amount Graph 2: Top Called Numbers Graph 3: Top Called
Operators Graph 4: Top Calling Numbers Graph 5: Top Group Name 7.Result Analysis As per result analysis of proposed algorithm, input support value
0.1, 0.2, 0.3 and 0.4 with the pattern length 3, 5, 10 provides good set of positive and negative mining rules. In case of fortification value 0.5, we got
result set of negative rules only. As 0.5 thresholds value has been set for class value to dissever positive and negative rules. Results of proposed
algorithm are as: Figure 7.1. Time analysis with support constant 0.1 Time Analysis SupportPattern LengthTime (Without AJAX)Time (With AJAX)
0.1369ms17ms 0.1519ms16ms 0.11023ms16ms Figure 7.2. Time analysis with support constant 0.2 Time Analysis SupportPattern LengthTime
(Without AJAX)Time (With AJAX) 0.2369ms17ms 0.2517ms16ms 0.21023ms16ms Figure 7.3. Time analysis with support constant 0.3 Time
Analysis SupportPattern LengthTime (Without AJAX)Time (With AJAX) 0.3369ms17ms 0.3517ms16ms 0.31023ms16ms Figure

The Concepts Of Big Data Analysis Essay
Abstract–The important goal of this paper is to provide an overview on the concepts of big data analysis. Furthermore the growing emergence and
importance of qualitative data analysis in the field of business intelligence and data science is broadly explained .It also marks out effective tools
and techniques used to obtain prominent qualitative analytic results on the global level. Moreover we have concluded on the basis of comparison
of the tools depending on various factors and parameters by representing it on a tabular manner. I.INTRODUCTION Today, the data consumption
rate is tremendously expanding, the amount of data generated and stored is nearly imperceivable and highly growing. Big data that is nothing but
a large volume of unstructured or structured data that runs in and out in to a business on daily basis. This big data is analyzed in order to achieve
prominent business growth and improved business strategies [1]. Every year there is at least 40% increase in the amount of data growth on global
level, leading to which companies have started adopting new data analytic techniques and tools and also have stepped ahead moving their data
towards the cloud for their big data analytic requirements and for better analysis.[3][2] In big data analysis it is not the amount of data that is
essential but how efficiently we handle, process and analyze it is the key factor. Big data analysis doesn't revolve around how much data we occupy,
it deals with how well you make use

Elmina Corporation Case Study
Introduction Elmina Corporation is a $25 million–dollar data collection and analysis company that has been in business for less than two years. With
expected growth by over 60% in the next eighteen months, the company is looking for ways to leverage the collected data but wants to ensure that the
information technology (IT) infrastructure will support the expected growth that the company is anticipating. The information technology will have to
be redesigned to support the growth. With a limited technology infrastructure to back the expected growth, all its IT must be redesigned for support.
The solution must be implemented in either a hosted solution, on–site solution, or a hybrid model. Various technologies and partners are to be... Show
more content on Helpwriting.net ...
The goal is to encourage specific products to those customers most likely to acquire them, and to determine which products a specific customer is
most likely to buy. This helps to raise the percentage of revenue to marketing costs. A description of current outsourcing and offshoring activities.
Offshoring web analytics is the current trend in outsourcing. Outsourcing web analytics is possible since it's websites, the website's popularity and
business performance are intertwined. Outsourcing web analytics can be done ways, offsite and onsite web analytics. Most American companies are
choosing to offshore to India due to it being an English speaking, as well as it's cheap labor (Singh, 2012). Offsite analytics allows you to analyze
and quantify web activities. Since the data needed for web analytics are created online, thus analysis can be conducted from any location. Hence,
clients that decide to send web analytics jobs pick low cost outsourcing destinations, as the raw data can be transferred swiftly to any location through
email or File Transfer Protocol (FTP). When a client opts to outsource web analytics, various measures that ensure the level of security that a client
needs are adopted. Varied levels of security can be applied at both hardware and software levels, utilizing firewalls, intrusion detection systems (IDS),
intrusion prevention systems, along with many more. The outsourcing team can have a small team working from the client's

