Information On Line Transaction Processing

Information On Line Transaction Processing
ETL Overview
Within an enterprise there are various different applications and data sources which have to be integrated together to enable Data Warehouse to provide
strategic information to support decision–making. On–line transaction processing (OLTP) and data warehouses cannot coexist efficiently in the same
database environment since the OLTP databases maintain current data in great detail whereas data warehouses deal with lightly aggregated and
historical data. Extraction, Transformation, and Loading (ETL) processes are responsible for data integration from heterogeneous sources into
multidimensional schemata which are optimized for data access that comes natural to human analyst. In an ETL process, first, the data are extracted
from ... Show more content on Helpwriting.net ...
2. Incremental extraction: In this type of extraction only the changes made to the source systems will be extracted with respect to the previous
extraction. Change data capture (CDC) is mechanism that uses incremental extraction.
There are two physical methods of extraction: Online extraction and Offline extraction. Online extraction process of ETL connects to source system to
extract the source tables or store them in a preconfigured format in intermediary systems e.g., log tables. In Offline extraction the data extracted is
staged outside the source systems.
Transformation
The transform stage applies a series of rules or filters to the extracted data from to derive the data for loading into the end target. An important function
of transformation is the cleaning of data, which process aims to pass only "proper" data to the target. one or more of the following transformation types:
1. Selecting only certain columns to load.
2. Translating coded values and encoding free–form values.
4. Deriving a new calculated value.
5. Sorting.
6. Joining data from multiple sources and duplicating the data.
7. Aggregation and disaggregation.
9.Turning multiple columns into multiple rows or vice versa.
10. Splitting a column into multiple columns.
12. Lookup and validate the relevant data from tables or referential files for slowly
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Mobile Phones And Its Impact On Our Day Lives
Motivation
Mobile phones becomes an essential companion in our day to day lives. They helps us to keep in touch with friends, family, colleagues, access email,
browse internet etc. Mobile phones were brought to life with the help of an operating system. In the present world, Android and IOS are having the
major mobile operating systems market share in the world.
Android holds a market share of 61.9% in the current US market while Apple`s IOS holds only 32.5% of share. Internationally android`s market share
is even lot better when compared to Apple`s IOS. So keeping these facts in mind we are inspired to perform big data analytics on tweets related to
android operating system.
Introduction
Big data is the buzzing word in the present software industry. Huge amounts of data is being generated daily from various sources. Companies are
trying to perform analytics on big data and get some valuable output which gives an edge over there competitors. In order to achieve this we need to
program map reduce jobs in Hadoop ecosystem. It is very difficult to develop the code and reuse it for different business cases. On the other hand,
People are very much comfortable to query data using SQL like queries.
A team of developers at Facebook developed a dataware house tool namely called as HIVE. Hive supports the queries like SQL type which is called as
HiveQL. These queries are compiled as map reduce jobs and are executed using Hadoop. Through HiveQL we can plugin custom map reduce

Data Warehouse Components And Architecture
DATA WAREHOUSE COMPONENTS & ARCHITECTURE
Lecture Note # 02
The data in a data warehouse comes from operational systems of the organization as well as from other external sources. These are collectively referred
to as source systems. The data extracted from source systems is stored in a area called data staging area, where the data is cleaned, transformed,
combined, deduplicated to prepare the data for us in the data warehouse. The data staging area is generally a collection of machines where simple
activities like sorting and sequential processing takes place. The data staging area does not provide any query or presentation services. As soon as a
system provides query or presentation services, it is categorized as a presentation server. A presentation server is the target machine on which the data is
loaded from the data staging area organized and stored for direct querying by end users, report writers and other applications. The three different kinds
of systems that are required for a data warehouse are:
1. Source Systems
2. Data Staging Area
3. Presentation servers
The data travels from source systems to presentation servers via the data staging area. The entire process is popularly known as ETL (extract, transform,
and load) or ETT (extract, transform, and transfer). Oracle's ETL tool is called Oracle Warehouse Builder (OWB) and MS SQL Server's ETL tool is
called Data Transformation Services (DTS).
A typical architecture of a data warehouse is shown below:

Fraud Detection in Banking Transactions
Preface Purpose The purpose of this document is to detail the description of the Real Time (Active) Fraud Detection in Banking Transactions (FDBT)
Project. This document is a key project artifact and is used during the design, construction, and rollout phases. Scope The objective of this project
report is to capture the functional and non–functional requirements for the Real Time FDBT project. This report lists out the complete system
requirements and design architecture of the project. The requirements contained herein will include, but not be limited to: Capabilities or system
functionality – What a system does, to include, but not be limited to: Interfaces (internal and external hardware) Business Rules Data ... Show more
content on Helpwriting.net ...
If any transaction is more than 80% credit limit in 48 hours (one transaction or sum or transactions in the 48 hour period). Deposit activity out of the
normal range for any account Invalid Routing Transit numbers Excessive numbers of deposited items Total deposit amounts greater than average Large
deposited items masked by smaller deposit transactions The amount exceeds the historical average deposit amount by more than a specified percentage
A duplicate deposit is detected Deposited checks contain invalid routing or transit numbers The level of risk can be managed based on the age of the
account (closed account getting lot of transactions suddenly). The number of deposits exceed the normal activity by the customer Consider the
proximity of the customer's residence or place of business Wire transfers, letters of credit, and non–customer transactions, such as funds transfers,
should be compared with the OFAC lists before being conducted. A customer's home/business telephone is disconnected. A customer makes frequent or
large transactions and has no record of past or present employment experience. A customer uses the automated teller machine to make several bank
deposits below a specified threshold. Wire transfer activity to/from a financial secrecy haven, or high–risk geographic location without an apparent
business reason, or when it is inconsistent with the customer's business or history.

