Big data plays a very crucial role in different fields of the modern world. Big data term is used for the data that is massive, varied and complex structure having the difficulties in collecting, storing, processing, analyzing and visualizing. Research which is to be processed in the direction of revealing the hidden patterns and the correlations between the different types of the data is named as Big Data Analytics or BDA. For the better decision making, for utilizing these useful information or for taking the better insights in the organizations or the company’s big data analytics is used. For this reason the analysis and execution of the big data implementation is needed. This paper aims to provide overview about the contents of the big data, its characteristics, big data analytics phases and the tools and techniques used during the different phases of the analysis. Sakshi Goel | Neeraj Kumar | Saharsh Gera "Big Data: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50641.pdf Paper URL: https://www.ijtsrd.com/computer-science/database/50641/big-data-a-review/sakshi-goel
An Comprehensive Study of Big Data Environment and its Challenges.ijceronline
Big Data is a data analysis methodology enabled by recent advances in technologies and Architecture. Big data is a massive volume of both structured and unstructured data, which is so large that it's difficult to process with traditional database and software techniques. This paper provides insight to Big data and discusses its nature, definition that include such features as Volume, Velocity, and Variety .This paper also provides insight to source of big data generation, tools available for processing large volume of variety of data, applications of big data and challenges involved in handling big data
Big data is the term for any gathering of information sets, so expensive and complex, that it gets to be hard to process for utilizing customary information handling applications. The difficulties incorporate investigation, catch, duration, inquiry, sharing, stockpiling, Exchange, perception, and protection infringement. To reduce spot business patterns, anticipate diseases, conflict etc., we require bigger data sets when compared with the smaller data sets. Enormous information is hard to work with utilizing most social database administration frameworks and desktop measurements and perception bundles, needing rather enormously parallel programming running on tens, hundreds, or even a large number of servers. In this paper there was an observation on Hadoop architecture, different tools used for big data and its security issues.
Big Data Summarization : Framework, Challenges and Possible Solutionsaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value. We then present background technology for big data summarization brings to us. The objective of this paper is to discuss the big data summarization framework, challenges and possible solutions as well as methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of open problems and future directions..
An Comprehensive Study of Big Data Environment and its Challenges.ijceronline
Big Data is a data analysis methodology enabled by recent advances in technologies and Architecture. Big data is a massive volume of both structured and unstructured data, which is so large that it's difficult to process with traditional database and software techniques. This paper provides insight to Big data and discusses its nature, definition that include such features as Volume, Velocity, and Variety .This paper also provides insight to source of big data generation, tools available for processing large volume of variety of data, applications of big data and challenges involved in handling big data
Big data is the term for any gathering of information sets, so expensive and complex, that it gets to be hard to process for utilizing customary information handling applications. The difficulties incorporate investigation, catch, duration, inquiry, sharing, stockpiling, Exchange, perception, and protection infringement. To reduce spot business patterns, anticipate diseases, conflict etc., we require bigger data sets when compared with the smaller data sets. Enormous information is hard to work with utilizing most social database administration frameworks and desktop measurements and perception bundles, needing rather enormously parallel programming running on tens, hundreds, or even a large number of servers. In this paper there was an observation on Hadoop architecture, different tools used for big data and its security issues.
Big Data Summarization : Framework, Challenges and Possible Solutionsaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value. We then present background technology for big data summarization brings to us. The objective of this paper is to discuss the big data summarization framework, challenges and possible solutions as well as methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of open problems and future directions..
Big Data Summarization : Framework, Challenges and Possible Solutionsaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value.
We then present background technology for big data summarization brings to us. The objective of this
paper is to discuss the big data summarization framework, challenges and possible solutions as well as
methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of open problems and future directions..
BIG DATA SUMMARIZATION: FRAMEWORK, CHALLENGES AND POSSIBLE SOLUTIONSaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value.We then present background technology for big data summarization brings to us. The objective of this paper is to discuss the big data summarization framework, challenges and possible solutions as well as methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of open problems and future directions..
