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2990.202020 - SPRING 2020 - INFOTECH IMPORT IN STRAT PLAN (ITS-831-41) - FULL TERM

Final Portfolio Project Draft Anish Kumar Polasani on Sat, Apr 18 2020, 8:42 PM

47% highest match Submission ID: 8af26740-f808-46a0-a746-d4dce74dd746

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DWArc.doc Running Head: 1 DATA WAREHOUSE ARCHITECTURE

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1 DATA WAREHOUSE ARCHITECTURE

9

1 DATA WAREHOUSE ARCHITECTURE

Anish Kumar Polasani

2 UNIVERSITY OF CUMBERLANDS

1 DATA WAREHOUSE ARCHITECTURE

3 DATA WAREHOUSE IS AN INFORMATION SYSTEM THAT COMPRISES COMMUNICATIVE AND HISTORICAL DATA FROM ONE OR A NUMBER OF SOURCES. Its purpose is to simplify the process of analysis and reporting in an organization. It can also be used as a place to obtain truthful version of information for the intentions of forecasting and making decisions. 4 FOR ONE TO IDENTIFY A DATA WAREHOUSE, IT HAS TO CONTAIN THE FOLLOWING CHARACTERISTICS: Subject-orientation, integration, time variation and non- volatility. 4 THERE ARE THREE TYPES OF DATA WAREHOUSE ARCHITECTURES. These are; 5 THE SINGLE-TIER, TWO-TIER AND THREE-TIER ARCHITECTURE.

Prompt 1: 1 DATA WAREHOUSE ARCHITECTURE

6 THE MAJOR COMPONENTS OF DATA WAREHOUSE ARCHITECTURE INCLUDE:

4 DATA WAREHOUSE DATABASE

7 THIS IS THE FOUNDATION OF THE DATA WAREHOUSING ENVIRONMENT WHICH WAS IMPLEMENTED ON THE RELATIONAL DATABASE MANAGEMENT SYSTEM (RDBMS) TECHNOLOGY. 4 THIS KIND OF IMPLEMENTATION, HOWEVER, IS CONSTRAINED BY THE FACT THAT TRADITIONAL RDBMS IS NOT OPTIMIZED FOR DATA WAREHOUSING BUT FOR TRANSACTIONAL DATABASE PROCESSING ONLY. This therefore slows down performance (Fazal Et Al, 2017).

However, alternative approaches have been developed and are the ones being put to use. These include:

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· Parallel relational databases – In a data warehouse, relational databases are set up since they allow for scalability. 4 THESE PARALLEL RELATIONAL DATABASES ADDITIONALLY ALLOW SHARED MEMORY ON VARIOUS MULTIPROCESSOR CONFIGURATIONS.

· NEW INDEXING STRUCTURES – THE LATEST INDEX STRUCTURES ARE INCORPORATED IN THE IMPROVEMENT OF SPEED AND TO EVADE RELATIONAL TABLE SCAN.

8 · MULTIDIMENSIONAL DATABASES (MDDB) – THESE DATABASES ARE USED TO OVERRIDE ANY LIMITATIONS SET IN PLACE DUE TO THE RELATIONAL DATA MODEL.

Metadata

This is a simple technological concept that is used to define the data warehouse. Metadata is used in the process of constructing, sustaining and handling the data warehouse. 9 METADATA PLAYS A CRUCIAL ROLE IN DATA WAREHOUSE ARCHITECTURE SINCE IT LAYS DOWN THE FOUNDATION, UTILIZATION, FEATURES AND VALUES OF DATA WAREHOUSE ARCHITECTURE. Moreover, it describes how data can be altered and processed (Fazal Et Al, 2017).