Questions On Using Data Analytics
How–To: Data Analytics By Zach Hazen | Submitted On November 15, 2015 Recommend Article Article Comments Print Article Share this article on
Facebook Share this article on Twitter Share this article on Google+ Share this article on Linkedin Share this article on StumbleUpon Share this article
on Delicious Share this article on Digg Share this article on Reddit Share this article on Pinterest This is a very simple post aimed at sparking interest in
Data Analysis. It is by no means a complete guide, nor should it be used as complete facts or truths. I 'm going to start today by explaining the concept
of ETL, why it 's important, and how we 're going to use it. ETL stands for Extract, Transform, and Load. While it sounds like a very ... Show more
What programs am I going to use to transform the data? What am I going to do once I have all the numbers? What kind of visualizations will
emphasize the results? All questions you should have answers to. Step 2: Get Your Data (EXTRACT) This sounds a lot easier than it actually is. If you
're more of a beginner, it 's going to be the hardest obstacle in your way. Depending on your use there are typically more than 1 way to extract data. My
personal preference is to use Python, which is a scripting programming language. It is very strong, and it is used heavily in the analytic world. There is
a Python distribution called Anaconda that already has a lot of tools and packages included that you will want for Data Analytics. Once you 've installed
Anaconda, you 'll need to download an IDE (integrated developer environment), which is separate from Anaconda itself, but is what interfaces with the
programs itself and allows you to code. I recommend PyCharm. Once you 've downloaded all of the things necessary to extract data, you 're going to
have to actually extract it. Ultimately, you have to know what you 're looking for in order to be able to search it and figure it out. There are a number of
guides out there that will walk you more through the technicalities of this process. That is not my goal, my goal is to outline the steps necessary to
analyze data. Step 3: Play With Your Data (TRANSFORM) There are a number of programs and ways to accomplish this. Most aren 't free,

Number Plate Extraction
III. NUMBER PLATE EXTRACTION
The captured input image has number plate covered by vehicle body, so by this step only number plate area is detected and extracted from whole body
of vehicle. The number plate extraction phase influence the accuracy of ANPR system because all further step depend on the accurate extraction of
number plate area. The input to this stage is vehicle image and output is a portion of image containing the exact number plate. Number plate can be
distinguished by its features. Instead of processing every pixel, the system processes only the pixels that have these features. The features are derived
from number plate format and the characters constituting the plate. Number plate color, rectangular shape of number plate boundary, the color change
between the plate background and characters on it etc. can be used for detection and extraction of number plate area. The extraction of Indian number
plate is difficult as compared to the foreign number plate because in India there is no standard followed for the aspect ratio of plate. This factor makes
the detection and extraction of number plate very difficult. The various methods used for number plate extraction are as follows:–
A. Number Plate Extraction using Boundary/Edge information
Normally the number plate has a rectangular shape with aspect ratio, so number plate can be extracted by finding all possible rectangles from the input
vehicle image. Various edge detection methods commonly used to find these

Describe The Advantages And Disadvantages Of The...
The administration process in any organization depends mainly on effective decision making. The Management Information Systems (MIS) appeared
with the aim to facilitate the planning and decision making process and therefore the administration process by incorporating the processes of
collecting, processing, storing, retrieving and communicating information towards a more efficient management and planning in businesses. Therefore;
MIS can be defined as a system that transforms data to information that is communicated with management in a proper form (Al–Mamary, Shamsuddin
et al. 2013). It is designed for a better communication among management and employees and for serving as an information recording system that
support the organization's strategic ... Show more content on Helpwriting.net ...
 Application Virtualization: means to abstract the application layer from the operating system, and run it in a compressed form without depending on
the operating system.
2.1.1 Virtualization characteristics
In reference to an article presented at ("5 Characteristics Of Virtualization", 2010), describes five characteristics of virtualization as follows:
 Flexible: business can configure and reconfigure to meet the changing environment and the company and customer needs.
 Scalable: business can scale up or down to facilitate its growth and expansion depending on its needs.
 Efficient: virtualization saves money on the business.
 Secure: virtualization provide high security on company's data.
 Accessible: it is accessible to anyone needs it at any time.
2.1.2 Advantages and Disadvantages of Virtualization
Virtualization has recently spread widely among businesses as an IT solution; and became one of the most used products regardless of the business size
due to its benefits (Occupytheory 2015). However; before adopting virtualization companies should know in addition to the advantages of virtualization
the disadvantages associated with its adoption and then to decide whether to adopt it or not. Main advantages and disadvantages of virtualization of
virtualization are illustrated in table

Using Extract Transform Load ( Etl )
In today 's organizations, basic descision making procedures and day by day operations frequently rely upon information that is put away in an
assortment of information stockpiling frameworks, arrangements, and areas. To transform this information into helpful business data, the information
commonly should be consolidated, purified, institutionalized, and compressed. For example, data may be changed over to an alternate information sort
or different database servers may store the vital information utilizing diverse patterns. Dissimilarities like these must be settled before the information
can be effectively stacked to an objective target. After the plan and improvement of data warehouse as per the business prerequisites, the way toward
combining the information into the information stockroom from different sources is to be thought of. Extract Transform Load (ETL) procedures are
basic in the achievement of the Data Warehousing ventures. The way toward extricating information from one source (extract), changing it as per the
outline of the data warehouse(transform) and stacking it into data warehouse (load) constitute ETL. As it were, ETL is the way toward extracting
information from different information sources, changes it according to the prerequisites of the target data warehouse and effectively stacking it into the
data warehouse (database). In the transformation procedure data is institutionalized to make it perfect with the target database along with data purifying

Data Warehouse Case Study
Case Study: A Data Warehouse for an Academic Medical Center
Jonathan S. Einbinder, MD, MPH; Kenneth W. Scully, MS; Robert D. Pates, PhD; Jane R. Schubart, MBA, MS; Robert E. Reynolds, MD, DrPH
ABSTRACT The clinical data repository (CDR) is a frequently updated relational data warehouse that provides users with direct access to detailed,
flexible, and rapid retrospective views of clinical, administrative, and financial patient data for the University of Virginia Health System. This article
presents a case study of the CDR, detailing its five–year history and focusing on the unique role of data warehousing in an academic medical center.
Specifically, the CDR must support multiple missions, including research and education, in addition to ... Show more content on Helpwriting.net ...
There has also been increasing interest in using the CDR to serve a broader audience than researchers and to support management and administrative
functions–"to meet the challenge of providing a way for anyone with a need to know–at every level of the organization–access to accurate and timely
data necessary to support effective decision making, clinical research, and process improvement."4 In the area of education, the CDR has become a
core teaching resource for the Department of Health Evaluation Science's master's program and for the School of Nursing. Students use the CDR to
understand and master informatics issues such as data capture, vocabularies, and coding, as well as to perform
Case Study: A Data Warehouse for an Academic Medical Center
167
exploratory analyses of healthcare questions. Starting in Spring 2001, the CDR will also be introduced into the university's undergraduate medical
curriculum.
System Description
Following is a brief overview of the CDR application as it exists at the University of Virginia. System Architecture. The CDR is a relational data
warehouse that resides on a Dell PowerEdge 1300 (Dual Intel 400MHz processors, 512MB RAM) running the Linux operating system and Sybase
11.9.1 relational database management system. For storage, the system uses a Dell Powervault 201S 236GB RAID Disk Array. As of

