This document discusses and compares various big data analytics software and tools. It begins with an abstract describing how companies now use big data analytics software to handle large amounts of data. The document then provides an executive summary of a research study analyzing how over 50 companies use big data analytics. The main body compares the features and benefits of various popular big data analytics software, including Apache Hadoop, CDH, Cassandra, Knime, Datawrapper, MongoDB, Lumify, HPCC, Storm, SAMOA, Talend, and RapidMiner. It also discusses analyzing data sets using the R programming language. The conclusion emphasizes how big data tools help transform large amounts of raw data into useful analytics and insights.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable and distributed processing of large datasets. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing vast amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. HDFS stores data reliably across machines in a Hadoop cluster and MapReduce processes data in parallel by breaking the job into smaller fragments of work executed across cluster nodes.
Big Data Tools: A Deep Dive into Essential ToolsFredReynolds2
Today, practically every firm uses big data to gain a competitive advantage in the market. With this in mind, freely available big data tools for analysis and processing are a cost-effective and beneficial choice for enterprises. Hadoop is the sector’s leading open-source initiative and big data tidal roller. Moreover, this is not the final chapter! Numerous other businesses pursue Hadoop’s free and open-source path.
The document discusses how big data analytics can transform the travel and transportation industry. It notes that these industries generate huge amounts of structured and unstructured data from various sources that can provide insights if analyzed properly. Hadoop is one tool that can help manage and process large datasets in parallel across clusters of servers. The document discusses how sensors in vehicles and infrastructure can provide real-time data on performance, maintenance needs, inventory levels, and more. This data, combined with analytics, can help optimize operations, improve customer experiences, predict issues, and increase efficiency across the transportation sector. It emphasizes that companies must develop data science skills and implement new technologies to fully leverage big data for strategic advantage.
The document discusses the evolution of Hadoop from version 1.0 to 2.0. It describes the core components of Hadoop 1.0 including HDFS, MapReduce, HBase, and Zookeeper. It outlines some limitations of Hadoop 1.0 related to scalability, availability, and resource utilization. Hadoop 2.0 introduced YARN to address these limitations by separating resource management from job scheduling. The document also introduces Apache Spark as a more user-friendly interface for Hadoop and compares it to Hadoop. It predicts that by 2020, Hadoop will be used for over 10% of data processing and a key part of many enterprise IT strategies and operations.
This document discusses common patterns of Apache Hadoop use in enterprises. It identifies three main patterns: 1) Hadoop as a data refinery to process large amounts of data and load it into existing data systems, 2) data exploration using Hadoop to directly analyze large amounts of raw data, and 3) application enrichment where data in Hadoop is used to customize applications and user experiences. The document provides examples of each pattern across different industries.
A short presentation on big data and the technologies available for managing Big Data. and it also contains a brief description of the Apache Hadoop Framework
Big data: Descoberta de conhecimento em ambientes de big data e computação na...Rio Info
This document discusses big data and intensive data processing. It defines big data and compares it to traditional analytics. It discusses technologies used for big data like Hadoop, MapReduce, and machine learning. It also discusses frameworks for analyzing big data like Apache Mahout and how Mahout is moving away from MapReduce to platforms like Apache Spark.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable and distributed processing of large datasets. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing vast amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. HDFS stores data reliably across machines in a Hadoop cluster and MapReduce processes data in parallel by breaking the job into smaller fragments of work executed across cluster nodes.
Big Data Tools: A Deep Dive into Essential ToolsFredReynolds2
Today, practically every firm uses big data to gain a competitive advantage in the market. With this in mind, freely available big data tools for analysis and processing are a cost-effective and beneficial choice for enterprises. Hadoop is the sector’s leading open-source initiative and big data tidal roller. Moreover, this is not the final chapter! Numerous other businesses pursue Hadoop’s free and open-source path.
The document discusses how big data analytics can transform the travel and transportation industry. It notes that these industries generate huge amounts of structured and unstructured data from various sources that can provide insights if analyzed properly. Hadoop is one tool that can help manage and process large datasets in parallel across clusters of servers. The document discusses how sensors in vehicles and infrastructure can provide real-time data on performance, maintenance needs, inventory levels, and more. This data, combined with analytics, can help optimize operations, improve customer experiences, predict issues, and increase efficiency across the transportation sector. It emphasizes that companies must develop data science skills and implement new technologies to fully leverage big data for strategic advantage.
The document discusses the evolution of Hadoop from version 1.0 to 2.0. It describes the core components of Hadoop 1.0 including HDFS, MapReduce, HBase, and Zookeeper. It outlines some limitations of Hadoop 1.0 related to scalability, availability, and resource utilization. Hadoop 2.0 introduced YARN to address these limitations by separating resource management from job scheduling. The document also introduces Apache Spark as a more user-friendly interface for Hadoop and compares it to Hadoop. It predicts that by 2020, Hadoop will be used for over 10% of data processing and a key part of many enterprise IT strategies and operations.
This document discusses common patterns of Apache Hadoop use in enterprises. It identifies three main patterns: 1) Hadoop as a data refinery to process large amounts of data and load it into existing data systems, 2) data exploration using Hadoop to directly analyze large amounts of raw data, and 3) application enrichment where data in Hadoop is used to customize applications and user experiences. The document provides examples of each pattern across different industries.
A short presentation on big data and the technologies available for managing Big Data. and it also contains a brief description of the Apache Hadoop Framework
Big data: Descoberta de conhecimento em ambientes de big data e computação na...Rio Info
This document discusses big data and intensive data processing. It defines big data and compares it to traditional analytics. It discusses technologies used for big data like Hadoop, MapReduce, and machine learning. It also discusses frameworks for analyzing big data like Apache Mahout and how Mahout is moving away from MapReduce to platforms like Apache Spark.
This document discusses big data technologies for enterprise analytics. It begins by defining big data and classifying big data technologies into three groups: Apache Hadoop, NoSQL databases, and extended RDBMS. It then provides examples of using different technologies for enterprise data warehouse extensions, website clickstream analysis, and real-time analytics. The document also discusses Hadoop distributions and Pentaho's support for big data and provides some big data success stories.
Social Media Market Trender with Dache Manager Using Hadoop and Visualization...IRJET Journal
This document proposes using Apache Hadoop and a data-aware cache framework called Dache to analyze large amounts of social media data from Twitter in real-time. The goals are to overcome limitations of existing analytics tools by leveraging Hadoop's ability to handle big data, improve processing speed through Dache caching, and provide visualizations of trends. Data would be grabbed from Twitter using Flume, stored in HDFS, converted to CSV format using MapReduce, analyzed using Dache to optimize Hadoop jobs, and visualized using tools like Tableau. The system aims to efficiently analyze social media trends at low cost using open source tools.
1. The document provides an overview of Hadoop and big data technologies, use cases, common components, challenges, and considerations for implementing a big data initiative.
2. Financial and IT analytics are currently the top planned use cases for big data technologies according to Forrester Research. Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers.
3. Organizations face challenges in implementing big data initiatives including skills gaps, data management issues, and high costs of hardware, personnel, and supporting new technologies. Careful planning is required to realize value from big data.
This document outlines the course content for a Big Data Analytics course. The course covers key concepts related to big data including Hadoop, MapReduce, HDFS, YARN, Pig, Hive, NoSQL databases and analytics tools. The 5 units cover introductions to big data and Hadoop, MapReduce and YARN, analyzing data with Pig and Hive, and NoSQL data management. Experiments related to big data are also listed.
This document discusses big data and Hadoop. It defines big data as high volume data that cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework that can store and process large data sets across clusters of commodity hardware. It has two main components - HDFS for storage and MapReduce for distributed processing. HDFS stores data across clusters and replicates it for fault tolerance, while MapReduce allows data to be mapped and reduced for analysis.
Hadoop is an open source platform for storing and processing large amounts of data across distributed systems. The document evaluates nine major Hadoop solutions based on 32 criteria. It finds that Hadoop is becoming widely adopted in enterprises due to its ability to cost-effectively manage both structured and unstructured data at large scales. While Hadoop itself is free to use, many vendors add proprietary features and support to their commercial distributions, creating competition in the growing Hadoop market. The evaluation identifies leaders and strong performers among the solutions for meeting enterprise data and analytics needs.
This document provides an introduction to big data, including its key characteristics of volume, velocity, and variety. It describes different types of big data technologies like Hadoop, MapReduce, HDFS, Hive, and Pig. Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers. MapReduce is a programming model used for processing large datasets in a distributed computing environment. HDFS provides a distributed file system for storing large datasets across clusters. Hive and Pig provide data querying and analysis capabilities for data stored in Hadoop clusters using SQL-like and scripting languages respectively.
Learn About Big Data and Hadoop The Most Significant ResourceAssignment Help
Data is now one of the most significant resources for businesses all around the world because of the digital revolution. However, the ability to gather, organize, process, and evaluate huge volumes of data has altered the way businesses function and arrive at educated decisions. Managing and gleaning information from the ever-expanding marine environments of information is impossible without Big Data and Hadoop. Both of which are at the vanguard of this data revolution.
If you have selected a programming language, and have difficulties writing the best assignment, get the assistance of assessment help experts to learn more about it. In this blog, we will look at the basics of Big Data and Hadoop and how they work. However, we will also explore the nature of Big Data. Also, its defining features, and the difficulties it provides. We'll also take a look at how Hadoop, an open-source platform, has become a frontrunner in the race to solve the challenges posed by Big Data. These fully appreciate the potential for change of Big Data and Hadoop for businesses across a wide range of sectors. It is necessary first to grasp the central position that they play in current data-driven decision-making.