Facing The Database Conundrum : Sql, Hbase, Hive, And The...
FACING THE DATABASE CONUNDRUM SQL, HBASE, HIVE, OR SPARK: WITH THE SATURATION OF THE ENVIRONMENT WHICH DO
YOU CHOOSE SAMANTHA MOHR UNIVERSITY OF MARYLAND UNIVERSITY COLLEGE SPRING 2015 ABSTRACT There is currently a
conundrum facing experts in the field of Big Data. The struggle is the ability to perform large–scale data analysis and the impracticality of using
relational database processing languages to handle the information that is collected/processed. Specifically, the growth of data, the sheer volume that
must be stored in databases, processed by cloud analytic and queried by applications has led to a growth in the data capacity the needs to be handled.
Unfortunately, this exponential growth has exceeded the hardware and... Show more content on Helpwriting.net ...
Are dynamic columns something you require support for, then you should choose Cassandra. Do you do batch analytic modeling on your data,
Hadoop may be the choice for you. For live streaming analytic modeling abilities, Apache Spark is a much better choice. So you want to work
with your data as if it were SQL, then you should try Hive. This paper will provide you with a detailed knowledge of how by choosing the correct
database processing and query language you are able to mitigate the processing capacity problems that are involved with the vast growth of data
recently. This will help to show that while there may be no one size fits all answer, there is a fit for the problem at hand based on the storage,
processing, and query needs that are to be met. INTRODUCTION BACKGROUND As a result of the appearance of big data in our world,
conventional data warehousing and data analysis methods no longer have the process power needed. What is Big Data you may ask and why is it
such a big deal. NIST defines big data as anywhere "[...] data volume, acquisition velocity, or data representation limits the ability to perform effective
analysis using traditional relational approaches [...]" (Mell & Cooper, n.d.). 1 (Gong, 2012, p.15) Today's analyst is inundated by an ever growing
number of data being created by social media, mobile phones, climate sensors, digital pictures, etc. The volume being generated is staggering (2.7
Zettabytes of data in the digital universe).While

Essay On Analytics
Organizations are dealing with a tsunami of data from businesses, humans, and now machines through the Internet of Things. Generating timely
insights and predictions has become crucial to capitalize on fast–moving business opportunities. With business analytics now focused on the future,
predictive and prescriptive analytics are taking a front seat. As a Lead Assistant Manager with EXL Services Decision Analytics, I have gained essential
skills in model development and monitoring while enabling the business to predict and mitigate losses. I aspire to extend my knowledge on multiple
technical and business domains through an intense MS program. Information has been my strength throughout my student life. Grasping data from
different... Show more content on Helpwriting.net ...
Eagerness to learn more and the ability to grasp concepts quickly earned me a position in Risk Analytics team with a premier UK– based multinational
bank. With an objective to address Economic Capital Charge as per IAS39 accounting standards, I designed a loss forecasting model based on
historical recovery trend. To incorporate bulk recoveries from debt sale, I evolved an overlay methodology addressing multiple types of debt sale
strategies. Having generated a favourable financial impact , I received Mastermind award as a part of Rewards and Recognitions at EXL. In
addition, I worked on model monitoring projects focused on improving the accuracy and robustness of the risk models. Within one and a half year at
EXL, Management reco gnized me as a star performer with excellent analytical, programming and communication skills and promoted me to the post
of Assistant Manager. Post promotion, I led the rebuild of Probability of Default Impairment Forecasting Model under the IAS39 accounting standards
for one of the high materiality credit card portfolios. Exploratory analysis of portfolio default trends revealed that the existing models were
underpredicting the impairment loss. We revised the model methodology and generated an impact of over 100 million USD on impairment provision
of the banks. Here, I leveraged my leadership skill to train team members, technical skills to design efficient SAS code automating model development
process across products, and

Exploratory Data Analysis Sample
Exploratory data analysis is a process that allows investigation of each variable to be examined for the population or events of interest (Sylvia &
Terhaar, 2014). Descriptive variables are utilized to describe the characteristics of a population or collection of events (Sylvia & Terhaar, 2014). The
descriptive variables utilized in the case study regarding older community–dwelling patients with diabetes are age, gender, race, morbidity level, and
chronic disease conditions. The descriptive data provides a detailed image of the population of participating in the study. The average age of patients
participating in the study was 74.7 years, with the age of 73 being the most frequent. Of the three hundred ninety–two patients participating in the study,
a little over half of the study participates were white men. Ninety–seven percent of the participates reported a "high risk" or "very high risk" morbidity
level. Last, the descriptive variable assessing chronic health conditions reflected that 100% of participates have diabetes. Greater than 20 % of the
patients reported hypertension, lipid disorder, degenerative joint disease, ischemic heart... Show more content on Helpwriting.net ...
An additional method to determine an outlier is to consider the distribution of the outlier to the median as opposed to the mean. Based on this
principle, a value is considered an outlier if "positioned 1.5 times the value of the 75th percentile in a distribution" (Sylvia & Terhaar, 2014). In the
case study, the boxplot figure displays values above and below the 75th percentile or the whiskers (Sylvia & Terhaar, 2014). However, the principal
investigator of the study determined these values should be included as they provide meaning to the final results. In some situations when the outliers
cause a skew in the distribution, other solutions or techniques should be pursued for analysis(Sylvia & Terhaar,