Assessment Applied : Data Science
Name: Mohd Taufiq Asraf Bin Kamarulzaman
Position Applied: Data Scientist
Please describe:
1. The most important challenges you would anticipate in this task
Data Issues
Data issues are the most common problem in analytics. In this case, there are many sources of data, which inconsistency data is one of the possible
issues. There are also other problem might occur for this situation, such as duplication records, missing data, unstandardized data, misspelled or
multiple spelling in the data.
Furthermore, there are several variables or student information that will take some time to analyse which are, Information on why they want to study
overseas and Other data points related to their attitude to international education. It is because; the response of the student for this information can be
very subjective and quite troublesome to quantify or to group it.
Segmentation or Model Issues
The student segmentation and the classification model can be outdated over time. The behaviour of the students or prospective students change or
maybe the job markets have shifted will make the models outdated. To avoid segmentation or model become obsolete, the data and the algorithm must
be updated over time.
We might have created segments that are not meaningfully different from each other. The segmentation should produce segments that are similar
characteristics within the group and different characteristics across groups.
2. At a simplified level, the process and technologies

Data Mining Case Study
Indiana University Health – A Cerner data warehouse in 90 days – Case Study
http://www.healthcatalyst.com/success_stories/how–to–deliver–healthcare–EDW–in–90–days/?
utm_medium=cpc&utm_campaign=Data+Warehouse&utm_source=bing&utm_term=+data%20+warehousing%20+case%20+study&utm_content=35427
Name: Goutham Para
Provide brief but complete answers. One page maximum (print preview to make sure it does not exceed one–two pages).
Q1: Describe the original data warehouse designed for Indiana University Health and its limitations. Please describe the new data warehouse and the
differences between each?
The original data warehouse structured and designed for Indiana University Health is traditional enterprise data warehouse. They ... Show more content
on Helpwriting.net ...
Data warehouse has different concepts of data. Each concept is divided into a specific data mart. Data mart deals with specific concept of data, data
mart is considered as a subset of data warehouse. In Indiana University traditional data warehouse is unable to create large data storage. Further it
shows any errors and imposed rules on data. The early binding method is disadvantage. It process longer time to get enterprise data warehouse (EDW)
to initiate and running. We need to design our total EDW, from every business rule through outset. The late binding architecture is most flexible to bind
data to business rules in data modeling through processing. Health catalyst late binding is flexible and raw data is available in data warehouse. It
process result by 90 days and stores IU data without any errors.
Q3: While this case study supports a specific data warehouse product, please locate another case study from another data warehousing Software
Company and explain the data warehouse that was designed in that case study?
TCS Company provided a solution to one of its client for changing hardware and software to existing database presented in client's data warehouse for
reutilization. Client is leading global provider for offering communication services, it delivers solutions to multinational Companies and Indian
consumers (Tata consultancy). Company implemented a solution by replacing the existing hardware and software with TATA Company data warehouse

Male Sexual Enhancement Research Paper
Male sexual enhancement products are oftentimes confused with products of penis enlargement. Due to this mistake, loads of skepticism exists
regarding whether the male enhancement products practically work.
Supplements for sexual improvement have a greater effect compared to penis enlargement since they can refine everything regarding male sexual
performance –not penis size. Anyways, throughout erections, it is the penis's corpora cavernosa that gets filled up with blood. Blood amount that this
membrane accepts and the amount of time it accepts it will determine how tough an erection will be and how long it will last. Now pay attention please.
Men get left with weak erections that lack in performance, for loads of age.
The corpora cavernosa could virtually lose its overall force to absorb this blood influx. Unusual supplements for male however, enhancement as well as
come with ingredients such as leaf extract of Epimedium that can refine any erectile function. The corpora cavernosa can then enhance its capacity and
over and over again get the grip to deal with improved blood flow for harder erections that usually teenagers tend to get ... Show more content on
Helpwriting.net ...
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Are Data Algorithms And Algorithms Can Be Been Solving...
Traditional statisticians have been solving data problems for years. Example, when faced with the large data problem statisticians had a simple and
proven technique to solve the problem by sampling the records. This is a sound methodology as the statisticians could prove statistically that a
representative or random sample could speak for the whole population. The only problem was that when stakeholders wanted to drill down into the
details they inevitably had to query the whole file. The reality is that data scientists run various data mining techniques and various algorithms that can
accommodate both large and small files. However, companies may want to consider examining and cleansing the data they collect before running their
analyses. This brings up the question of whether or not big data methods and algorithms can be considered reliable. Data quality is still a challenge even
in the world of big data.
The reality is that he world of big data is somewhat messy. Given data is collected so fast and is usually unprocessed (raw) it may be incomplete versus
most traditional transactional data. Data may be missing many values or may contain incomplete records. Customer profiles may even be incomplete.
Many companies embark upon a formal data cleansing strategy before or after placing data into their big data environment.
Cleansing data initiatives vary among companies because it costs time and money. Spending time up front to cleanse data can pay off, in the end,
resulting

What Is A Four-Distributed Static Compensator
This paper deals with a four–leg distributed static compensator (DSTATCOM) which is used to solve the current related power quality. An amplitude
adaptive notch filter (AANF) is employed for the reference current extraction of DSTATCOM control scheme because of its simplicity, capability of
frequency and amplitude measuring, appropriate extraction of the fundamental signal, and insensitivity to the amplitude variation of the input signal. To
generate the gate pulses of the switches, an adaptive hysteresis band current controller is used. In addition, fuzzy logic controllers are used for a better
performance of DSTATCOM under dynamic conditions. The proposed control algorithm is robust to power disturbances, especially when the main
voltage ... Show more content on Helpwriting.net ...
However, these algorithms generally demonstrate a slow time response. Time–domain algorithms are based on the instantaneous extraction of harmonic
currents/voltages. The common time–domain control strategies are instantaneous reactive power theory (IRPT) [6] and synchronous reference frame
theory (SRF) [7]. Calculation of active and reactive powers by transforming three–phase voltages and currents into two phases is the principle of PQ
theory, which does not work properly under non–sinusoidal supply conditions [8]. SRF theory is based on conversion of three–phase quantities into
their corresponding dc components, and low–pass filters (LPFs) are employed for harmonic filtering which contain a time delay and deteriorate the
performance of the controller [9].
Most of the advanced control and signal processing methods are accurate and show a better dynamic response than the FFT, but a large amount of
calculations is required, which does not demonstrate an excellent performance in frequency–varying conditions [10–13]. Adaptive notch filter (ANF) is
another advanced algorithm which has been introduced as an effective control technique for extracting reference sinusoidal components from distorted
input signal. ANF is capable of changing the notch frequency suitably by tracking the frequency variations of the input signal [14–16].
To the best knowledge of the authors, the modified notch filter in a