4 SOURCING, ACQUISITION, CLEAN-UP AND TRANSFORMATION TOOLS (ETL TOOLS) DATA TRANSFORMATION, SOURCING AND MIGRATION TOOLS ARE USED TO PERFORM A NUMBER OF TASKS SUCH AS SUMMATIONS, CONVERSIONS AND DATA ALTERATIONS IN A UNIFORM MATTER IN THE DATA WAREHOUSE. 2 THEY CAN ALSO BE REFERRED TO AS EXTRACT, TRANSFORM AND LOAD (ETL) TOOLS.

6 THE FUNCTIONS OF ETL TOOLS INCLUDE:

· Making data anonymous according to regulations.

· Getting rid of unwanted information

· Performing calculations on derivative data and summaries

· Searching and substituting common descriptions from different data sources

· Populating missing data with defaults

Access Tools

7 THESE ARE FRONT-END TOOLS THAT HELP USERS TO INTERACT WITH THE DATA WAREHOUSE. For ease of access, a number of these tools require the presence of an information specialist. These access tools have four major categories. These are: 7 THE QUERY AND REPORTING TOOLS, ONLINE ANALYTICAL PROCESSING TOOLS, APPLICATION DEVELOPMENT TOOLS AND DATA MINING TOOLS (FAZAL ET AL, 2017).

10 THERE ARE VARIOUS TYPES OF DATA TRANSFORMATIONS NEEDED TO PREPARE DATA FOR A DATA WAREHOUSE. They include:

· Format revision – This is where standardization and changing of fields that contain numeric and text data types is done, so as to provide values that could be properly understood by viewers (Biswas, 2018).

· Merging of information - This type of data transformation in ETL means that different fields are brought together using a connection, such as price of products, depiction, types of packages used, and viewing these fields as a single entity.

· Decoding of fields- In multiple-source structures, similar data items are defined by an assortment of field values. This ETL transformation type changes codes into values that make sense to the end-users. This therefore helps to avoid legacy systems are infamous for using enigmatic codes to symbolize business values (Biswas, 2018).

Over the years, there have been major transformations to the upgrades for data warehousing. These technological advancements have resulted to data

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warehouses that are bigger, faster, and serve a number of purposes. 9 ONE OF THE CURRENT KEY TRENDS IS THE PHYSICAL AND LOGICAL CONSOLIDATION WHICH HELPS IN MINIMIZING COSTS. In as much as “datafication” is beneficial in the world of data warehousing, it does not mean that more money should be thrown into the system. In other words, a hundred times the data does not mean that a hundred times the cost involved should be translated. Therefore, expanding the data warehouses must be associated with less monetary inputs. This can be done through a combination of compression, virtualization and multi-tenant databases as well as servers that are modified to handle big volumes of data and handle heavy workloads.

Another important trend in data warehousing is the improvement on performance and optimization. In data warehouse-capable platforms, progressive functionality for controlling and managing input and output, disk storage and CPU memory are currently being included. New entrants of the market are currently placing optimization into consideration as a way to differentiate themselves from their competitors. Additionally, current data warehouse vendors are emphasizing on the importance of optimizing on storage through usage-based data placement approaches (Mukherjee & Ker, 2017).

Prompt 2: Big Data

Big data is a mishmash of structured, unstructured and semi-structured data collected by organizations that can be used to mine information and applied in advanced analytics applications such as machine-learning projects and predictive modeling. Big data does not necessarily equate to big volumes of data. However, big data placements incorporate terabytes, petabytes or even exabytes of information collected over a long period of time.

Big data originates from different sources such as business transaction systems, medical record systems and social networks among others. An example of big data being used professionally, that I have witnessed, is the consumer data system (Oussous Et Al, 2018).

There are a number of analytics that are done with the information sets found in the consumer data system. These are:

1. Customer satisfaction analysis – All the information gathered could help in revealing how customers feel about the staff members of the company, the product or services being delivered to the said clients or other complaints.

2. Market analysis – This involves the information that can be used to make informed and innovative decisions on the promotion of new brands, services and initiatives.