The World Testifies Of Data And Our Understanding Of It Essay
1.1Introduction
(Ian, Frank & Hall 2011) The world testifies to the increasing gap between the development of data and our understanding of it. Data mining is
defined as the analysis of big observational data sets to establish unsuspected relationships that summarize the data in a novel, understandable and
useful way to the data owner (David, Heikki & Padhraic 2001). These relationships and summaries are referred to as models or patterns. Patterns
comprise sets of co–occurring attribute values referred to as item sets or more complex patterns that include sequences, which consider explicit
precedence relationships that are either positional or temporal, and graphs considering arbitrary relationships between points (Mohammed and Wagner
2014). Other types of additional summarised relationships include rules, clusters, tree structures among others.
Lying hidden in all this data is information– potentially useful information–that is rarely made explicit or taken advantage of. Data mining activity for
pattern deployment is achieved automatically or semi automatically (Ian, Frank & Hall 2011). The realised patterns lead to some economic advantage.
Patterns can also be expressed or as a transparent box constructed to reveal the structure of the pattern with the assumption that the realised patterns
make good predictions.
Data mining is used in variety of fields and applications (Galit, Stiumueli, Natin & Peter 2010). This includes the military for purposes of intelligence,

Presentation Chapters 3 5
Nicole Fiamingo's
Presentation
MKT 730 – Marketing Research
February 17,
Dell Printer: "The New Kid On The
Block"
http://www.youtube.com/watch?v=42mNRePWIME
Chapter 3
Research Design
Marketing Research Proposal
1.
2.
3.
4.
Executive Summary
Background
Problem Definition
Approach to the problem
5. Research Design

6. Fieldwork/ Data Collection
7. Data Analysis
8. Reporting
9. Cost and Time
10.Appendices
What the Research Design Includes
1. Define the information needed
2. Design the exploratory, descriptive, and/or causal phases of the research 3. Specify the measurement and scaling procedures
4. Construct and pretest a questionnaire/ data collection
5. Specify the sampling process and sample size
6. Develop a plan of data ... Show more content on Helpwriting.net ...
http://www.businessinsider.com/2009/1/dell–losing–market–share–in–anemic–pc–market–dell
Dell Running Case
1. Search the internet to find information on the latest U.S market share of Dell and other PC marketers.
2. Search the Internet to obtain information on Dell's marketing strategy. Do you agree with Dell's marketing strategy? Why or why not? 3. Visit the U.S
Census Bureau at www.census.gov. As Dell seeks to increase its penetration of U.S households, what information available from the U.S Census
Bureau is helpful?
4. What information available from syndicated firms would be useful to
Dell as it seeks to increase its penetration of U.S households?
Dell's Current Marketing Strategy
"Dell is committed to delivering new dimensions in entertainment, mobility and gaming and is leading the industry in advancing new technologies like
4G solutions and 3D–capable laptops to provide the best entertainment and mobile experiences ever imagined," said Steve
Felice, president of Dell's Consumer, Small and Medium Business unit,
In support of the company's "Power To Do More" brand positioning, Dell recently introduced its first–ever Consumer marketing campaign, "You Can
Tell It's Dell." "The Power To Do More" captures Dell's belief that technology serves an important purpose in helping every one of our customers––
from large enterprises to public institutions, specialized technology services clients, small and medium–sized business and consumers ––