Memory Management in BigData: A Perpective Viewijtsrd
The requirement to perform complicated statistic analysis of big data by institutions of engineering, scientific research, health care, commerce, banking and computer research is immense. However, the limitations of the widely used current desktop software like R, excel, minitab and spss gives a researcher limitation to deal with big data and big data analytic tools like IBM BigInsight, HP Vertica, SAP HANA & Pentaho come at an overpriced license. Apache Hadoop is an open source distributed computing framework that uses commodity hardware. With this project, I intend to collaborate Apache Hadoop and R software to develop an analytic platform that stores big data (using open source Apache Hadoop) and perform statistical analysis (using open source R software).Due to the limitations of vertical scaling of computer unit, data storage is handled by several machines and so analysis becomes distributed over all these machines. Apache Hadoop is what comes handy in this environment. To store massive quantities of data as required by researchers, we could use commodity hardware and perform analysis in distributed environment. Bhavna Bharti | Prof. Avinash Sharma"Memory Management in BigData: A Perpective View" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14436.pdf http://www.ijtsrd.com/engineering/computer-engineering/14436/memory-management-in-bigdata-a-perpective-view/bhavna-bharti
Big data refers to large datasets that cannot be processed using traditional computing techniques. Hadoop is an open-source framework that allows processing of big data across clustered, commodity hardware. It uses MapReduce as a programming model to parallelize processing and HDFS for reliable, distributed file storage. Hadoop distributes data across clusters, parallelizes processing, and can dynamically add or remove nodes, providing scalability, fault tolerance and high availability for large-scale data processing.
The business analytics marketplace is experiencing a challenge as classic BI tools meet up with evolving big data technologies, in particular Hadoop. We explore how IBM works to meet this challenge, providing a big picture perspective of their big data offerings around Hadoop, its open data platform and BigInsights.
The document provides a comparative analysis of Apache Hadoop and Apache Spark, two popular platforms for big data analytics. It discusses their key features, capabilities, strengths, limitations, use cases and provides a recommendation on selecting the right tool based on specific business needs and data processing requirements.
This document provides an introduction to big data and Hadoop. It defines big data as large, complex datasets that are difficult to manage and analyze using traditional methods. Hadoop is an open-source software framework used to store and process big data across distributed systems. It includes components like HDFS for scalable storage, MapReduce for parallel processing, Hive for data summarization, and Pig for creating MapReduce programs. The document discusses how Hadoop offers advantages like scalability, ease of use, cost-effectiveness and flexibility for big data processing. It provides examples of Hadoop's real-world use in healthcare, finance, retail and social media. The future of big data and Hadoop is also examined.
The document discusses big data and big data analytics in banking. It defines big data as large, complex datasets that are difficult to process and store using traditional databases. Sources of big data include social media, sensors, transportation services, online shopping, and mobile apps. Characteristics of big data include volume, velocity, and variety. Hadoop is presented as an open source framework for analyzing big data using HDFS for storage and MapReduce for processing. The benefits of big data analytics in banking include fraud detection, risk management, customer segmentation, churn analysis, and sentiment analysis to improve customer experience.
The document discusses cloud computing, big data, and big data analytics. It defines cloud computing as an internet-based technology that provides on-demand access to computing resources and data storage. Big data is described as large and complex datasets that are difficult to process using traditional databases due to their size, variety, and speed of growth. Hadoop is presented as an open-source framework for distributed storage and processing of big data using MapReduce. The document outlines the importance of analyzing big data using descriptive, diagnostic, predictive, and prescriptive analytics to gain insights.
I have collected information for the beginners to provide an overview of big data and hadoop which will help them to understand the basics and give them a Start-Up.
The document provides an introduction to the e-book which discusses how advanced analytics and big data are transforming businesses. It notes that the amount of data in the world is doubling every two years and analytics on this data is growing. New platforms and technologies now make it possible to economically process huge datasets and lower the cost and increase the speed of analysis.
The e-book contains essays from data analytics experts organized into five sections: business change, technology platforms, industry examples, research, and marketing. The technology platforms section focuses on tools that make advanced analytics affordable for organizations of all sizes. The introduction aims to provide insights into how analytics are evolving across different fields and industries through these expert perspectives.
C-BAG Big Data Meetup Chennai Oct.29-2014 Hortonworks and Concurrent on Casca...Hortonworks
The document discusses a Big Data Meetup organized by C-BAG (Chennai Big Data Analytic Group) on October 29, 2014 in Chennai. It provides details about two speakers, Dhruv Kumar from Concurrent Inc. and Vinay Shukla from Hortonworks, who will discuss reducing development time for production-grade Hadoop applications and Hortonworks' Hadoop platform respectively. The remainder of the document consists of presentation slides that cover topics including the modern data architecture with Hadoop, enterprise goals for data architecture, unlocking applications from new data types, and case studies.
6/5/2020 Originality Report
https://blackboard.nec.edu/webapps/mdb-sa-BB5b75a0e7334a9/originalityReport/ultra?attemptId=2ab444ee-13c8-49eb-86f5-578168754de8&course_id=_47058_1&includeDeleted=true&print=true 1/12
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SafeAssign Originality Report
Cloud Computing - 202040 - CRN174 - Pollak • Final Project
%26Total Score: Medium risk
Santhosh Muthyapu
Submission UUID: 43a98d6d-211b-6de9-9bf1-1de6250058fd
Total Number of Reports
3
Highest Match
43 %
Bibliography.docx
Average Match
26 %
Submitted on
06/05/20
11:46 AM EDT
Average Word Count
816
Highest: CLOUDMISCONFIGURATION.pptx
%43Attachment 1
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Student paper Student paper Student paper
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fiids
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View Originality Report - Old Design
Word Count: 193
Bibliography.docx
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2 Student paper 1 Student paper 5 Student paper
https://blackboard.nec.edu/webapps/mdb-sa-BB5b75a0e7334a9/originalityReport?attemptId=2ab444ee-13c8-49eb-86f5-578168754de8&course_id=_47058_1&includeDeleted=true&print=true&force=true
6/5/2020 Originality Report
https://blackboard.nec.edu/webapps/mdb-sa-BB5b75a0e7334a9/originalityReport/ultra?attemptId=2ab444ee-13c8-49eb-86f5-578168754de8&course_id=_47058_1&includeDeleted=true&print=true 2/12
Source Matches (6)
Student paper 83%
Student paper 92%
Student paper 94%
Student paper 81%
Bibliography: Baset, S., Suneja, S., Bila, N., Tuncer, O., & Isci, C. (2017). Usable declarative configuration specification and validation for applications, systems, and cloud.
Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference on Industrial Track - Middleware ’17. https://doi.org/10.1145/3154448.3154453
Berger, S., Garion, S., Moatti, Y., Naor, D., Pendarakis, D., Shulman-Peleg, A., Rao, J. R., Valdez, E., & Weinsberg, Y. (2016). Security intelligence for cloud management
infrastructures. IBM Journal of Research and Development, 60(4), 11:1–11:13. https://doi.org/10.1147/JRD.2016.2572462
Duncan, R. (2020). A multi-cloud world requires a multi-cloud security approach. Computer Fraud & Security, 2020(5), 11–12. https://doi.org/10.1016/S1361-3723(20)30052-X
January 15, S. P. on, & 2020. (2020, January 15). Cloud Misconfigurations: The Security Problem Coming From Inside IT. Security Boulevard.
https://securityboulevard.com/2020/01/cloud-misconfigurations-the-security-problem-coming-from-inside-it/ Torkura, K. A., Sukmana, M. I. H., Strauss, T., Graupner, H., Cheng, F.,
& Meinel, C. (2018, November 1). CSBAuditor: Proactive Security Risk Analysis for Cloud Storage Broker Systems. IEEE Xplore. https://doi.org/10.1109/NCA.2018.8548329
1
2 2
3
4 5
1
Student paper
Proceedings of the 18th ACM/IFIP/USENIX Middleware
Conference on Industrial Track - Middleware ’17.
https://doi.org/10.1145/3154448.3154453
Original source
Proceedings of the 18th ACM/IFIP/USENIX Middleware
Conference on Industrial Track - Middleware '17
doi:10.1145/3.
This document discusses big data technologies for enterprise analytics. It begins by defining big data and classifying big data technologies into three groups: Apache Hadoop, NoSQL databases, and extended RDBMS. It then provides examples of using different technologies for enterprise data warehouse extensions, website clickstream analysis, and real-time analytics. The document also discusses Hadoop distributions and Pentaho's support for big data and provides some big data success stories.
Social Media Market Trender with Dache Manager Using Hadoop and Visualization...IRJET Journal
This document proposes using Apache Hadoop and a data-aware cache framework called Dache to analyze large amounts of social media data from Twitter in real-time. The goals are to overcome limitations of existing analytics tools by leveraging Hadoop's ability to handle big data, improve processing speed through Dache caching, and provide visualizations of trends. Data would be grabbed from Twitter using Flume, stored in HDFS, converted to CSV format using MapReduce, analyzed using Dache to optimize Hadoop jobs, and visualized using tools like Tableau. The system aims to efficiently analyze social media trends at low cost using open source tools.