3. Comparative analysis – This incorporates the evaluation of user behavior as well as the observation of consumer engagements in order to compare one company’s brand authority and services with that of its competition.

10 DESPITE ITS ADVANTAGES, BIG DATA IS PLACING HEAVY DEMANDS ON ORGANIZATIONS AND DATA MANAGEMENT TECHNOLOGIES. This is because they require heavy monetary investments to suit the needs of an organization. Additionally, accessibility of data in big data systems is a challenge. This forces the companies to further invest in data catalogs, placing a strain on finances (Oussous Et Al, 2018).

Prompt 3: Green Computing

Green computing is the learning and practicing of environmentally sustainable computing or IT. Its goals are to cut down on the use of hazardous materials, maximize the energy efficiency of a product and reduce waste from industries. There are a number of ways that organizations can encourage and promote green IT. They include:

1. Purchasing energy efficient hardware – Hardware such as notebooks, servers and workstations that meet the guidelines of Environmental Protection Agency lower power consumptions. Such systems have good ratings from the Electronic Product Environmental Assessment Tool (EPEAT). Additionally, these organizations can purchase microprocessors that increase the output of a company without increasing the energy usage (Okewu Et Al, 2017).

2. Using power management practices – Organizations can also employ some power management practices such as using products that control and provide flexibility over the power consumption of computers and other gadgets. The organizations could as well use third-party power management programs that allow staff members of organizations to manually reduce power voltage to the CPU.

3. Optimizing the design of data centers – Firms could employ a data center design that involves the hot and cool aisle layout. This layout would

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tremendously cut down on the amount of energy needed to run the center.

10 PRICEWATERHOUSECOOPERS LLP IS AN EXAMPLE OF A COMPANY THAT HAS IMPLEMENTED GREEN COMPUTING STRATEGIES SUCCESSFULLY. The company built a LEED Gold licensed data center which ended up cutting down its carbon footprints by twenty percent within two years (Mitchell, 2010) A data warehouse is an important segment to the success of an organization. It is mandatory for an organization to keep up with technological advancements in order to ensure effectiveness of its data warehouse. Also, big data systems are necessary evils to every company that wants to prosper. Companies should therefore put in efforts to deal with the challenges that come with it so as to reap its benefits. In line with big data systems, green computing is now a necessity in industries so as to encourage energy saving.

References

Biswas, N., Sarkar, A., & Mondal, K. C. (2018, July). Empirical Analysis of Programmable ETL Tools. 11 IN INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE, COMMUNICATIONS, AND BUSINESS ANALYTICS (PP. 267-277). Springer, Singapore.

Fazal, T., Strutt, D., Gibb, R., Armstrong, M., Adendorff, M., & Helal, R. (2017). U.S. Patent No. 9,740,992. Washington, DC: U.S. 12 PATENT AND TRADEMARK OFFICE.

2 MUKHERJEE, R., & KAR, P. (2017, January). 2 A COMPARATIVE REVIEW OF DATA WAREHOUSING ETL TOOLS WITH NEW TRENDS AND INDUSTRY INSIGHT. 13 IN 2017 IEEE 7TH INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC) (PP. 943-948). IEEE.

14 OKEWU, E., MISRA, S., MASKELIŪNAS, R., DAMAŠEVIČIUS, R., & FERNANDEZ-SANZ, L. (2017). 14 OPTIMIZING GREEN COMPUTING AWARENESS FOR ENVIRONMENTAL SUSTAINABILITY AND ECONOMIC SECURITY AS A STOCHASTIC OPTIMIZATION PROBLEM. SUSTAINABILITY, 9(10), 1857.

12 OUSSOUS, A., BENJELLOUN, F. Z., LAHCEN, A. A., & BELFKIH, S. (2018). 12 BIG DATA TECHNOLOGIES: A survey. 12 JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 30(4), 431-448.