Personal Distress Emergency Room Nurses Experience Based...
Working in a high volume, fast paced and stressful work environment can lead to high stress on the nurses. Directors from the Emergency Nurses
Association, a professor of nursing research, incarnate word, a director and assistant professor of nursing conducted a qualitative study titled ?It?s a
burden you carry?: Describing Moral Distress in Emergency Nursing. The purpose of this study was to survey moral distress emergency room nurses
experience based off of how they describe their response. Themes from data included adaptive/maladaptive coping, being overwhelmed, and
dysfunctional practice arena (Wolf et al., 2016).
Research Design
Qualitative research design is an evolving process that continues to expand as the research continues. Multiple types of study designs can be used when
researching dependent on the information the researchers are trying to obtain. In the article, the researchers described the design as exploratory using
semi–structured focus groups for data collection. Recordings were made of the interviews and then analyzed for similar themes. This type of design is
called descriptive phenomenology which participants describe lived experiences through detailed interview or conversational process and then
researchers bracket the opinions in their study (Polit & Beck, 2014). The method of design was appropriate for the research question and objectives as
it allowed for the understanding of feelings and identity. The themes of questioning allowed for

Research Methodology: Two Types Of Research Methodology
1.4 Research Methodology This section is mainly about the study of the method that will be used to examine the research problem. Method acts as a
tool or instrument for measuring variable stated in the objectives or the way of the study being conducted. Once researcher have chosen the scientific
method as the means of inquiry, researcher must make some decisions about how researcher are going to gather all information (Kenneth & Bruce,
2005). There are two types of research method can be used to collect data either quantitative method or qualitative method. The term of quantitative
research is numerical in nature and can be mathematically computed. Quantitative data measure uses different scales, which can be classified as
nominal scale, ordinal scale, interval scale and ... Show more content on Helpwriting.net ...
The study of social fact influenced by many variables, thus qualitative research method is the best measure to conduct this research. The term refer to
any type of qualitative research study that produces findings not arrived by the way of statistical procedures or other means of quantification (Strauss
& Corbin, 1990). The qualitative method, naturalistic approach is use to observe and interpret reality with the purpose of developing a theory that
will explain what was experienced. The qualitative examination reflect some type of individual phenomenological point of view. Douglas (1976) and
Greertz (1973) believe that numerous substances exist and various elucidation are accessible from distinctive people that are all similarly legitimate.
Reality is a social construct. If one function from this perspective, how one conducts the study and what decision a researcher makes from a study are
extensively not quite the same as those a scientist originating from a quantitative position (Isadore & Carolyn,

Exploratory Data Analysis
1. 1 Displaying Distributions with Graphs 1. In statistics, individuals are the objects described by a set of data, they can be people or animal or
things. 2. In statistics, a variable is any characteristic of an individual. A variable can take different values for different individuals. 3. Exploratory
data analysis is an examination where statistical tools and ideas can help you examine data in order to describe their main features. 4. A categorical
variable places an individual into one of several groups or categories while a quantitative variable takes numerical values for which arithmetic
operations such as adding and averaging make sense. 5. It is useful to use a bar chart when dealing with counts 6. It is useful to use a pie chart when...
Show more content on Helpwriting.net ...
The median is resistant to extreme observations because its based on the middle one or two observations of the ordered list, while the mean is
nonresistant due to the influence of few extreme observations. 5. In stats, spread is a way to measure the variability of the observations around the
center. 6. To calculate Q1 and Q3 , you must first arrange the observations in increasing order to find the median, Q1 is the median of the
observations whose position in the list is to the left of the overall median, Q3 is the median of the observations whose position in the list is to the right
of the overall median. 7. The five–number summary is minimum, Q1, median, Q3, maximum 8. Standard deviation measures spread. 9. The
relationship between variance and standard deviation is that the standard deviation(s) is the square root of the variance s2. 10. Standard deviation
equals zero when there is no spread, that only happens when all observations have the same value. 11. Standard deviation is non resistant to extreme
observations because strong skewness or a few outliers can make the standard deviation very large. 2.1 Density Curves and the Normal Distributions
A density curve is a curve that is always above or on the x–axis and one that has its area exactly one underneath that