Business Rules and Business Intelligence
Business Intelligence projects start out as a simple report or request for an extract of data. Once the base data is aggregated then the next request usually
is about summing data or creating more reports that have different views to the data sets. Before long complex logic comes into play and the metrics
coming out of the system are very important to many corporate wide citizens. "Centrally managed business rules enable BI projects to draw from the
business know–how of a company and to work with consistent sets of business logic – they are what add the intelligence to business intelligence."
(pg14)
Once reports are no longer a straightforward representation of base data they begin to depend more and more on business rules. The term ... Show more
In order to achieve a consistent business definition across an organization the individual departments need to share and become aware of how this data
can heavily influence what they know to be true and how other interpret what is true. It helps to drive consistency by encoding business logic and can
be defined as a specialized version of business rules. According to the article "only 20% of organizations practice MDM as a separate solution."(pg15)
An independent business logic component should look like a set of rules that are shared among departments that directly interface with the business
users and IT systems, eliminating the need for developers to write programming code. Experts call such a thing a "business rules engine" but there
different schools of thought on the term. Some people think that it is a software application that can be used to capture to business know–how and can
interpret operational data to gain insight. Others think that it resembles something akin to AI (artificial intelligence) or expert systems that interpret the
implications of business rules on a set of data. Whatever method is chosen as path forward it is important that businesspeople can review the existing
rules and make modifications to a rules engine.
The rules should be documented and formulated in a "natural language" (language that a plain speaking business person can understand) so that a
business

A Data Warehouse And Business Intelligence Application
Abstract
A data warehouse and business intelligence application was created as part of the Orion Sword Group project providing business intelligence to order
and supply chain management to users. I worked as part of a group of four students to implement a solution. This report reflects on the process
undertaken to design and implement the solution as well as my experience and positive learning outcome.
Table of Contents
Abstract 1
1. Introduction 3
2. Process and Methodology 3
2.1 Team Member Selection and Organisation 3
2.2 Requirement Analysis 4
2.3 Top Down Vs Bottom Up Data Warehouse Design 4
2.4 Team Dynamics and Conflict Resolution 5
2.5 Final System Architecture, Design and Implementation 5
3. Proposals for Future Implementation 6
4. Self–Reflection 7
4.1 Self Discovery and Technical Development 7
4.2 Reinforced Understanding of the Subject 7
5. Conclusion 7
6. References 8
7. Peer Review Assessment 8
1. Introduction
Working as part of a group of four students, an end–to–end data warehouse application was designed and built as part of the Orion Sword Group
Consultancy project for the Data warehouse course module.
The implementation of the data warehouse was based on Kimball's (Kimball and Ross, 2013) dimensional modelling techniques which involved
business requirements analysis & and determination of data realities and the four step dimensional modelling design process. These was followed by
the design and

The Hunger For Analyzing Data
The hunger for analyzing data to improve delivered needs and to better meet quality measures is spurring a revolution in all the industries like
Healthcare, Manufacturing Industry, Insurance Domain, etc. Considering any Industry, the providers are demanding better respective IT systems that
allow Information Management and data analytics professionals to filter through large amounts of data and turn it into "information" that can change
the business and function of the industry.
Data warehouse Analysts are bridging the present business aspect with the future. Their role is critical to a company's ability to make sound business
decisions. A Data Warehouse Analyst is responsible for data design, database architecture, and metadata and ... Show more content on Helpwriting.net
...
The roles and duties of the BI Analyst vary from the company's aspect and requirements. The Analyst needs to understand the business implications of
technical solutions, and assist in defining the technology solutions to support any future business requirements.
The roles and requirements of a Data warehouse Analyst are as follows:
Solicits, develops, documents and manages requirements throughout the project and/or product life cycle up to and through change control
Development of complex extraction, transformation and load processes (ETL) Performance and tuning of ETL processes and preparation of business
and technical documents
Develops custom solutions to business problems or customer engagements through in–depth analysis, coordination and negotiation with key decision
makers
Conduct walk–throughs with stakeholders and obtain sign–offs
Solve problems considering business process and environmental changes
Review Technical Design documents and Test Plans
Work with technical resources and share business knowledge to assist in solution generation
Design of specialized data structures for the purpose of business intelligence and data visualization
Maintaining a set of development standards in conjunction with team architect
Working with the project team to plan, design, develop, and implement application systems and/or

A Holistic View Of Data Migration
This is one of the very crucial iterations of my internship which is aimed to give a holistic view of data migration process. This phase is allocated
maximum possible time with the goal of understanding the basic principles and guidelines that are part of typical data migration process. In this
iteration the agenda is to know and understand the concepts of data migration and thingsthat necessitatedata migration, how does a data migration
strategy look like, what are the pre requisites for a good migration strategy, roles involved and their responsibilities in the entire work flow, technology
required, objectives at each phase, end to end data flow, adherence of a strategy to the business rules and requirements that govern the process and
outcomes of migration work process. I am expected to be knowledgeable of applications business domains that are part of the migration.
There might be multiple source systems or legacy systems that need to be considered as part of the data conversion and migration and it is expected to
understand a reasonable portion of the intended use of each of these underlying source systems. One of the other activities in the agenda is to give a
deep dive in to the different stages of migration and the connectivity between the stages of Extract, Transform and Load. Introduction to various
environments that host or stage the data is also part of the plan.It is also anticipated to have introduction to the data flow and conversion from source to

Data And Data Of Data
Data: Data is studied as the lowest part of abstraction level from which knowledge and information can be derived. Data is always a raw form of
information. It can be a collection of images, numbers, inputs, characters or any other outputs that can be converted into symbolic representation.
Information: Information refers to data that provides a meaningful connection between them. Here, data refers to the collection that can be processed to
provide useful answers which leads to an increase in knowledge.
Knowledge: Knowledge terms to the collection of a set of information, so that it provide something useful. We express our knowledge by the way we
act on information provided to us.
2– DATA STREAM
In recent years, the processing and ... Show more content on Helpwriting.net ...
These characteristics of data stream pose a huge challenging problem in this field. Many algorithms for data stream mining have been developed in
scientific and business applications.
3– DATA STREAM MININIG
Fig 2 from http://dspace.cusat.ac.in/jspui/bitstream/123456789/3616/1/report_doc.pdf
Data stream mining refers to the extraction of informational structure from continuous and rapid data streams. It poses a lot of challenges varying to
different aspects that include problems related to storage, computational, querying and mining. For the past recent years, data mining research have
gained due to an extra ordinary increase in computing power and increasing data of streaming information. Due to an increase number in the database
sizes and computational power, many algorithms and techniques have been developed within the past few years. Because of these data stream
requirements, it is important to design new techniques other than the traditional methods that would require the data to be first stored in their database
and then pre–processing it using scientific and complex algorithms that makes several pass over data and the data is generated with high rate which is
very difficult to store. This causes two research challenge:
Fast Mining methods requires to be developed: for example, algorithms that only require one pass on data and have limited memory
Need to