1. The document provides an overview of Hadoop and big data technologies, use cases, common components, challenges, and considerations for implementing a big data initiative.
2. Financial and IT analytics are currently the top planned use cases for big data technologies according to Forrester Research. Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers.
3. Organizations face challenges in implementing big data initiatives including skills gaps, data management issues, and high costs of hardware, personnel, and supporting new technologies. Careful planning is required to realize value from big data.
This document outlines the course content for a Big Data Analytics course. The course covers key concepts related to big data including Hadoop, MapReduce, HDFS, YARN, Pig, Hive, NoSQL databases and analytics tools. The 5 units cover introductions to big data and Hadoop, MapReduce and YARN, analyzing data with Pig and Hive, and NoSQL data management. Experiments related to big data are also listed.
This document discusses big data and Hadoop. It defines big data as high volume data that cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework that can store and process large data sets across clusters of commodity hardware. It has two main components - HDFS for storage and MapReduce for distributed processing. HDFS stores data across clusters and replicates it for fault tolerance, while MapReduce allows data to be mapped and reduced for analysis.
Hadoop is an open source platform for storing and processing large amounts of data across distributed systems. The document evaluates nine major Hadoop solutions based on 32 criteria. It finds that Hadoop is becoming widely adopted in enterprises due to its ability to cost-effectively manage both structured and unstructured data at large scales. While Hadoop itself is free to use, many vendors add proprietary features and support to their commercial distributions, creating competition in the growing Hadoop market. The evaluation identifies leaders and strong performers among the solutions for meeting enterprise data and analytics needs.
This document provides an introduction to big data, including its key characteristics of volume, velocity, and variety. It describes different types of big data technologies like Hadoop, MapReduce, HDFS, Hive, and Pig. Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers. MapReduce is a programming model used for processing large datasets in a distributed computing environment. HDFS provides a distributed file system for storing large datasets across clusters. Hive and Pig provide data querying and analysis capabilities for data stored in Hadoop clusters using SQL-like and scripting languages respectively.
Learn About Big Data and Hadoop The Most Significant ResourceAssignment Help
Data is now one of the most significant resources for businesses all around the world because of the digital revolution. However, the ability to gather, organize, process, and evaluate huge volumes of data has altered the way businesses function and arrive at educated decisions. Managing and gleaning information from the ever-expanding marine environments of information is impossible without Big Data and Hadoop. Both of which are at the vanguard of this data revolution.
If you have selected a programming language, and have difficulties writing the best assignment, get the assistance of assessment help experts to learn more about it. In this blog, we will look at the basics of Big Data and Hadoop and how they work. However, we will also explore the nature of Big Data. Also, its defining features, and the difficulties it provides. We'll also take a look at how Hadoop, an open-source platform, has become a frontrunner in the race to solve the challenges posed by Big Data. These fully appreciate the potential for change of Big Data and Hadoop for businesses across a wide range of sectors. It is necessary first to grasp the central position that they play in current data-driven decision-making.
Memory Management in BigData: A Perpective Viewijtsrd
The requirement to perform complicated statistic analysis of big data by institutions of engineering, scientific research, health care, commerce, banking and computer research is immense. However, the limitations of the widely used current desktop software like R, excel, minitab and spss gives a researcher limitation to deal with big data and big data analytic tools like IBM BigInsight, HP Vertica, SAP HANA & Pentaho come at an overpriced license. Apache Hadoop is an open source distributed computing framework that uses commodity hardware. With this project, I intend to collaborate Apache Hadoop and R software to develop an analytic platform that stores big data (using open source Apache Hadoop) and perform statistical analysis (using open source R software).Due to the limitations of vertical scaling of computer unit, data storage is handled by several machines and so analysis becomes distributed over all these machines. Apache Hadoop is what comes handy in this environment. To store massive quantities of data as required by researchers, we could use commodity hardware and perform analysis in distributed environment. Bhavna Bharti | Prof. Avinash Sharma"Memory Management in BigData: A Perpective View" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14436.pdf http://www.ijtsrd.com/engineering/computer-engineering/14436/memory-management-in-bigdata-a-perpective-view/bhavna-bharti
Big data refers to large datasets that cannot be processed using traditional computing techniques. Hadoop is an open-source framework that allows processing of big data across clustered, commodity hardware. It uses MapReduce as a programming model to parallelize processing and HDFS for reliable, distributed file storage. Hadoop distributes data across clusters, parallelizes processing, and can dynamically add or remove nodes, providing scalability, fault tolerance and high availability for large-scale data processing.
The business analytics marketplace is experiencing a challenge as classic BI tools meet up with evolving big data technologies, in particular Hadoop. We explore how IBM works to meet this challenge, providing a big picture perspective of their big data offerings around Hadoop, its open data platform and BigInsights.
The document provides a comparative analysis of Apache Hadoop and Apache Spark, two popular platforms for big data analytics. It discusses their key features, capabilities, strengths, limitations, use cases and provides a recommendation on selecting the right tool based on specific business needs and data processing requirements.
This document provides an introduction to big data and Hadoop. It defines big data as large, complex datasets that are difficult to manage and analyze using traditional methods. Hadoop is an open-source software framework used to store and process big data across distributed systems. It includes components like HDFS for scalable storage, MapReduce for parallel processing, Hive for data summarization, and Pig for creating MapReduce programs. The document discusses how Hadoop offers advantages like scalability, ease of use, cost-effectiveness and flexibility for big data processing. It provides examples of Hadoop's real-world use in healthcare, finance, retail and social media. The future of big data and Hadoop is also examined.
The document discusses big data and big data analytics in banking. It defines big data as large, complex datasets that are difficult to process and store using traditional databases. Sources of big data include social media, sensors, transportation services, online shopping, and mobile apps. Characteristics of big data include volume, velocity, and variety. Hadoop is presented as an open source framework for analyzing big data using HDFS for storage and MapReduce for processing. The benefits of big data analytics in banking include fraud detection, risk management, customer segmentation, churn analysis, and sentiment analysis to improve customer experience.
The document discusses cloud computing, big data, and big data analytics. It defines cloud computing as an internet-based technology that provides on-demand access to computing resources and data storage. Big data is described as large and complex datasets that are difficult to process using traditional databases due to their size, variety, and speed of growth. Hadoop is presented as an open-source framework for distributed storage and processing of big data using MapReduce. The document outlines the importance of analyzing big data using descriptive, diagnostic, predictive, and prescriptive analytics to gain insights.
I have collected information for the beginners to provide an overview of big data and hadoop which will help them to understand the basics and give them a Start-Up.
The document provides an introduction to the e-book which discusses how advanced analytics and big data are transforming businesses. It notes that the amount of data in the world is doubling every two years and analytics on this data is growing. New platforms and technologies now make it possible to economically process huge datasets and lower the cost and increase the speed of analysis.
The e-book contains essays from data analytics experts organized into five sections: business change, technology platforms, industry examples, research, and marketing. The technology platforms section focuses on tools that make advanced analytics affordable for organizations of all sizes. The introduction aims to provide insights into how analytics are evolving across different fields and industries through these expert perspectives.
C-BAG Big Data Meetup Chennai Oct.29-2014 Hortonworks and Concurrent on Casca...Hortonworks
The document discusses a Big Data Meetup organized by C-BAG (Chennai Big Data Analytic Group) on October 29, 2014 in Chennai. It provides details about two speakers, Dhruv Kumar from Concurrent Inc. and Vinay Shukla from Hortonworks, who will discuss reducing development time for production-grade Hadoop applications and Hortonworks' Hadoop platform respectively. The remainder of the document consists of presentation slides that cover topics including the modern data architecture with Hadoop, enterprise goals for data architecture, unlocking applications from new data types, and case studies.
Similar to 2Running Head BIG DATA PROCESSING OF SOFTWARE AND TOOLS2BIG.docx (20)
6/5/2020 Originality Report
https://blackboard.nec.edu/webapps/mdb-sa-BB5b75a0e7334a9/originalityReport/ultra?attemptId=2ab444ee-13c8-49eb-86f5-578168754de8&course_id=_47058_1&includeDeleted=true&print=true 1/12
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%1
SafeAssign Originality Report
Cloud Computing - 202040 - CRN174 - Pollak • Final Project
%26Total Score: Medium risk
Santhosh Muthyapu
Submission UUID: 43a98d6d-211b-6de9-9bf1-1de6250058fd
Total Number of Reports
3
Highest Match
43 %
Bibliography.docx
Average Match
26 %
Submitted on
06/05/20
11:46 AM EDT
Average Word Count
816
Highest: CLOUDMISCONFIGURATION.pptx
%43Attachment 1
Global database (3)
Student paper Student paper Student paper
Institutional database (1)
Student paper
Internet (1)
fiids
Top sources (3)
Excluded sources (0)
View Originality Report - Old Design
Word Count: 193
Bibliography.docx
1 5 3
2
4
2 Student paper 1 Student paper 5 Student paper
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6/5/2020 Originality Report
https://blackboard.nec.edu/webapps/mdb-sa-BB5b75a0e7334a9/originalityReport/ultra?attemptId=2ab444ee-13c8-49eb-86f5-578168754de8&course_id=_47058_1&includeDeleted=true&print=true 2/12
Source Matches (6)
Student paper 83%
Student paper 92%
Student paper 94%
Student paper 81%
Bibliography: Baset, S., Suneja, S., Bila, N., Tuncer, O., & Isci, C. (2017). Usable declarative configuration specification and validation for applications, systems, and cloud.
Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference on Industrial Track - Middleware ’17. https://doi.org/10.1145/3154448.3154453
Berger, S., Garion, S., Moatti, Y., Naor, D., Pendarakis, D., Shulman-Peleg, A., Rao, J. R., Valdez, E., & Weinsberg, Y. (2016). Security intelligence for cloud management
infrastructures. IBM Journal of Research and Development, 60(4), 11:1–11:13. https://doi.org/10.1147/JRD.2016.2572462
Duncan, R. (2020). A multi-cloud world requires a multi-cloud security approach. Computer Fraud & Security, 2020(5), 11–12. https://doi.org/10.1016/S1361-3723(20)30052-X
January 15, S. P. on, & 2020. (2020, January 15). Cloud Misconfigurations: The Security Problem Coming From Inside IT. Security Boulevard.
https://securityboulevard.com/2020/01/cloud-misconfigurations-the-security-problem-coming-from-inside-it/ Torkura, K. A., Sukmana, M. I. H., Strauss, T., Graupner, H., Cheng, F.,
& Meinel, C. (2018, November 1). CSBAuditor: Proactive Security Risk Analysis for Cloud Storage Broker Systems. IEEE Xplore. https://doi.org/10.1109/NCA.2018.8548329
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2 2
3
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Student paper
Proceedings of the 18th ACM/IFIP/USENIX Middleware
Conference on Industrial Track - Middleware ’17.
https://doi.org/10.1145/3154448.3154453
Original source
Proceedings of the 18th ACM/IFIP/USENIX Middleware
Conference on Industrial Track - Middleware '17
doi:10.1145/3.
61Identify the case study you selected. Explain whether the.docxBHANU281672
6:1
Identify the case study you selected. Explain whether the primary offender demonstrates features of a disciplined psychopath or an undisciplined psychopath. Provide examples to support your conclusion. Explain how these features differ from those displayed by individuals with antisocial personalities or narcissism. Explain the challenges a forensic psychology professional might have working with individuals with antisocial personality disorder or psychopathy.
Support your post with references to the Learning Resources and other academic sources.
Case Study #1
FPSY 6201 Psychological Aspects of Violent Crime Week 6 Case Studies
Paul is a 31-year-old man who was recently arrested for shooting a store manager during a robbery. He has a history of aggression and violating the law, including burglary, robbery, assault, and numerous drug charges. He is a high school dropout and has never been able to hold a job. When he first meets someone, he can come across as engaging, funny, and charming. He has been in numerous relationships; however, in those relationships he was emotionally detached and parasitic, as well as verbally and physically abusive. He has a volatile temperament and no sense of obligation or responsibility to anyone. His crimes often display a complete lack of empathy for his victims.
.
60CHAPTER THREEconsistent with the so-called performative app.docxBHANU281672
60 CHAPTER THREE
consistent with the so-called performative approach in social studies (K,apchan, 1995; Schechner, 2002; Warren 2001). According to this approach, to perform is to carry something into effect; hence, intercultural communication can be viewed as a process of carrying meaning, or cultural identity, as such, into effect.
When we speak of performativity or performance in intercultural communi cation, we must remember that "performance is the manifestation of performa tivity. This is to say, performativity refers to the reiterative process of becoming, while performance refers to the materialization of that process-the individual acts by human players in the world" (Warren, 2001: 106; boldface added)
The performative approach suggests that intercultural communication is per formed, like music. There are a variety of verbal and nonverbal elements (notes), with which people create various language games (music). Some games are quite simple (a routine greeting), while others are more complex (business negotia tions). In all cases, though, meanings are performed; that is, they are created and re-created in the process of interaction. People perform various activities repeat edly, and through repetition these movements become symbolic resources making up cultural identity. In intercultural interactions, to use Nietzsche's expression, "the deed is everything" (quoted in Butler, 1990: 25).
,11
I
"I
I
,,
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I,
Introducing the Performativity Principle
Looking at intercultural communication as performance, we will formulate our third principle of intercultural communication: the Perfo.rmativity Principle. There are three parts to this principle, and each deals with intercultural communication as creating and enacting meaning in the process of interaction. First, we will dis cuss the dramaturgy of intercultural performativity, or how people move from rules to roles. Next, we will present intercultural communication as a reiterative process. Finally, we will show the structure of intercultural communication as per formance. We will discuss each part separately and then formulate the Performa tivity Principle as a whole.
The Dramaturgy of Performativity:
From Rules to Roles
Communication as Drama. When people communicate with one another, they try to reach their goals by using various language means. Every act of com munication is a performance whereby people lace each other (either literally or in a mediated fashion, such as via the telephone or the Internet) and, as if on stage, present themselves-their very identities-dramatically to each other.
The theatrical or dramaturgical metaphor for communication does not sug
gest that people perform actions according to predetermined scripts or that per formances are insincere and deceitful. Nor does the theatrical metaphor suggest that people think of themselves as actors, always conscious of performing on stage. What the dramaturgical view of performativity states.
6 pagesThe following sections are in the final consulting .docxBHANU281672
6 pages
The following sections are in the final consulting report: Introduction to the Organization and Entry, Informal Data Collection, Microdiagnosis, and Contracting. Begin composing these sections in a document of 6–9 pages, not including the title page, table of contents, or reference list. Address the following elements:
Introduction to the Organization
Type of organization
Description of and information about the organization (e.g., review Web sites, press, and published documents)
Number of employees or key members
The opportunities that were initially identified or issues the organization faces
Entry, Informal Data Collection, Microdiagnosis, Contracting
Description of the issue or opportunity that served as a starting point for your work with the client
The process of diagnosing the problem and the agreed-upon objectives
The process you used to reach an agreement with the organization
.
600 words needed1. What do we mean by the New Public Administr.docxBHANU281672
600 words needed
1. What do we mean by the New Public Administration? Relatedly, but distictively,
2. what is meant by the New Public Management?
3. How are they related?
4. How has the advent of digital technology helped inspire new emphases on efficiency on the public sector?
.
6 peer responses due in 24 hours Each set of 2 responses wil.docxBHANU281672
6 peer responses due in 24 hours
Each set of 2 responses will have its own instructions.
Respond to at least two of your classmates
TAMMY’S POST:
The differences between mandatory, aspirational, principle and virtue ethics are paramount to ethical practice. The comprehension and implementation of the spheres of each allow for adhesion to policy and a sense of professionalism.
"General Principles, as opposed to Ethical Standards, are aspirational in nature. Their intent is to guide and inspire psychologists toward the very highest ethical ideals of the profession. General Principles, in contrast to Ethical Standards, do not represent obligations and should not form the basis for imposing sanctions. Relying upon General Principles for either of these reasons distorts both their meaning and purpose". (American Psychological Association, 2017)
The literature and the doctrine parameters cause uncertainty due to the conflictual environment and obligations. Questions of conflict about perceptual tension, as an example in
Professional ethics in interdisciplinary collaboratives: Zeal, paternalism, and mandated reporting
(2006) are between an attorney's zeal or client autonomy within the judicial system relationships in contrast to the Social Services scope of interests of humanity and social justice. Since the adaption of roles and environments tend to adjust, concern if responsibility sways in the contention of the differences. Social services render a larger and more diverse "moral community" and their sustainability stemming from virtue. The judicial system attends to the political policy and rules governing lawful adherence versus deviance. Another spectrum is mandatory reporting obligations which are said to be more profound when ethics pursue and in the collaboration still clash. An issue is an act of ethics versus the 'command' according to an agency (Anderson, Barenberg, & Tremblay, 2006. p. 663).
The differences between principle ethics and virtue ethics
The general principles of the APA are considered aspirational. Simultaneously, therapists, psychologists, and psychiatrists, and similar social services are mandated in the ethical codes of conduct to act in the betterment and safety of others, especially those deemed incompetent or incapacitated to do so.
The difference between principle ethics and virtue ethics splits by social normative and subjectivity. Social normative are more definite by culture but still universal and often mandatory. For instance, law-abiding and humane acts from avoiding reckless driving, speeding, or operating under the influence of obligatory care of the elderly, a child, or the disability are mandatory. Virtue ethics are less objective and more diverse to demographics and ethnography. Like integrity, it is a matter of right and wrong based on habits, behaviors rooted in one's upbringing. For example, seeing someone drop money instead of keeping it is returned to the person seen dropping it. Another.
6 page paper onWhat is second language acquisition and why is .docxBHANU281672
6 page paper on
What is second language acquisition and why is it important? The disadvantages of not learning a second language. The benefits of being bilingual and multilingual. When is the best time to learn a second language and why? Why is it important to learn a second language at a younger age rather than an older age?
3 reliable sources.
.
600 Words1) Specify some of the ways in which human resource m.docxBHANU281672
600 Words
1) Specify some of the ways in which human resource management differs significantly in the public sector from the private sector?
2) Specify some of the ways in which all public managers are involved in the areas human resource management?
3) In recent times, organizations have been devoting an increasing amount of the organization's resources toward human resources. This is particularly true in areas such as technical and social training, dispute resolution, and the like. Why do you think this is?
4) What are some of the ways that human resource managers operating in local government agencies (i.e. municipal, county, school districts, and so forth) are addressing the skills shortages caused by massive generational retirements in the public sector?
source
http://www.jstor.org.proxy.li.suu.edu:2048/stable/20447680
.