Apache Spark Resilient Distributed Datasets
Table of Contents
Abstract1
1Introduction1
2Spark Core2
пЃ¶2.1 Transformations2
пЃ¶2.2 Actions2
3Spark SQL3
4Spark Streaming4
5GraphX4
6MLlib Machine Learning library4
7How to Interact with Spark5
8Shared Variables5
8.1Broadcast Variables:5
8.2Accumulators:5
9Sample Word Count Application6
10Summary8
References8
Abstract
Cluster computing frameworks like MapReduce has been widely successful in solving numerous Big data problems. However, they tend to use one
well none map and reduce pattern to solve these problems. There are many other class of problems that cannot fit into this closed box which may be
handed using other set of programming model. This is where Apache Spark comes in to help solve these ... Show more content on Helpwriting.net ...
Spark 's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory. [3]
The availability of RDDs facilitates the implementation of both iterative algorithms that visit their dataset multiple times in a loop, and exploratory data
analysis, which is the querying of data repeatedly. The latency of applications builds with spark compared to Hadoop, a MapReduce platform may be
reduced by several orders of magnitude. [3]
Another key aspect of apache spark is that it makes writing code easy and quickly. This is as a result of over 80 high level operators included in with

spark library. This is evidence as spark comes with a REPL an interactive shell. The REPL can be used t0 test the outcome of each line of code without
coding the entire job. As a result, ad–hoc data analysis is possible and code is made much shorter.
Apache Spark is well complemented with set of high–level libraries that can be easily used in the same application. These include Spark SQL, Spark
Streaming, MLlib and GraphX which we will explain in details in this paper. 2Spark Core
Spark core is at the base of the apache spark foundation. It provides distributed task dispatching, scheduling, and basic I/O capabilities which is
exposed through a common API (JAVA, Scala, Python and R) centered on the RDD concept. [1] As the core it provides the following:
пЃ¶Memory management and fault recovery.
пЃ¶Scheduling,

Visualization In Data Mining Essay
1 ABSTARCT
Visualization in data mining is a new methodology for exploring and analyzing a huge data sets, data visualization techniques and joining traditional
data mining strategies. It is used for large amounts of data sets and information. Visualization of model–fitting, data and results play a very important
role, but vast data sets are distinctive and new techniques of a data display needed for managing and dealing with large data sets. With the help of this
paper to learn the importance of visualization techniques, approaches and methods used in data mining . The strategy used to achieve this objective is
writing an literature review of many books, journal articles and conference proceeding which are written by experts.
Contents
2. Introduction3
3. Literature Review4
4. Conclusion5
5. Reference5
2 INTRODUCTION
Data mining is the procedure of identifying new hidden, insights and unexpected patterns in large data. Analyzing and exploring the huge volume of
data sets and information becomes increasingly very difficult. Visualization of data mining helps to deal with a flood of data and information. As a
volume of a large set of data stored and collected in databases develops, there is a developing need to provide a data summarization through
visualization. The benefit of visual information investigation is that the client directly involves in the process of

Essay on Math 533
AJ DAVIS is a department store chain, which has many credit customers and wants to find out more information about these customers. A sample of 50
credit customers is selected with data collected on the following five variables:
LOCATION (Rural, Urban, Suburban)
INCOME (in $1,000's – be careful with this)
SIZE (Household Size, meaning number of people living in the household)
YEARS (the number of years that the customer has lived in the current location)
CREDIT BALANCE (the customers current credit card balance on the store's credit card, in $).
The data appears below, and is available in Doc Sharing Course Project Data Set as an EXCEL file:
LOCATION INCOME($1000) SIZE YEARS CREDIT BALANCE($) Urban 54 3... Show more content on Helpwriting.net ...
Rather what I want you do is to highlight what you see for three individual variables (no more than 1 graph for each, one or two measures of central
tendency and variability (as appropriate), and two or three sentences of interpretation). For the 10 pairings, identify and report only on three of the
pairings, again using graphical and numerical summary (as appropriate), with interpretations. Please note that at least one of your pairings must include
Location and at least one of your pairings must not include Location.
All DeVry University policies are in effect, including the plagiarism policy.
Project Part A report is due by the end of Week 2.
Project Part A is worth 100 total points. See grading rubric below.
Submission: The report from part 4 including all relevant graphs and numerical analysis along with interpretations.
Format for report:
Brief Introduction
Discuss your 1st individual variable, using graphical, numerical summary and interpretation
Discuss your 2nd individual variable, using graphical, numerical summary and interpretation
Discuss your 3rd individual variable, using graphical, numerical summary and interpretation
Discuss your 1st pairing of variables, using graphical, numerical summary and interpretation

Discuss your 2nd pairing of variables, using graphical, numerical summary and interpretation
Discuss your 3rd pairing of variables, using graphical,