Mitchell, R. L. 15 (2010, OCTOBER 25). PricewaterhouseCoopers: 16 CHOPPED CARBON FOOTPRINT BY 20% IN TWO YEARS. RETRIEVED APRIL 18, 2020, FROM HTTPS://WWW.COMPUTERWORLD.COM/ARTICLE/2549768/PRICEWATERHOUSECOOPERS--CHOPPED-CARBON- FOOTPRINT-BY-20--IN-TWO-YEARS.HTML

Citations (16/16) 1 https://panoply.io/data-warehouse-guide/

2 Another student's paper

3 Another student's paper

4 http://www.test3.guru99.com/data-warehouse-architecture.html

5 Another student's paper

6 Another student's paper

7 https://www.cloudmoyo.com/blog/data-architecture/what-you-need-to-know-about-advanced-data-warehousing/

8 Another student's paper

9 Another student's paper

10 Another student's paper

11 Another student's paper

12 Another student's paper

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Matched Text

13 Another student's paper

14 Another student's paper

15 Another student's paper

16 Another student's paper

Suspected Entry: 100% match

Uploaded - DWArc.doc

DATA WAREHOUSE ARCHITECTURE

Source - https://panoply.io/data-warehouse-guide/ Data Warehouse Architecture

Suspected Entry: 100% match

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DATA WAREHOUSE ARCHITECTURE

Source - https://panoply.io/data-warehouse-guide/ Data Warehouse Architecture

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DATA WAREHOUSE ARCHITECTURE

Source - https://panoply.io/data-warehouse-guide/ Data Warehouse Architecture

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DATA WAREHOUSE ARCHITECTURE

Source - https://panoply.io/data-warehouse-guide/ Data Warehouse Architecture

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DATA WAREHOUSE ARCHITECTURE

Source - https://panoply.io/data-warehouse-guide/ Data Warehouse Architecture

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UNIVERSITY OF CUMBERLANDS

Source - Another student's paper University of the Cumberlands

Suspected Entry: 78% match

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THEY CAN ALSO BE REFERRED TO AS EXTRACT, TRANSFORM AND LOAD (ETL) TOOLS

Source - Another student's paper Such tools are also referred to as an extract, transform, and load (ETL) tools

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MUKHERJEE, R., & KAR, P

Source - Another student's paper Mukherjee, R., & Kar, P

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A COMPARATIVE REVIEW OF DATA WAREHOUSING ETL TOOLS WITH NEW TRENDS AND INDUSTRY INSIGHT

Source - Another student's paper A comparative review of data warehousing ETL tools with new trends and industry insight

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DATA WAREHOUSE IS AN INFORMATION SYSTEM THAT COMPRISES COMMUNICATIVE AND HISTORICAL DATA FROM ONE OR A NUMBER OF SOURCES

Source - Another student's paper A data warehouse architecture is an information system that contains communicative or historical data from multiple or single sources

Suspected Entry: 69% match

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FOR ONE TO IDENTIFY A DATA WAREHOUSE, IT HAS TO CONTAIN THE FOLLOWING CHARACTERISTICS

Source - http://www.test3.guru99.com/data-warehouse-architecture.html A data warehouse has following characteristics

Suspected Entry: 78% match

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THERE ARE THREE TYPES OF DATA WAREHOUSE ARCHITECTURES

Source - http://www.test3.guru99.com/data-warehouse-architecture.html Data Warehouse Architectures There are mainly three types of Datawarehouse Architectures

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DATA WAREHOUSE DATABASE

Source - http://www.test3.guru99.com/data-warehouse-architecture.html Data Warehouse Database

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THIS KIND OF IMPLEMENTATION, HOWEVER, IS CONSTRAINED BY THE FACT THAT TRADITIONAL RDBMS IS NOT OPTIMIZED FOR DATA WAREHOUSING BUT FOR TRANSACTIONAL DATABASE PROCESSING ONLY

Source - http://www.test3.guru99.com/data-warehouse-architecture.html Although, this kind of implementation is constrained by the fact that traditional RDBMS system is optimized for transactional database processing and not for data warehousing