The Pros And Cons Of Fuzzy Logic
As mentioned in the previous section of this chapter trend analysis, analyze just changes in the past years in electricity demand and utilize it to predict
future electricity demand, but there is no process to explain why these changes happened. End users and behavior of end user are not important in this
model. But in end use method of forecasting, statistical information related to customers along with amount of change act as the basis for the forecast.
While in Economical methods, the results are estimated upon the relationship between dependent variables and factors that influence electricity
consumption. Time series and least–square method are used to estimate the relationship. Comparison of these three parametric model shows that ...
Show more content on Helpwriting.net ...
2.3 Factors Affecting Accurate Demand Forecast
The operation of electricity system is strongly influenced by the accuracy of demand forecast as economy and control of electric power system is quite
sensitive to forecasting errors [44–45]. The four important factors affecting load forecast are:
I. Weather conditions Electricity demand has a strong correlation to weather. To develop an efficient and accurate demand forecasting model for
electricity much effort has been put to find a relationship between the weather and the demand of electricity. The change in comforts of customer due to
change in weather conditions resulting in usage of appliances likes air conditioner, space heater and water heater. It also includes use of agricultural
appliances for irrigation. The pattern of demand differs greatly in the areas with large meteorological difference during summer and winter. Dry and wet
temperature, humidity, dew point, wind speed, wind direction, sunshine and amount of precipitation are common weather parameters that influence
electricity demand. Among the weather variables listed above, two composite weather variable functions, the cooling degree days and heating degree
days are broadly used by utility

Nt1330 Unit 7 Igm
Based on the comparison Table 1, Table 2 and Table 3, we identified following set of categories on which we would like to evaluate the above tools and
computing paradigm in subsequent sub–sections: 4.1.1. Distributed Computation, Scalability and Parallel Computation As we can see from the
comparison tables, all computing tools provide these facilities. Hadoop distributes data as well as computing via transferring it to various storage nodes.
Also, it linearly scales by adding a number of nodes to computing clusters but shows a single point failure. Cloudera Impala also quits execution of the
entire query if a single part of it stops. IBM Netezza and Apache Giraph whereas does not have single point failure. In terms of parallel computation
IBM Netezza is fastest due to hardware built parallelism. 4.1.2. Real Time Query, Query Speed, Latency Time The Hadoop employs MapReduce
paradigm of computing which targets batch–job processing. It does not directly support the real time query execution i.e OLTP. Hadoop can be
integrated with Apache Hive that supports HiveQL query language which supports query firing, but still not provide OLTP tasks (such as updates and
deletion at row level) and has late response time (in minutes) due to absence of pipeline ... Show more content on Helpwriting.net ...
Also, since Clouera and Giraph perform in memory computation they do not require data input and data output that saves a lot of processing cost
involved in I/O. None of the tools require the ETL (Extract, Transform and Load) service, thereby they save a major cost involved in data
preprocessing. Hadoop is highly fault tolerant that is achieved by maintaining multiple replicas of data sets, and its architecture that facilitates dealing
with frequent hardware malfunctions. Giraph achieves fault tolerance using barrier checkpoints.

Security Is A Fundamental Part Of A Structured Business
Security is a basic need for people to feel safe about themselves and their belongings. Security is an essential part of everyone's life and the more secure
something it the more stress free you can be. For data companies, security is a fundamental part of a structured business; this leads them to develop data
security, more specifically, security in data warehousing. Data security is the protection of data from destructive forces and from the unauthorized use
or undesirable actions, in systems such as databases. Data warehouses are central repositories of integrated data from one or more disparate sources.
They store current and historical data and are used for creating analytical reports for knowledge workers throughout the enterprise. ... Show more
Data mart is a simple form of a data warehouse that is focused on a single subject, such as sales, finance or marketing. Data marts are often built and
controlled by a single department within an organization. Given their single–subject focus, data marts usually draw data from only a few sources. The
sources could be internal operational systems, a central data warehouse, or external data. De–normalization is the norm for data modeling techniques in
this system. Online Analytical Processing or OLAP is characterized by a relatively low volume of transactions. Queries are often very complex and
involve aggregations. OLAP system response time is an effectiveness measure that is used by Data Mining techniques. OLAP databases store
aggregated, historical data in multi–dimensional schemas. OLAP systems typically have data latency of a few hours, as opposed to data marts, where
latency is expected to be closer to one day. Online Transaction Processing or OLTP is characterized by a large number of short on–line transactions.
OLTP systems emphasize very fast query processing and maintaining data integrity in multi–access environments. OLTP systems the number of
transactions per second measures effectiveness; this contains detailed and current data. The schema used to store transactional databases is the entity
model. Normalization is the norm for data modeling techniques in this system. Predictive Analysis is about

The Data Warehouse Essays
A Data Warehouse is a database–centric system of decision support technologies used to consolidate business data from many disparate sources for use
in reporting and analysis (Data Warehouse). Data Warehouses and Data Warehouse systems are primary used to server executives, senior management,
and business analysts with accurate, consolidated information from various internal and external sources to aid in the process of making complex
business decisions (Data Warehouse Process).
The term Data Warehouse was first coined by Bill Inmon, who has been commonly recognized as the "father of data warehousing" and is the lead
proponent of the normalized or sometimes referred to as the top–down, approach to Data Warehouse design (Reed, M.)(Data ... Show more content on
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Kimball's approach also involves the building of business process specific data marts that collect the transactional components of the original data and
form them into what are known as "facts" (the data that needs to be measured such as sales or purchases) and links them to well defined dimensions
(the data that facts are measured against for instance, the customers that make the purchases, the employees that make the sales, and the dates those
transactions occurred). This results in a logical structure commonly referred to as the star–schema data mart. It derives its name from the star like
appearance of a central fact table sorrowed by multiple dimension tables with each dimension table linked only to the fact table. The main advantages
of the dimensional model over the normalized model are greater performance and quicker time to initial benefit. The dimensional model achieves this
performance via the simplified logical structure of the star–schema data mart greatly reducing response–time when reacting to user requests as opposed
to the spread