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Running head: Data MINING 1
Data MINING 8
Data Mining
Student: Avinash Kustagi
University of Cumberlands
Course Name: Business Intelligence
Course number: ITS-531
Professor: Dr. Abiodun Adeleke
05/29/2020
Data mining can be explained as the method to interpret information and hypothesis from large knowledge and data collections like databases or data warehouses.
Data mining popularity is increasing rapidly right now in the world. It is slowly becoming one of the most desired fields of work in the world right now. Data plays a
very big role in developing and shaping a business. It is because of Data mining that an organization comes to know more about what the market has demand for and
what their customers prefer and what they absolutely dislike. Data mining has proven to be extremely helpful in making valuable and important business decisions.
As described in the article” Business data mining — a machine learning perspective”, data mining has become an integral part of business development (Bose &
Mahapatra, 2001). Data mining has several applications in different fields of life. It is used in the field of finance, television industry, education, retail industry, and
telecommunication industry. Data mining is very valuable in the field of finance. Data mining help in data analysis to find a result in loan prediction. It gives an analysis
of the customer’s credit history and fraud detection (Valcheva, n.d.). It also assists in determining the previous money laundering trends and deduces a conclusion
about any unusual patterns in a credit history. It also assists in helping develop targeted marketing. In the field of finance, data mining and analysis helps in deducing
conclusion results from the previous trend in markets to determine what fiscal produc.
61520, 256 PMGlobal Innovation and Intellectual Property.docxBHANU281672
6/15/20, 2:56 PMGlobal Innovation and Intellectual Property
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12.1 Innovation as a Tool for Global Growth
LEARNING OBJECTIVE
Identify three types of innovation that can fuel global growth.
Over 93 percent of global executives rate innovation as a key driver of organic global growth. More importantly, research
shows that around 85 percent of a company's productivity gains are related to R&D and other innovation-related
investments.
Innovation is the commercialization of new invention. However, many innovations do not necessarily build on new
inventions. An invention is a new concept or product that derives from ideas or from scientific research. Innovation, on the
other hand, is the combination of new or existing ideas to create something desired by customers, viable in the
marketplace, and possible with technology (see Figure 12.1).
Figure 12.1Primary components of innovation
The inputs used to innovate could be new inventions or they could be old ideas. For example, Henry Ford didn't invent the
automobile. Karl Benz from Germany did. However, Ford combined scientific management concepts with the automobile
production process to build automobiles more efficiently (Figure 12.2). This innovation built on existing inventions to
usher in a new industry with the scale to meet demand.
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Figure 12.2Innovation in the auto industryCarl Benz of Mercedes Benz invented the automobile (left). Henry Ford of Ford
Motor Company innovated by combining ideas on assembly lines with car production (right).
Most global managers struggle to get people in their companies to innovate. So far, no one has created a formula or model
that reliably leads companies to increased innovation. Some management approaches are helpful, but none is perfect. As
Dr. Brian Junling Li, vice president of Alibaba Group, puts it, “Innovation doesn't come from organized plans. It comes
from our preparedness to deal with the uncertainty of the future.” To understand how global companies can effectively
deal with the uncertainties of the future, we first need to examine the different types of innovation in which companies can
invest.
Three Kinds of Innovation
Different types of innovation have different implications for company growth. Based on those implications, we can
organize innovations into three types: those that improve performance, those that enhance efficiency, and those that create
a market.
Performance-improving innovations replace old products with upgraded models. Often, the improvements in these models
are consistent worldwide. Performance-improving innovations keep a company growing because they provide .
6 Developing Strategic and Operational PlansIngram Publish.docxBHANU281672
6 Developing Strategic and Operational Plans
Ingram Publishing/Thinkstock
To mean well is nothing without to do well.
—Plautus
Trinummus
Learning Objectives
After reading this chapter, you should be able to do the following:
• Identify strategy concepts, including the components of organizational strategy; generic strategies; diversi-
fication, integration, and implementation strategies; and blue ocean strategy.
• Describe the use of strategies for large, multiunit organizations, including the use of the Boston Consult-
ing Group matrix to discern strategic implications from the analysis of existing operations, and the use of
product/market expansion strategies and diversification strategies for organizational growth.
• Discuss tactical issues that are relevant to pursuing participation in a managed-care network.
• Delineate the factors that influence the selection of a strategy by an organization.
• Explain how operational plans support strategic plans, and describe how operational plans are developed.
Section 6.1Strategy Concepts
Introduction
After developing a set of objectives for the time period covered by the strategic plan, the strat-
egy necessary for accomplishing those objectives must be formulated. First, planners must
design an overall strategy, and then define the operating details of that strategy as it relates
to providing services, promoting operations, determining locations, and increasing revenue
sources. This chapter introduces the concept of strategy, and describes strategy elements,
approaches to strategy development, and how operational plans support strategic plans.
6.1 Strategy Concepts
The word strategy has been used in a number of ways over the years and especially so in
the context of business. As we discussed in Chapter 2, strategy means leadership and may
be defined as the course of action taken by an organization to achieve its objectives. It is a
description first in general terms and then, in increasingly greater detail, of the activities
the organization will undertake to meet its goals and fulfill its ongoing mission. Strategy
is the catalyst or dynamic element of managing that enables a company to accomplish its
objectives.
Strategy development is both a science and an art, a product of both logic and creativity. The
scientific aspect deals with assembling and allocating the resources necessary to achieve
an organization’s objectives with emphasis on matching organizational strengths with envi-
ronmental opportunities, while working within cost and time constraints. The art of strat-
egy is mainly concerned with the effective use of resources, including motivating people to
make the strategy work, while being sensitive to the environmental forces that may affect
the organization’s performance and maintaining the ability to adapt the HCO to these chang-
ing conditions.
Components of Organizational Strategy
The focus of strategy varies by the planning level: the organizat.
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Running head: DEFENSE-IN-DEPTH AND AWARENESS TECHNIQUES
1
Running head: DEFENSE-IN-DEPTH AND AWARENESS TECHNIQUES
4
Defense-in-Depth and Awareness Techniques
Vikesh Desai
University of Cumberlands
Defense-in-Depth and Awareness Techniques
Awareness is one of the essential aspects in most of the organization, which requires a high magnitude to address comprehensively in all sections.
The depth in defense is more paramount to ensure that the organizations are comprehensively and effectively protect their system from the cyber-
attack activities. The most crucial strategy to deploy is two strategic systems that enhance the high degree of security instead of implementing one
security system. Various organizations have taken into account the defense in depth very crucial. Still, the organizations demanded to incorporate
their awareness through the provision of comprehensive educations to the employees and the workers in the organizations concerning the vital
measures that should be taken into account to curb security issues and develop holistic values taken into account. Most of the organizations are
known not to take the awareness as pressing issues that demand high consideration for the process of protecting and enhancing the security to be
tight. For any organization to protect their system from the cybercrime attack, they need to embrace situational awareness so that they can compre-
hensively develop strategic interventions that enable them to improve and assist in the detection of the up and coming threats as well as the
1
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strengthens that countermeasures the cybercrime activities. To me.
6.2 What protocols comprise TLS6.3 What is the difference.docxBHANU281672
6.2 What protocols comprise TLS?
6.3 What is the difference between a TLS connection and a TLS session?
6.4 List and briefly define the parameters that define a TLS session state.
6.5 List and briefly define the parameters that define a TLS session connection.
6.6 What services are provided by the TLS Record Protocol?
6.7 What steps are involved in the TLS Record Protocol transmission?
6.8 What is the purpose of HTTPS?
6.9 For what applications is SSH useful?
6.10 List and briefly define the SSH protocols.
.
6.2 What protocols comprise TLS6.3 What is the difference bet.docxBHANU281672
6.2 What protocols comprise TLS?
6.3 What is the difference between a TLS connection and a TLS session?
6.4 List and briefly define the parameters that define a TLS session state.
6.5 List and briefly define the parameters that define a TLS session connection.
6.6 What services are provided by the TLS Record Protocol?
6.7 What steps are involved in the TLS Record Protocol transmission?
6.8 What is the purpose of HTTPS?
6.9 For what applications is SSH useful?
6.10 List and briefly define the SSH protocols.
.
6-3 Discussion Making DecisionsDiscussion Topic Starts Jun 5, 2.docxBHANU281672
6-3 Discussion: Making Decisions
Discussion Topic
Starts Jun 5, 2021 11:59 PM
View
this interactive discussion scenario
and answer the question(s) posed at the end of the presentation.
A transcript for the video
Interactive Discussion Scenario
is available.
.
6 PEER RESPONSES DUE IN 24 HOURS.. EACH SET OF 2 HAS ITS OWN INSTRUC.docxBHANU281672
6 PEER RESPONSES DUE IN 24 HOURS.. EACH SET OF 2 HAS ITS OWN INSTRUCTIONS..
Guided Response:
Review your classmates’ posts and choose two posts to respond to.
If you choose a peer that selected the same student as you, address the following prompts:
· Discuss how your plans are similar and how they differ.
· Do you think you and your chosen peer have similar or different teaching styles? Explain.
· Do you think you and your chosen peer could team teach? Explain.
If you choose a peer that selected a different student than you, address the following prompts:
· Share what you appreciated about their plan and suggest at least one additional way to build a relationship with that student.
· Do you think you and your chosen peer have similar or different teaching styles? Explain.
· Do you think you and your chosen peer could team teach? Explain.