Suspected Entry: 68% match

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THESE PARALLEL RELATIONAL DATABASES ADDITIONALLY ALLOW SHARED MEMORY ON VARIOUS MULTIPROCESSOR CONFIGURATIONS

Source - http://www.test3.guru99.com/data-warehouse-architecture.html Parallel relational databases also allow shared memory or shared nothing model on various multiprocessor configurations or massively parallel processors

Suspected Entry: 63% match

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· NEW INDEXING STRUCTURES – THE LATEST INDEX STRUCTURES ARE INCORPORATED IN THE IMPROVEMENT OF SPEED AND TO EVADE RELATIONAL TABLE SCAN

Source - http://www.test3.guru99.com/data-warehouse-architecture.html New index structures are used to bypass relational table scan and improve speed

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SOURCING, ACQUISITION, CLEAN-UP AND TRANSFORMATION TOOLS (ETL TOOLS) DATA TRANSFORMATION, SOURCING AND MIGRATION TOOLS ARE USED TO PERFORM A NUMBER OF TASKS SUCH AS SUMMATIONS, CONVERSIONS AND DATA ALTERATIONS IN A UNIFORM MATTER IN THE DATA WAREHOUSE

Source - http://www.test3.guru99.com/data-warehouse-architecture.html Sourcing, Acquisition, Clean-up and Transformation Tools (ETL) The data sourcing, transformation, and migration tools are used for performing all the conversions, summarizations, and all the changes needed to transform data into a unified format in the datawarehouse

Suspected Entry: 80% match

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THE SINGLE-TIER, TWO-TIER AND THREE-TIER ARCHITECTURE

Source - Another student's paper These methods are known as the Single-tier, Two-tier, and Three-tier architecture

Suspected Entry: 84% match

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THE MAJOR COMPONENTS OF DATA WAREHOUSE ARCHITECTURE INCLUDE

Source - Another student's paper The significant components of data warehouse architecture include

Suspected Entry: 73% match

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THE FUNCTIONS OF ETL TOOLS INCLUDE

Source - Another student's paper The functions of these tools include

Suspected Entry: 77% match

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THIS IS THE FOUNDATION OF THE DATA WAREHOUSING ENVIRONMENT WHICH WAS IMPLEMENTED ON THE RELATIONAL DATABASE MANAGEMENT SYSTEM (RDBMS) TECHNOLOGY

Source - https://www.cloudmoyo.com/blog/data-architecture/what-you-need-to-know- about-advanced-data-warehousing/ The database is a vital element of the data warehousing environment and is implemented on the RDBMS (Relational Database Management System) technology

Suspected Entry: 82% match

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THESE ARE FRONT-END TOOLS THAT HELP USERS TO INTERACT WITH THE DATA WAREHOUSE

Source - https://www.cloudmoyo.com/blog/data-architecture/what-you-need-to-know- about-advanced-data-warehousing/ The users use front-end tools to interact with the data warehouse

Suspected Entry: 73% match

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THE QUERY AND REPORTING TOOLS, ONLINE ANALYTICAL PROCESSING TOOLS, APPLICATION DEVELOPMENT TOOLS AND DATA MINING TOOLS (FAZAL ET AL, 2017)

Source - https://www.cloudmoyo.com/blog/data-architecture/what-you-need-to-know- about-advanced-data-warehousing/ These tools include query and reporting, application development, online analytical processing, and data mining tools, collectively known as access tools

Suspected Entry: 62% match

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· MULTIDIMENSIONAL DATABASES (MDDB) – THESE DATABASES ARE USED TO OVERRIDE ANY LIMITATIONS SET IN PLACE DUE TO THE RELATIONAL DATA MODEL

Source - Another student's paper Multidimensional databases might also be used to overcome limitations placed due to the relational data model