Examples Of Historical Data Trends
Historical Data Trends
Now that the E–Station's database is in production and functioning properly, the next phase is to obtain historical data trends. It is necessary to use both
internal and external data sources to build trends for current and future analysis. The database management team will build a central data warehouse
(DWH) repository to store the data needed for reporting. The team will use the same software used earlier to secure the database tables, namely, SQL
Server Data Tools (SSDT). The software also has built in capabilities for Business Intelligence (BI) reporting functions that include summarized reports
along with charts and graphs. This paper will outline how the E–Station database management team will use SSDT–BI ... Show more content on
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Satisfied customers mean they are returning on a continuous basis. After the Schema model, the DBA will create the necessary tables and queries
needed for reporting purposes to show where the business value stands (Chapple, 2017).
Fact Table
The fact table stores the customers charging information. The data is numerical and easily manipulated for summing the rows (Chapple, 2017). The
analyst will pull a profit report using criteria by station, spot, and equipment.
Dimension Table
The dimension table will store the primary key of the charging units, and the fact table will store the foreign key, linking back to the dimension table,
which has the qualitative data (Chapple, 2017). The dimension table data is the sales of kilowatts used, along with the location and equipment used.
Besides, the dimension table will also store the geography and time data as the attributes. Both the dimension and fact tables need indexing on the start
time for efficient query retrieval, which is a significant key to use for appending data in chronological order (Brown, 2017). Thus, the best load strategy
is to update the new or changed data. The DBA will perform load testing and query performance testing to ensure the granular level data is consistent
and accurate. Also, index testing is necessary to ensure system performance is adequate.
Reporting
The analyst will use dashboards created by the DBA to pull the weekly, monthly, and quarterly reports. The reports will

The Principle Of Affinity Chromatography
The objective of this lab was to learn the principle of affinity chromatography by isolating a carbohydrate–binding protein from an extract of jack bean
meal.
The protein that was used for this lab came from jack bean meal, and it is identified as Concanavalin A, or Con–A. This is a lectin protein that has the
ability to bind with glucose, but it is not a glycoprotein. Furthermore, the monomeric molecular weight of this protein is 25,5001. Concanavalin A also
exists as a dimer and a tetramer, and this depends upon the pH of this substance. Finally, Con–A requires a transition metal ion such as manganese as
well as calcium ions for binding purposes.
1. Begin by inserting a cheesecloth into a syringe and pressing the cheesecloth to the very bottom of the column.
2. Pour 10 ml of the affinity gel into the column. Allow a few minutes for the solution to harden and transform into a gel–like consistency.
3. Fill the rest of the column with NaCl, and do so by pouring the solution along the interior side of the column. This allows the gel to become moist.
4. Next, gather 0.5 ml of the protein extract and save this portion in a separate container. This will be used for the preparation of the membrane.
5. Load the remaining protein mix into the column, and this will begin to bind within the column. Once the solution has settled, fill the column with
NaCl a total of four times; as the wash is flowing through, collect and label the protein solution in fractions, and save for the

Definition Of Business Intelligence ( Bi ) Essay
Definition of Business Intelligence (BI)
A strategic BI starts with elements of Business Intelligence. BI strategy includes the competitive advantages in the planning process. It begins with a
comprehensive view from both technical and business standpoint.
What to look for –
Fully adopted BI tools that support both short term and long term goals of the company. The Short term strategy must be framed so as to underpin the
long term ones.
The assessment Session–
Recognizing the values of the company
Identifying the internal capacity backed by external environment
Merge both to set the new vision of the company.
When you are framing the BI strategy you must look for Internal and External factors.
Internal Factors include – Goal, Resource, and Structure and system of a company while External Factors includes – Competitors, Communities,
Clients, State Rules, Industry, Interest group, Social Media, and Public.
Defining BIM – Business Intelligence Model is not just producing a report, analyzing them and heading for dashboards. It involves ETL (Extract–
Transform–Load), DW (Data Warehouse), portal and MDM (Master Data Management ) as well.
ETL (Extract–Transform–Load) covers the data loading process from the source to the warehouse system.
DW or EDW is the report of the data analysis. The integration of data from various sources is compiled by Enterprise Data Warehousing system.
Manual Integration, Application Based Integration, Middle–ware Data Integration, Virtual

Contrast Of Agile And Waterfall Development Methods
Contrast of Agile and Waterfall Development Methods Application requirements are provided by stakeholders and users for all development efforts.
This is true for both agile and waterfall development projects. The difference is the amount of requirements that are provided. In the waterfall approach,
all requirements are provided at the beginning of the project (Israr Ur Rehman, 2010, p. 2). Specifically, for a new application, the expectation is that
stakeholders are able to provide all the requirements for the new application. Stakeholders, project managers, business analysts, users of the application
meet to identify and document the requirements. Detailed application documentation is created, reviewed and signed off by the appropriate stakeholder
(Ove Armbrust, 2011, p. 239). Following the agile methodology, requirements are defined for the phase to be delivered. The goal is to break up the
application into iterations and define the requirements for the iterations or stories (Ove Armbrust, 2011, p. 239). In the agile implementations, the
requests are stored in a backlog. This is the list stories remaining to be developed. Requirements documentation generated from a waterfall project is
extensive; in contrast to agile where the documentation is limited to the story or stories to be developed (Ove Armbrust, 2011, p. 239). In my
experience, the requirements documentation has been stored in a central repository to be referenced during the project.

Information Retrieval And Evaluating Its Usefulness
Study Report on
Information retrieval and evaluating its usefulness
Adarsh Murali Kashyap
800828747
Table of Contents
1. Introduction III
2. ETL process III
3. Creation of a warehouse using SQL statements VII
4. OLAP operations VII
5. Data Mining IX
5.1. Cluster Analysis IX
5.2. Association Rule Mining XII
5.3. Outcome of ETL, OLAP, Mining operations XII
6. Data Analytics and its usefulness for business XII
7. Usage of production logs to test and engineer an application's performance: XIII
8. References: XIV
9. VB Code XV
1. Introduction
Information is very important to a business organization. Information helps in identifying opportunities, understanding the customers in ... Show more
There are many ways of cleaning data using many tools that help in formatting, removal of unwanted parts of data. Here I will make an effort to
demonstrate a method of extracting, cleaning of data files using Visual Basic and evaluating its usefulness. This report comprises certain topics that I
have studied during the course of my masters program which includes few concepts of data warehousing, data management and data analytics. These
topics cover different ways of data manipulation such as extraction, transformation techniques, loading of data using SQL queries (creation of tables,
insertion of values and checking their normal forms), creation of a data warehouse, evaluating its usefulness by measuring several factors, applying data
mining techniques to analyze data in a better way that will lead to improved understanding of business and importance of analytics on business data.
2. ETL process
ETL is a process of managing databases by performing the below mentioned steps:
Step 1: Extraction – Extract data from data sources.
Step 2: Transformation
 Data cleaning: remove errors, inconsistencies and redundancies.
 Data transformation: transform data into warehouse format.
 Data reduction: remove useless data, shrink data without loss of information.
Step 3: Loading – Load transformed data into database/warehouse.
I will be considering "Movies.list" file from IMDB