BRITTNEY’S POST:
I would work to have a relationship with Olivia just like I would work to have a relationship with any one of my students. I would start every morning by asking her how she is as she comes through door, ask her at some point throughout the day how she is doing, and ask how everyone’s day went at the end of the day. I would also make a point on Mondays to ask everyone what they did over the weekend and Fridays what everyone’s plans are for the weekend. Talking about a child’s day and/or weekend is a great way to build a connection with my students, as well as making it clear that they can talk to me if they need to, and speaking to them with respect, not like they are below you. In addition, it would help to talk about your weekend plans and your day as well. I think each of my strategies will make a positive impact on building a relationship with my students because each one has everything to do with them learning to trust, talk to, and respect me as well.
A few suggestions I would give Olivia’s parents to further build this bond is to suggest one on one time after school a couple times a week or a monthly recap with all the students. One on one time with Olivia would consist of Olivia being able to talk about whatever she wants with homework help and additional tutoring if needed. A monthly recap would consist of one hour a month where the student and their parents can come in for cookies and discuss anything they want. Such as, critiques on my teaching skills/methods, suggestions on material/activities, or just anything I can improve on as an educator. I think it is important to develop a relationship with every child because children do not want to learn from someone they do not like or who does not like them. Rita Pierson, who discusses how she, her parents, and maternal grandparents were educators and the value and importance of human connection. Pierson discusses how everyone is affected by a teacher or an adult at some point in their life. She then goes on to discuss how a teacher said “They don’t pay me to like the kids. They pay me to teach a lesson. The k.
6 peer responses due in 18 hours Each set of 2 responses will ha.docxBHANU281672
6 peer responses due in 18 hours
Each set of 2 responses will have its own instructions..
Guided Response:
Respond to one peer in this Discussion Forum. Read the challenging behavior scenario they have created and use the Developmental Discipline guidance strategy to problem solve. You must include the following in your response: child’s name, how you will approach the child, possible reminder or private sign, describe how you provide time and space, an example of self-talk that can help the child problem solve, and a choice you can offer the child. Additionally, can you use humor to defuse the situation? If so, how? If not, why?
My post:
Collaborative problem solving is one of the guidance strategies to address challenging behaviors. This strategy is based on the notion that a child does not just behave undesirably. There must be a reason for such behavior. Thus, understanding why the child is having a challenging behavior is the start towards addressing this behavior (Schaubman, Stetson, & Plog, 2011). The focus is on building skills like problem-solving, flexibility, and frustration tolerance rather than motivation the child to behave better. Surprisingly, children with challenging behaviors do not lack the will to behave in a desired manner. Simply, they do not have the skills necessary to behave in a desired manner. This information is vital to addressing challenging behaviors among children in the future. This would be achieved through identifying the challenging behaviors, skills needed to address the behaviors, and partnering with the child to build these needed skills (
Kaiser & Sklar Rasminsky, 2017
). This strategy would help address Olivia’s disruptive behavior, impulsivity and addressing peers negatively. Reward and punishment may not work on Olivia. Thus, Olivia needs to develop skills to address her behaviors (Schaubman et al., 2011). One of the skills to develop is social skills to enable her to control her impulsivity, connect with others, and relate with her peers positively. Apart from this strategy, time-out or time-away would address Olivia’s challenging behaviors. A scenario portraying Olivia’s challenging behavior is her inability to wait for her turn during a group activity. She is always blurting out answers before her turn arrives. How can this be solved?
References
Kaiser, B., & Sklar Rasminsky, J. (2017). Chapter 9: Guidance. In
Challenging behavior in young children: Understanding, preventing, and responding effectively
(4th ed.). Pearson Education.
Schaubman, A., Stetson, E., & Plog, A. (2011). Reducing teacher stress by implementing collaborative problem solving in a school setting.
School Social Work Journal
,
35
(2), 72-93.
BRITTNEY'S POST:
What did you learn about your chosen strategy and what information surprised you?
After reading Time Out or Time Away I have learned a couple of things, such as, not every teacher uses the timeout method and I also learned about the tim.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
2Running Head BIG DATA PROCESSING OF SOFTWARE AND TOOLS2BIG.docx
1. 2
Running Head: BIG DATA PROCESSING OF SOFTWARE
AND TOOLS
2
BIG DATA PROCESSING OF SOFTWARE AND TOOLS
University of the Cumberlands
Big Data Processing of Software and Tools
Data Science & Big Data Analytics
ITS 836-21 Group-1
Prof: Gamini Bulumulle
Date submitted: 02/23/2020
Submitted By:
Table of contents
Abstract..................................................................................
.....................................3
Executive
summary.................................................................................
....................4
3. ·
RapidMiner.............................................................................
..................11
Analyzing the data sets using R
language…………………………………………..12
Conclusion..............................................................................
....................................12
References..............................................................................
.....................................14
Abstract
The concept of big data analytics has been used over the years
and most companies have embraced the idea, to harness data
that is being used in their day to day company routines.
Companies can apply analytics and receive huge benefits from
it, back in the 1950s, companies were using big data in in terms
of spreadsheet analysis. This was a crude form of big data
analytics used to reveal small bits of data and data patterns.
Nowadays companies use big data analytics software to handle
huge chunks of data because it has a variety of benefits to
businesses. Some of the advantages of big data analytics
include: the speed in handling data, efficiency and productivity.
Many businesses prefer to accumulate huge data and later run
analytics of the data to be used for future references in the
company. Big data analytics ensures that businesses make the
right choices when it comes to handling data in the
organization. The ability of big data to work quicker and remain
efficient gives companies the advantage that they did not have
previously. This research paper will focus majorly on the big
data analytics software and their benefits to an organization.
Keywords: Big data, analysis, spreadsheet, efficiency,
organization
4. Executive summary
Big data analysis software gives organizations the ability to get
new ideas based the results of the analysis. It then encourages
more effective and efficient business ideas, increased benefits,
increased proficiency, and happy clients. In a research by Tom
Davenport more than fifty companies were analysed to see how
they employed the use of big data analysis software
(Chandarana, P., & Vijayalakshmi, M., 2014, April). The
conclusions that were made from the research was that there
were decreased costs when it comes to data analysis. The
companies that were using big data analytics software such as
Apache Hadoop and a cloud based analysis had reduced costs
when it comes to storage and analysis of data, these companies
also had an upper hand in making business decisions. The
research also proved that the companies that were making use of
bid data analysis software were quicker and had better dynamics
when analysing data. With in memory analysis and Hadoop,
combined with the ability to analyse new collections of data,
companies can be able to analyse data with a considerable speed
and come into conclusion based on the results of the analyses.
With the use of big data analytics software, there is an
increased ability to measure the needs of customers and know
what they need. Davenports research brings emphasis on bid
data analytics, there is an increased understanding of the needs
of the clients and better ways to address these issues.
Nowadays, many organizations widely use big data analytics to
make a big difference in the market. With open source big data
analytics software, the most valuable sections of the
organizations are secure, expenses are reduced. Hadoop is one
of the best big data analytics software that most business
5. currently use and many vendors currently employ the services
of Hadoop.
Hypothetically, a company may be faced with the need to do
market analysis in order to ascertain the trends in the market.
This scenario calls for the use of big data to help in the
marketing trend analysis. Big data software such as Hadoop,
Apache SAMOA, Casandra and Datawrapper can be used to
analyse the data and come up with an idea of what the market
looks like. All the software listed above play a role when it
comes to market trend analysis. For example, Hadoop will be
used to analyse huge data sets and help in giving out
information that relates to the future trends in that line of
business. Datawrapper will help the organization to perceive the
type of information to be analysed for market trends.
Big data analytics software
There are many things that come to the limelight when it comes
to the use of big data analytics in the modern world. Some of
the things that come to mind when it comes to big data include
what analysis software are to be used, how big the data indices
are, what is the normal data yield within an organization and so
on (Bhosale, H. S., & Gadekar, D. P., 2014). Big data analysis
6. can be broadly classified in the following ways: improvement
stages, advanced devices, as analysis instruments, for data
analytics and other analysis devices. Some of the software used
for big data analytics include the following:
Apache Hadoop
This software is used in big data analytics to analyse huge
chunks of data and grouped file systems. Hadoop forms a part of
big data and MapReduce model of programming. It is an open
source software that uses Java programming to give a cross
functional support and analysis of data. It is one of the widely
used analytics software. Research has it that more that fifty
Fortune companies use Hadoop in their data analysis systems.
Some of the noteworthy companies that use Hadoop include
Facebook, Intel, Amazon Web services, Hortonworks, IBM
statistics, Microsoft and many more.
The are many benefits that comes with using Hadoop and some
of them are listed below: the entire system of Hadoop has a
distributed file system which has the capacity to carry all kinds
of data such as pictures, XML, JSON, Hadoop is also very
valuable when it comes to R&D uses, the software also has an
advantage when it comes to access to data, the tool is highly
versatile and easily accessible when it comes to using a system
of computers. However, there are many disadvantages that come
with using Hadoop. Some of the downfalls include the issue of
repetition and a reduced functionality when it comes to I/O
activities.