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METADATA PLAYS A CRUCIAL ROLE IN DATA WAREHOUSE ARCHITECTURE SINCE IT LAYS DOWN THE FOUNDATION, UTILIZATION, FEATURES AND VALUES OF DATA WAREHOUSE ARCHITECTURE

Source - Another student's paper Metadata plays a very crucial role in the data warehouse architecture as it defines the sources, values and data features of the data of the warehouse

Suspected Entry: 62% match

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ONE OF THE CURRENT KEY TRENDS IS THE PHYSICAL AND LOGICAL CONSOLIDATION WHICH HELPS IN MINIMIZING COSTS

Source - Another student's paper One of the trends in data warehousing help in reducing costs is physical and logical consolidation

Suspected Entry: 65% match

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THERE ARE VARIOUS TYPES OF DATA TRANSFORMATIONS NEEDED TO PREPARE DATA FOR A DATA WAREHOUSE

Source - Another student's paper Explain the major components of a data warehouse architecture, including the various forms of data transformations needed to prepare data for a data warehouse

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DESPITE ITS ADVANTAGES, BIG DATA IS PLACING HEAVY DEMANDS ON ORGANIZATIONS AND DATA MANAGEMENT TECHNOLOGIES

Source - Another student's paper In your view, what demands is big data placing on organizations and data management technology

Suspected Entry: 64% match

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PRICEWATERHOUSECOOPERS LLP IS AN EXAMPLE OF A COMPANY THAT HAS IMPLEMENTED GREEN COMPUTING STRATEGIES SUCCESSFULLY

Source - Another student's paper In your discussion, find an example of an organization that has already implemented IT green computing strategies successfully

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IN INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE, COMMUNICATIONS, AND BUSINESS ANALYTICS (PP

Source - Another student's paper In International Conference on Computational Intelligence, Communications, and Business Analytics (pp

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PATENT AND TRADEMARK OFFICE

Source - Another student's paper Patent and Trademark Office

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OUSSOUS, A., BENJELLOUN, F

Source - Another student's paper Oussous, A., Benjelloun, F

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Z., LAHCEN, A Z., Lahcen, A

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A., & BELFKIH, S

Source - Another student's paper A., & Belfkih, S

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BIG DATA TECHNOLOGIES

Source - Another student's paper Big Data technologies

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 30(4), 431-448

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IN 2017 IEEE 7TH INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC) (PP

Source - Another student's paper In 2017 IEEE 7th International Advance Computing Conference (IACC) (pp

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OKEWU, E., MISRA, S., MASKELIŪNAS, R., DAMAŠEVIČIUS, R., & FERNANDEZ- SANZ, L

Source - Another student's paper Okewu, E., Misra, S., Maskeliūnas, R., Damaševi�ius, R., & Fernandez-Sanz, L

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OPTIMIZING GREEN COMPUTING AWARENESS FOR ENVIRONMENTAL SUSTAINABILITY AND ECONOMIC SECURITY AS A STOCHASTIC OPTIMIZATION PROBLEM

Source - Another student's paper Optimizing green computing awareness for environmental sustainability and economic security as a stochastic optimization problem

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SUSTAINABILITY, 9(10), 1857

Source - Another student's paper Sustainability, 9(10), 1857

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(2010, OCTOBER 25)

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CHOPPED CARBON FOOTPRINT BY 20% IN TWO YEARS

Source - Another student's paper Chopped carbon footprint by 20% in two years

Suspected Entry: 86% match

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RETRIEVED APRIL 18, 2020, FROM HTTPS://WWW.COMPUTERWORLD.COM/ARTICLE/2549768/PRICEWATERHOUSEC OOPERS--CHOPPED-CARBON-FOOTPRINT-BY-20--IN-TWO-YEARS.HTML

Source - Another student's paper https://www.computerworld.com/article/2549768/pricewaterhousecoopers--chopped- carbon-footprint-by-20--in-two-years.html