Nt1310 Unit 1 Review Of Related Studies
In the eighth week peer students was requested to provide for their discussion assignment some written material that would entail material for
Smartphone with, which would have software embedded upon it for Data Warehousing and operational data that has access control and integrity
controlling for their user and/or users. The usage of the Smartphone would be handle by user acknowledged as sales people and/or sales persons for
informational query of data that may either be deleted, updated or just to place an order for fulfillment. The supporting evidence needed to properly
have functional operational for security and it may come in several operational factoring of factors such as information Integrity, Personal Integrity,
Company Integrity and just the normal notion the device integrity acknowledged as Android or Blackberry. The two devices that function may have
their information in harm's way by data usage on both devices. This information can be damaged in one or more ways named as risk or attack. The
Personal Integrity that is considered to relate to the information posed by Smartphone, can have many reference selections ... Show more content on
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The Access Control prevention can be built from a standards standpoint that can enable a great number of protective methods in its existence .As
www.nyu.edu reminds us "Software Updates: System must be configures to automatically update operating system software, server application
(webserver, mail server, database, server, etc),client software(web–browsers, mail–client, offices suites, etc),and malware protection software(anti–
virus, anti–spyware, etc).For Medium or High Availability System, a plan to manually apply new updates within a documented time period is an
acceptable

An Introduction To Social Web Mining And Big Data?
"Torture data long enough and it will confess. . . but may not tell the truth" (Turbin, Volonino, & Woods, 2015, p. 88). In the world of Big Data
Analytics (BDA), companies who successfully harness the potential of big data are rewarded with valuable insight that could lead to a competitive
advantage in their market sector. Consequently, it is imperative to successfully extract data from all relevant data sources that can provide answers to
questions that companies set out to answer with BDA. One such source is data generated by social media (Schatten, Ševa, & Đurić, 2015). As such, this
paper will review the findings of Schatten, Ševa, & Đurić's(2015) article on how social web mining and big data can be utilized within the social ...
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90) (ETL) to prepare the data for analysis. Also, context, time and space (location) are essential to provide meaningful data for analysis.
Moreover, it is critical to use the correct analytical tools and understand the drawbacks of each to provide the accurate results. In the article, the authors
demonstrated how a combination of tools could be used to extract, transform, and analyze data using social semantic web techniques and big data
analytics such as social web mining, social and conceptual network analytics (SNA), speech recognition and mission vision, natural language
processing (NLP), sentiment analysis, recommender systems and user profiling, and semantic wiki systems to address various research questions. As
the authors aimed to analyze the Croatian political landscape, analytical tools like NLP were less effective as there was a limited database for the
Croatian language. Thus, the authors had to use a combination of tools to overcome the shortcomings of the NLT to create a true picture of the
landscape. Also, from personal experience, standard speech recognition, for example Siri, has trouble understanding accents. Thus, words spoken in
outlier accents are not processed correctly and could produce a skewed analysis based on inaccurate speech recognition patterns.
As such, understanding the requirements of the predication goal, selecting a combination of relevant tools, analyzing and producing effective models
predicting

Service Data Storage For Usps Facilities
In United States Postal Service, their large computer networks and advances in storage ability allows them to store immense amounts of data and easily
get access of it from any facilities for analyzing. With the endless quantities of data that is constantly being collected from the internet, customers, and
facilities, USPS is constantly improving their ways in storing data as efficiently as possible. The Enterprise Data Warehouse is the primary data storage
for USPS. It approximately 35 petabytes of storage capacity which allows it to store all the data collected from over 100 systems ranging from
financial, human resources, transactional, etc. To process and store data into the EDW, it requires three steps of extract, transform and load. During the
extraction process, the data is taken from the source of different systems within the USPS facilities. Then the transform process structures the data using
rules or tables and turns it into one consolidated warehouse format. It also combines some data with others so it is easier to be transferred between
different databases. The final process is the load with is basically integrating and writing the data into the database which can be accessed from any
facilities and systems within the USPS. The EDW allows USPS to store any amount of data as efficient as possible at the lowest cost and quickest
processing speed. It also allows the data to be used and migrate from database to database easily for analysis. USPS collects data

Case Study Of Bbma
As the case talks about the company BCBSMA (which is licensed insurance company)thats comes under healthcare sector , headquarters in Boston.
BCBSMA as per the case has more than 3 million members in the state and 106 million member in total .
In todays senario healthcare is the fastest growing industry in the world with 10% of the worlds GDP.
Healthcare consists of
1) hospital activities
2) health related activities
3) insurance department
Healthcare Services Industry
It includes various establishments dealing in different type of services like testing, outsourcing, transcription, quality assurance, validation, compliance,
chemical analysis, and other types of services. The global market share of biotechnology services industry is worth US $ 50 billion, which is soon
expected ... Show more content on Helpwriting.net ...
Ques 3. How Big Data Analytic was used and implemented back into the business. In the Business Big Data Analytics tools that were already being
used within the business, as well as reviewing the other leading products available on the market. Ultimately, the team chose IBM Cognos BI as the
best solution.
"Cognos Business Intelligence was by far the most popular solution, with more than 600 users across the business already reaping the value of its
extensive capabilities," explains Vangala. "This meant there was a lot of expertise that we could leverage, and the wider adoption of the solution was a
natural evolution. Using IBM Cognos BI, we saw success very quickly, and the executive management team took notice, which led to the decision to
make it the corporate standard." After the initial implementation, the demand for analytics grew steadily within the business, and the Business
Intelligence team continued to develop new solutions – not only reports, but also real–time dashboards and OLAP cubes for ad–hoc