CDH (Cloudera Distribution for Hadoop) software
CDH focuses on big merchantry matriculation arrangements of
that innovation. It is a thoroughly open-source and has a self-
ruling stage plagiarism that includes Apache Hadoop, Apache
Spark, Apache Impala, and some more. It permits you to gather,
process, oversee, find, model, and circulate widespread
information. Benefits of using CDH software: Comprehensive
dissemination, Cloudera Manager oversees the Hadoop group
well indeed, Easy usage, Less ramified organization, Upper
7. security and wardship. Disadvantages of using CDH software
include: Few muddling UI highlights like outlines on the CM
administration, Multiple prescribed methodologies for
establishment sound befuddling and, in any case, the Licensing
forfeit on a for every hub premise is truly costly.
Cassandra
Apache Cassandra is liberated from forfeit and open-source
sparse NoSQL DBMS ripened to oversee immense volumes of
information spread over various item servers, conveying upper
accessibility. It utilizes CQL (Cassandra Structure Language) to
cooperate with the database. A portion of the prominent
organizations utilizing Cassandra incorporates Accenture,
American Express, Facebook, General Electric, Honeywell,
Yahoo, and so on. Benefits of using big Apache Casandra
include: No single purpose of disappointment, Handles big data
rapidly, Log-organized capacity, Automated replication, Linear
tensility and Simple Ring diamond. Disadvantages of using
Casandra include: Requires some spare endeavours in
investigating and upkeep, Clustering could have been improved
and Row-level locking highlight isn't there.
Knime
KNIME represents Konstanz Information Miner which is an
open-source device that is used for Enterprise detailing,
incorporation, analytics, CRM, information mining, information
analysis, content mining, and merchantry insight. It underpins
Linux, OS X, and Windows working frameworks. It very well
may be considered as a decent option in unrelatedness to SAS.
A portion of the top organizations utilizing Knime incorporates
Comcast, Johnson and Johnson, Canadian Tire, and so forth.
Benefits of using KNIME include: Simple ETL activities, it
integrates very well with variegated innovations and dialects,
Rich numbering set, highly usable and sorted out work
processes, automates an unconfined deal of transmission work,
no steadiness issues and Easy to set up. Disadvantages of using
KNIME software for data analytics: Data dealing with a limit
can be improved, it occupies nearly the whole RAM and it
8. Could have permitted joining with diagram databases.
Datawrapper
Datawrapper is an open-source stage for information perception
that guides its clients to produce basic, word-for-word and
embeddable outlines rapidly. Its significant clients are
newsrooms that are spread everywhere throughout the world. A
portion of the names incorporates The Times, Fortune, Mother
Jones, Bloomberg, Twitter and so forth. Benefits of using
Datawrapper for big data analytics: The device is well tending
of. Works very well on all sorts of gadgets – versatile, tablet or
work area, fully responsive, Fast, Interactive, brings all the
diagrams in a single spot, Unconfined customization and fare
choices and It requires zero coding. Disadvantages: Limited
shading palettes
MongoDB
MongoDB is a NoSQL, report serried database written in C, C
#, and JavaScript. It is unviable to utilize and is an open-source
device that bolsters variegated working frameworks including
Windows Vista (and later forms), OS X (10.7 and later forms),
Linux, Solaris, and FreeBSD. Its primary highlights incorporate
Aggregation, Adhoc-inquiries, Uses BSON group, Shading,
Indexing, Replication, Server-side execution of JavaScript,
Schema less, Capped assortment, MongoDB the workbench
wardship (MMS), load adjusting and record stockpiling. A
portion of the significant clients utilizing MongoDB
incorporates Facebook, eBay, MetLife, Google, and so on.
Benefits of using MongoDB for big data analytics: Easy to
learn, Provides support for various innovations and stages, No
hiccups in establishment and support, Reliable and minimal
effort. Disadvantages of using MongoDB for big analytics:
Limited analytics and Slow for unrepeatable utilization cases.
Lumify
Lumify is a self-ruling and open-source instrument for big data
combination/reconciliation, analysis, and representation. Its
essential highlights incorporate full-content pursuit, 2D and 3D
orchestration perceptions, programmed formats, connect
9. analytics between diagram elements, combined with mapping
frameworks, geospatial analysis, sight and sound analytics, a
continuous coordinated effort through a lot of undertakings or
workspaces. Benefits of using Lumify for big data analytics:
Scalable, Secure, supported by a single-minded full-time urging
group, Supports the cloud-based condition. Functions admirably
with Amazon's AWS.
HPCC
HPCC represents High-Performance Computing Cluster. This is
a finished big data wattle over an uncommonly versatile
supercomputing stage. HPCC is likewise alluded to as DAS
(Data Analytics Supercomputer). This device was created by
LexisNexis Risk
Solution
s. This workings are written in C and an information-driven
programming language knowns as ECL (Enterprise Control
Language). It depends on Thor engineering that bolsters
information parallelism, pipeline parallelism, and framework
parallelism. It is an open-source device and is a decent
substitute for Hadoop and some other Big information stages.
Benefits of using HPCC for big data analytics: The engineering
depends on product processing groups which requite superior,
Parallel information preparing, Fast, incredible and profoundly
adaptable, supports superior online inquiry applications, and it
is Cost-powerful and exhaustive.
Storm
10. Apache Storm is a cross-stage, conveyed stream handling, and
shortcoming tolerant unvarying computational structure. It is
self-ruling and open-source. The designers of the tempest
incorporate Back type and Twitter. It is written in Clojure and
Java. Its engineering depends on tweaked gushes and darts to
portray wellsprings of data and controls to indulge cluster,
sparse handling of unbounded surges of information. Among
many, Groupon, Yahoo, Alibaba, and The Weather Channel are
a portion of the well-known organizations that utilization
Apache Storm. Benefits of using Apache storm for big data
analytics: Reliable at scale, very quick and shortcoming
tolerant, Guarantees the handling of information, it has
numerous utilization cases – ongoing analytics, log preparing,
ETL (Extract-Transform-Load), resulting calculation, conveyed
RPC, AI. Disadvantages of using Apache storm for big data
analytics: Difficult to learn and utilize, Difficulties with
investigating, and the use of Native Scheduler and Nimbus wilt
bottlenecks.
Apache SAMOA
SAMOA represents Scalable Advanced Massive Online
Analysis. It is an open-source stage for big data stream mining
and AI. It permits you to make sparse spilling AI (ML)
calculations and run them on numerous DSPEs (appropriated
stream preparing motors). Apache SAMOA's nearest elective is
a BigML device. Benefits of using Apache SAMOA for big data
11. analytics: Simple and witty to utilize, Fast and versatile, True
continuous spilling and it has a Write Once Run Anywhere
(WORA) engineering.
Talend
Talend Big information coordination items include: Open studio
for Big information: It goes under self-ruling and open-source
permit. Its parts and connectors are Hadoop and NoSQL. It
gives network perpetuate as it were, Big information stage: It
accompanies a client-based membership permit. Its parts and
connectors are MapReduce and Spark. It gives Web, email, and
telephone support and Real-time big data stage: It goes under a
client-based membership permit. Its parts and connectors
incorporate Spark gushing, Machine learning, and IoT. It gives
Web, email, and telephone support. Benefits of using Talend for
big data analytics: Streamlines ETL and ELT for Big
information, Accomplish the speed and size of sparkle,
accelerates your transition to continuous, handles numerous
information sources and It provides various connectors under
one rooftop, which thus will permit you to redo the wattle equal
to your needs. Disadvantages of using Talend for big data
analytics: Community valuables could have been something
more, could have an improved and simple to utilize interface
and Difficult to add a custom segment to the palette.
RapidMiner
RapidMiner is a cross-stage workings that offers a coordinated
12. domain for information science, AI and prescient analytics. It
goes under variegated licenses that offer little, medium and
huge restrictive versions just as a self-ruling release that takes
into consideration 1 legitimate processor and up to 10,000
information columns. Organizations like Hitachi, BMW,
Samsung, Airbus, and so along have been utilizing RapidMiner.
Benefits of using RapidMiner in big data software analytics:
Open-source Java centre, the repletion of wearing whet
information science instruments and calculations, the facility of
code-discretionary GUI, Integrates well with APIs and cloud,
Superb vendee assistance and specialized help. However, while
using RapidMiner, Online information administrations ought to
be improved.
Analyzing the data sets using R language
Data simulation is the crucial stage in processing raw data to
identify and trace certain patterns and generate the reports to
enhance the productivity. We have taken some sample data set
regarding a computer store, where we did some simulation to
show the different type of RAM available in the store and
simulated to hard disk prices.
13. Conclusion
The computerized age has made it simpler for experts to get to
the information that would permit you to improve your business
execution (Manikandan, S. G., & Ravi, S., 2014). In any case,
to use this data, you will require information examination
programming that can give you devices for information mining,
association, investigation, and perception. Besides, it ought to
be furnished with AI and propelled calculations to change your
crude information into significant bits of knowledge right away.
Along these lines, you can stay aware of business drifts, and
even discover approaches to additionally improve your general
tasks. In any case, there are a lot of components associated with
finding the privilege investigation apparatus for a specific
business. From looking at its exhibition to figuring how well it
plays with different frameworks, the exploration procedure can
be overpowering. In this way, to support you, we have
assembled the main items available and surveyed their
functionalities and ease of use. Big Data tools help us to store
and transform the huge data into analytics to track and
understand to predict certain patterns and gain the productivity
14. of the organization. Thusly, it will be simpler for you to decide
the most ideal information investigation stage for your tasks.
References
Bhosale, H. S., & Gadekar, D. P. (2014). A review paper on big
data and hadoop. International Journal of Scientific and
Research Publications, 4(10), 1-7.