Exploring The Lean Training Strategies That Healthcare...
Organizational managers are using lean and other process improvement strategies to address inefficiencies or defects through standard work and
employee training (Poksinska, 2010). Employee training is an essential element in the sustainment of process improvements (Dibia et al., 2014).
Bertram et al. (2013) defined training as the understanding, abilities, and mindset to learn and address tasks or job duties within the workplace
environment. Exploring the lean training strategies that healthcare managers may adopt is the focus of this qualitative exploratory case study.
Section 2 encompasses the project plan of the study. The detailed plan includes the purpose statement, role of the researcher, purposive sampling to
identify participants, research method, and research design. Ethically conducting research is an important component of any research project. Section 2
also contains the ethical aspect of identifying the target population, participant sampling requirements, data collection instrument, data collection
techniques, and data analysis. Research should be reliable and valid. Section 2 contains the process to ensure reliability and validity.
Purpose Statement
The purpose of this qualitative case study is to explore how healthcare managers successfully implemented lean training strategies to combat escalating
costs. The target population will consist of healthcare managers in a single rural care hospital located in Tennessee who have implemented lean training
strategies

Marketisation Of Home Care
Methodology For describing the research design of the thesis, this chapter aims to explain the methodology and epistemological approach of my
research, to provide the detailed outline of research design and research process, to address the ethical issues involved in the fieldwork, and to
reflect the limitations. 1. Methodological approach The methodological approach of this study is guided by its aims and research questions. This
section starts from the clarification of research questions to explain why the qualitative methods are employed. This study aims to examine how the
marketisation process is shaping the development of home care policy and practice for older people in urban China. The focus of this thesis is how
the role of the market is developing; how the other sectors respond to these changes; and how the care regime is shaped and, in turn, shaping the
practice in urban China. This leads to the research objectives: to examine the rationale behind the marketisation of care, to understand how home care
has been marketised in urban China since the 1990s, to investigate processes of marketisation of home care, and to explore existing and potential
impacts of marketisation of home care on the care system and participants in the care market in urban China. Among key stakeholders in the care
market, namely service users, providers, purchasers, care workers, and regulators, central to my study is experiences and viewpoints of service
providers and local regulators. The

Research Methodology : Research Methods
3. Research Methodology
3.1 Introduction
This chapter will focus on the research methods application and explain why use these research methods to accomplish this case study. This study was
used the qualitative method to collect various data in order to get more depth and comprehensive information to explore the questions of this study. The
qualitative method mainly used in exploratory research on objectives, it can be used to obtain potential causes, ideas, and motivations. Furthermore, the
qualitative method can also provide more in–depth problem–solving methods and explore new ideas in objectives of the study (Cohen, 2006).
3.2 Primary research: Questionnaire and Interview
Primary research is the method that obtain the first–hand data from experiments and investigation. It contains several different methods such as
questionnaire, interviews and surveys (Blaxter, 2006). Due to changes in China 's luxury consumption is from the beginning of 2014, there are only a
few secondary sources can be found on the Internet. Therefore, this study has mainly focused on gather first–hand information and resources on the
analysis of changes in China 's luxury consumption.
In the questionnaire part, by the time of Easter holidays, I went back to China to do this research. I have created 120 copies of the questionnaire in
order to collect more information from different age, sex, and occupation, region, and education Chinese consumers. The primary research has
conducted in two major

Using Data Analysis For Mixed Methods
There are many general guidelines that can help researchers in mixed methods designs to conduct an effective data analysis. Data analysis strategy
depends on the type of mixed methods design. Creswell (2007) suggested number of essential considerations should be in account when conducting
mixed methods data analysis: 1.The analysis strategy should be used in order to answer the research question(s) being asked. The questions are
different according to the research design as discussed earlier in this paper. 2. Data analysis approach will relate to either concurrent data analysis
(Triangulation and embedded designs) or sequential data analysis (Explanatory, exploratory, and embedded designs). As discussed before, data may be
merged or embedded in concurrent approach and connected or embedded in sequential approach. 3. In the concurrent approach, the analyses of
quantitative and qualitative date are done separately, while in sequential approach the analysis of the first step of the study is used to inform the
second step. For example, in sequential approach, if the first phase is qualitative design, then data analysis and the findings will guide data collection
in the second phase (e.g., quantitative phase). 4. Due to lack of available software for concurrent or sequential approaches, matrix or discussion can be
useful for comparing the results from qualitative and quantitative data in concurrent approach. 5. There is some software available to export qualitative
data into

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