Orchestrate Healthcare: A Case Study
Executive Overview Orchestrate Healthcare understands that Saint Francis Medical Center (SFMC), is a 284–bed facility serving more than 650,000
people throughout Missouri, Illinois, Kentucky, Tennessee and Arkansas. SFMC is guided by a mission to provide a ministry of healing and wellness
inspired by Christian philosophy and values, as a progressive, innovative regional tertiary care referral center. Presently, using McKesson Horizon
Clinicals, as well as, other ancillary systems, SFMC will be converting to Epic scheduled for July, 2016. This conversion will create tremendous value
for all stakeholders within the healthcare center's footprint by providing them the longitudinal clinical history of their patients in the new EPIC EMR.
Orchestrate Healthcare is confident we can successfully support SFMC's services for Data Conversion with our talent and experience. OHC
understands that ... Show more content on Helpwriting.net ...
For unit testing purposes, we like to produce an initial extract of 5–10 records, to try to load into an Epic environment. During this time, manually
manipulation of the extracted messages is necessary to allow the data to file into Epic. These messages are loaded one at a time and the initial errors are
"worked" by modifying the message or by adjusting the Epic interface translation tables or other configuration to accommodate. Once we get the
messages to file correctly, without errors, into Epic all changes that are needed are added to the extraction code. A second mini extract of a larger subset
may be performed, such as 200–300 records and then run into Epic and validated. This new mini–extract provides a basis to estimate the amount of
time and number of staff that will be required to validate the records in Epic. These initial messages provide a view into how complex the build will be,
the lower the data quality the more complex it can become as well as requiring a larger investment of time by staff manually validating and verifying
the data or increase in staff

Big Data has gained massive importance in IT and Business...
Big Data has gained massive importance in IT and Business today. A report recently published state that use of big data by a retailer could increase its
operating margin by more than 60 percent and it also states that US health care sector could make more than $300 billion profit with the use of big data.
There are many other sectors that could profit largely by proper analysis and usage of big data.
Although big data promises better margin's, more revenue and improvised operations it also brings new challenges to the It infrastructure which is
"extreme data management" .At the same time these companies should also need to look at workload automation and make sure it is robust enough to
make to handle the needs that the big data is ... Show more content on Helpwriting.net ...
We can combine these with existing enterprise data warehouse to create an integrated information supply chain which will be very effective. The goals
of an information supply chain would be to utilize and integrate the many various raw source data that already exists in organizations, analyze this data
and lastly to deliver the analytical results to business users. This information supply chain enable the data component called Smarter Computing.
Supporting Extreme Workloads has always been a challenge faced by the computing industry. For example the business transaction processing systems
have supported extreme transaction workload even since the beginning of data processing. With the advent of new business these organizations have
employed custom and optimized transaction processing systems to handle application workloads that created boundaries which was beyond what could
be handled by the generalized technologies. There are many examples for this these type of application like :
1. Airline reservation systems
2. Retail point–of–sale terminals
3. Bank ATM systems
4. Financial trading systems
5. Mobile phone systems
Most recently applications for sensor network that track items with RFID tags can also be added to this list of examples.
In the present scenario there has been a similar

Business Intelligence and Technology
Introduction
In modern business, vast amounts of data are accumulated, which makes the decision–making process complicated. It is a major mutual concern for all
business and IT sector companies to change the existing situation of "mass data, poor knowledge" and support better business decision–making and
help enterprises increase profits and market share. Business intelligence technologies have emerged at such challenging times. Business today has
compelled the enterprises to run different but coexisting information systems.
ETL plays an important role in BI project today. ETL stands for extraction, transformation and loading.
ETL is a process that involves the following tasks: Extracting data from source operational or archive systems which are the primary source of data for
the data warehouse; transforming the data – which may involve cleaning, filtering, validating and applying business rules; loading the data into a data
warehouse or any other database or application that houses data.
ETL Tools provide facility to extract data from different non–coherent systems, transform (cleanse & merge it) and load into target systems. The main
goal of maintaining an ETL process in an organization is to migrate and transform data from the source OLTP systems to feed a data warehouse and
form data marts. ETL process is the basis of BI and it is a prime decisive factor for success or failure of BI.
Today, the organization has a wide variety of ETL tools to choose from market.

Analyzing The Data Warehouse Storage Building A Business...
Abstract Recommendations are to utilize a data warehouse to retrieve the data to strategize analysis, root cause of issues, and Data warehouse is one of
the most important components in a Business Intelligence (BI) architecture. Building the company's data warehouse will be difficult from building an
operational system. Users will collect data on the overall business of the organization. Requirements to gather will include data elements, recording of
data in terms of time, data extract from source systems, and business rules. Before moving data into the data warehouse, users will indicate where the
data concerning metrics and business dimensions will come from. The data warehouse Storage Architecture will assist users in minimizing the ... Show
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After the data warehouse starts working properly with the ability to provide queries, print reports, perform analysis, and use OLAP, the information
delivery component should continue to grow and expand in order to influence the proper design of the information delivery components.
Introduction
ABC Industries is a diversified global organization that provides a variety of services, such as financial, technical, and manufacturing of products
across the globe. Its facilities are located in Europe, Asia, and the United States with revenue of $35 billion. The company has suffered problems with
its retrieving qualitative data from one source. Recommendations are to utilize a data warehouse to retrieve the data to strategize analysis, root cause of
issues, and Data warehouse is one of the most important components in a Business Intelligence (BI) architecture. Inmon (2005) defines data warehouse
as "a subject–oriented, integrated, time–variant, and non–volatile collection of data in support of management's decision making process" (p. 29).
According to Hoffer et al., 2007; Inmon, 2005 characterizes a data warehouse is a
Subject–oriented: Data from various sources are organized into groups based on common subject areas that an organization would like to focus on, such
as customers, sales, and products. Integrated: Data warehouse gathers data from various sources. All of these data must be

What Are Database Management?
What is Database Management In the new age of business it has become a must to keep up with its always–upgraded systems and technology. If a
Business has useful information about the needs of clients within its market, it can design a better way of marketing its product and earn a larger market
share. If a business knows what their competition is doing, the business can then develop a strategy around its competitors to meet or better their
competitors, which helps that business with the information on its competitor gain a competitive advantage. If that business has the inside information
about its current customers, it can help that company build a personal relationship with it customers and influence their perception of the company and
... Show more content on Helpwriting.net ...
At this point the data should be reliable and have little to no mistakes within it and as like everything else in business there is a deadline for when the
data should be distributed. Continuously improving data accuracy can increase the trust of a business. This can be done by identifying and outliers of
data that seems a bit off, and continuously cleansing and monitoring quality over time no matter how big and what format the file is in. ETL (extract
transform load) tools can help de dupe, validate, standardized things such as addresses and company name, and enrich your data. By doing these things
it helps you create clean high quality data for your own use or to sell. External references are used to do all this data hygiene to perform things like
cleaning up postal records or even having the right business name. The data at the end of the process should not only be reliable but should fit the
client's needs and the needs of the data users such as data engineers. This data typically has ID's associated with it to make it easier to manipulate and
allow for other engineer's easy use with such data in specific tools such as red point an ETL (Extract Transform Load) Acquiring the Data After
planning on what data is needed a company must come up with a method of acquiring the data. Collecting new data, converting or transforming data
that

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

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