Chandarana, P., & Vijayalakshmi, M. (2014, April). Big data
analytics frameworks. In 2014 International Conference on
Circuits, Systems, Communication and Information Technology
Applications (CSCITA) (pp. 430-434). IEEE.
Manikandan, S. G., & Ravi, S. (2014, October). Big data
analysis using Apache Hadoop. In 2014 International
Conference on IT Convergence and Security (ICITCS) (pp. 1-4).
IEEE.
Talia, D. (2013). Clouds for scalable big data
analytics. Computer, (5), 98-101.
Allen, G., Campbell, F., & Hu, Y. (2015). Comments on
15. “visualizing statistical models”: Visualizing modern statistical
methods for Big Data. Statistical Analysis And Data Mining:
The ASA Data Science Journal, 8(4), 226-228. doi:
10.1002/sam.11272
Griffith, D. (1993). Advanced spatial statistics for analysing
and visualizing geo-referenced data. International Journal Of
Geographical Information Systems, 7(2), 107-123. doi:
10.1080/02693799308901945
Create a PowerPoint presentation for the Sun Coast Remediation
research project to communicate the findings and suggest
recommendations. Please use the following format:
· Slide 1: Include a title slide.
· Slide 2: Organize the agenda.
· Slide 3: Introduce the project.
. Statement of the Problems
. Research Objectives
· Slide 4: Describe information gathered from the literature
review.
· Slide 5: Include research methodology, design, and methods.
. Research Methodology
. Research Design
16. . Research Methods
. Data collection
· Slide 6: Include research questions and hypotheses
· Slides 7 and 8: Explain your data analysis.
· Slides 9 and 10: Explain your findings.
· Slide 11: Explain recommendations including an explanation
of how research-based decision-making can directly affect
organizational practices.
· Slide 12 and 13: Reflect on your experience throughout the
course. Provide some of the things you learned and some of the
course’s takeaways that you can apply to your current or future
job.
· Slide 14: Include references for your sources.
Your PowerPoint must be a minimum of fourteen slides in
length (including the title slide and a reference slide).
Running head: INSERT TITLE HERE1
INSERT TITLE HERE11
17. Insert Title Here
Insert Your Name Here
Insert University Here
Table of Contents
Include the table of contents here. There is a tool for creating a
table of contents in the References tab of the Microsoft Word
tool bar at the top of the screen. Delete this before you begin.
Executive Summary
The executive summary will go here. The paragraphs are not
indented, and it should be formatted like an abstract. The
executive summary should be composed after the project is
complete. It will be the final step in the project. Delete this
before you begin.
Introduction
Note: The following introduction should remain in the research
project unchanged. Delete this note before you begin.
Senior leadership at Sun Coast has identified several areas for
18. concern that they believe could be solved using business
research methods. The previous director was tasked with
conducting research to help provide information to make
decisions about these issues. Although data were collected, the
project was never completed. Senior leadership is interested in
seeing the project through to fruition. The following is the
completion of that project and includes the statement of the
problems, literature review, research objectives, research
questions and hypotheses, research methodology, design, and
methods, data analysis, findings, and recommendations.
Statement of the Problems
Note: The following statement of the problems should remain in
the research project unchanged. Delete this note before you
begin.
Six business problems were identified:
Particulate Matter (PM)
There is a concern that job-site particle pollution is adversely
impacting employee health. Although respirators are required in
certain environments, PM varies in size depending on the
project and job site. PM that is between 10 and 2.5 microns can
float in the air for minutes to hours (e.g., asbestos, mold spores,
pollen, cement dust, fly ash), while PM that is less than 2.5
microns can float in the air for hours to weeks (e.g. bacteria,
viruses, oil smoke, smog, soot). Due to the smaller size of PM
that is less than 2.5 microns, it is potentially more harmful than
19. PM that is between 10 and 2.5 since the conditions are more
suitable for inhalation. PM that is less than 2.5 is also able to be
inhaled into the deeper regions of the lungs, potentially causing
more deleterious health effects. It would be helpful to
understand if there is a relationship between PM size and
employee health. PM air quality data have been collected from
103 job sites, which is recorded in microns. Data are also
available for average annual sick days per employee per job-
site.
Safety Training Effectiveness
Health and safety training is conducted for each new contract
that is awarded to Sun Coast. Data for training expenditures and
lost-time hours were collected from 223 contracts. It would be
valuable to know if training has been successful in reducing
lost-time hours and, if so, how to predict lost-time hours from
training expenditures.
Sound-Level Exposure
Sun Coast’s contracts generally involve work in noisy
environments due to a variety of heavy equipment being used
for both remediation and the clients’ ongoing operations on the
job sites. Standard ear-plugs are adequate to protect employee
hearing if the decibel levels are less than 120 decibels (dB). For
environments with noise levels exceeding 120 dB, more
advanced and expensive hearing protection is required, such as
earmuffs. Historical data have been collected from 1,503
20. contracts for several variables that are believed to contribute to
excessive dB levels. It would be important if these data could
be used to predict the dB levels of work environments before
placing employees on-site for future contracts. This would help
the safety department plan for procurement of appropriate ear
protection for employees.
New Employee Training
All new Sun Coast employees participate in general health and
safety training. The training program was revamped and
implemented six months ago. Upon completion of the training
programs, the employees are tested on their knowledge. Test
data are available for two groups: Group A employees who
participated in the prior training program and Group B
employees who participated in the revised training program. It
is necessary to know if the revised training program is more
effective than the prior training program.
Lead Exposure
Employees working on job sites to remediate lead must be
monitored. Lead levels in blood are measured as micrograms of
lead per deciliter of blood (μg/dL). A baseline blood test is
taken pre-exposure and postexposure at the conclusion of the
remediation. Data are available for 49 employees who recently
concluded a 2-year lead remediation project. It is necessary to
determine if blood lead levels have increased.
Return on Investment
21. Sun Coast offers four lines of service to their customers,
including air monitoring, soil remediation, water reclamation,
and health and safety training. Sun Coast would like to know if
each line of service offers the same return on investment.
Return on investment data are available for air monitoring, soil
remediation, water reclamation, and health and safety training
projects. If return on investment is not the same for all lines of
service, it would be helpful to know where differences exist.
Literature Review
After providing a brief introduction to this section, students
should include the literature review information here. Delete
this before you begin.
Research Objectives
After providing a brief introduction to this section, students
should include research objectives here. Delete this before you
begin.
RO1:
RO2:
RO3:
RO4:
RO5:
RO6:
Research Questions and Hypotheses
After providing a brief introduction to this section, students
should state the research questions and hypotheses. Delete this
23. After providing a brief introduction to this section, students
should detail the research design they have selected. Use the
following subheadings to include all required information.
Delete this before you begin.
Research Methodology
Research Design
Research Methods
Data Collection Methods
Sampling Design
Data Analysis Procedures
Data Analysis: Descriptive Statistics and Assumption Testing
After providing a brief introduction to this section, students
should provide the Excel Toolpak results of their descriptive
analyses. Use the following subheadings to include all required
information. Delete this before you begin.
Correlation: Descriptive Statistics and Assumption Testing
Simple Regression: Descriptive Statistics and Assumption
Testing
Multiple Regression: Descriptive Statistics and Assumption
Testing
Independent Samples t Test: Descriptive Statistics and
Assumption Testing
Dependent Samples (Paired-Samples) t Test: Descriptive
Statistics and Assumption Testing
24. ANOVA: Descriptive Statistics and Assumption Testing
Data Analysis: Hypothesis Testing
After providing a brief introduction to this section, students
should provide the Excel Toolpak results of their hypothesis
testing. Use the following subheadings to include all required
information. Delete this before you begin.
Correlation: Hypothesis Testing
Simple Regression: Hypothesis Testing
Multiple Regression: Hypothesis Testing
Independent Samples t Test: Hypothesis Testing
Dependent Samples (Paired Samples) t Test: Hypothesis Testing
ANOVA: Hypothesis Testing
Findings
After providing a brief introduction to this section, students
should discuss the findings in the context of Sun Coast’s
problems and the associated research objectives and questions.
Important Note: Students should refer to the information
presented in the Unit VII Study Guide and the Unit VII Syllabus
instructions to complete this section of the project. Restate each
research objective, and discuss them in the context of your
hypothesis testing results. The following are some things to
consider. What answers did the analysisprovide to your research
questions? What do those answers tell you? What are the
implications of those answers? Delete these statements before
you begin.
25. Example:
RO1: Determine if a person’s height is related to weight.
The results of the statistical testing showed that a person’s
height is related to their weight. It is a relatively strong and
positive relationship between height and weight. We would,
therefore, expect to see in our population taller people having a
greater weight relative to those of shorter people. This
determination suggests restrictions on industrial equipment
should be stated in maximum pounds allowed rather than
maximum number of people allowed.
RO2:
RO3:
RO4:
RO5:
RO6:
Recommendations
After providing a brief introduction to this section, students
should include recommendations here in paragraph form. This
section should be your professional thoughts based upon the
results of the hypothesis testing. You are the researcher, and
Sun Coast's leadership team is relying on you to make evidence-
based recommendations. Delete these statements before you
begin.
References
26. Include references here using hanging indentations, and delete
these statements and example reference.
Creswell, J. W., & Creswell, J. D. (2018). Research design:
Qualitative, quantitative, and mixed methods approaches (5th
ed.). Thousand Oaks, CA: Sage.