Big Data Summarization : Framework, Challenges and Possible Solutionsaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value.
We then present background technology for big data summarization brings to us. The objective of this
paper is to discuss the big data summarization framework, challenges and possible solutions as well as
methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of
open problems and future directions..
We have concentrated on a range of strategies, methodologies, and distinct fields of research in this article, all of which are useful and relevant in the field of data mining technologies. As we all know, numerous multinational corporations and major corporations operate in various parts of the world. Each location of business may create significant amounts of data. Corporate decision-makers need access to all of these data sources in order to make strategic decisions.
DOCUMENT SELECTION USING MAPREDUCE Yenumula B Reddy and Desmond HillClaraZara1
Big data is used for structured, unstructured and semi-structured large volume of data which is difficult to manage and costly to store. Using explanatory analysis techniques to understand such raw data, carefully balance the benefits in terms of storage and retrieval techniques is an essential part of the Big Data. The research discusses the MapReduce issues, framework for MapReduce programming model and implementation. The paper includes the analysis of Big Data using MapReduce techniques and identifying a required document from a stream of documents. Identifying a required document is part of the security in a stream of documents in the cyber world. The document may be significant in business, medical, social, or terrorism.
Big data is used for structured, unstructured and semi-structured large volume of data which is difficult to
manage and costly to store. Using explanatory analysis techniques to understand such raw data, carefully
balance the benefits in terms of storage and retrieval techniques is an essential part of the Big Data. The
research discusses the Map Reduce issues, framework for Map Reduce programming model and
implementation. The paper includes the analysis of Big Data using Map Reduce techniques and identifying
a required document from a stream of documents. Identifying a required document is part of the security in
a stream of documents in the cyber world. The document may be significant in business, medical, social, or
terrorism.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
Big data is a prominent term which characterizes the improvement and availability of data in all three formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file includes text and multimedia contents. The primary objective of this big data concept is to describe the extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V” dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity. Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is described with the types of the data, Value which derives the business value and Veracity describes about the quality of the data and data understandability. Nowadays, big data has become unique and preferred research areas in the field of computer science. Many open research problems are available in big data and good solutions also been proposed by the researchers even though there is a need for development of many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper, a detailed study about big data, its basic concepts, history, applications, technique, research issues and tools are discussed.
Big data is a broad term for data sets so large or complex that tr.docxhartrobert670
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set.
Analysis of data sets can find new correlations, to "spot business trends, prevent diseases, combat crime and so on."[1] Scientists, practitioners of media and advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[2]connectomics, complex physics simulations,[3] and biological and environmental research.[4]
Data sets grow in size in part because they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers, and wireless sensor networks.[5]
HYPERLINK "http://en.wikipedia.org/wiki/Big_data" \l "cite_note-6" [6]
HYPERLINK "http://en.wikipedia.org/wiki/Big_data" \l "cite_note-7" [7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data were created;[9] The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.[10]
Work with big data is necessarily uncommon; most analysis is of "PC size" data, on a desktop PC or notebook[11] that can handle the available data set.
Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires "massively parallel software running on tens, hundreds, or even thousands of servers".[12] What is considered "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make Big Data a moving target. Thus, what is considered to be "Big" in one year will become ordinary in later years. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."[13]
Contents
· 1 Definition
· 2 Characteristics
· 3 Architecture
· 4 Technologies
· 5 Applications
· 5.1 Government
· 5.1.1 United States of America
· 5.1.2 India
· 5.1.3 United Kingdom
· 5.2 International development
· 5.3 Manufacturing
· 5.3.1 Cyber-Physical Models
· 5.4 Media
· 5.4.1 Internet of Things (IoT)
· 5.4.2 Technology
· 5.5 Private sector
· 5.5.1 Retail
· 5.5.2 Retail Banking
· 5.5.3 Real Estate
· 5.6 Science
· 5.6.1 Science and Resear ...
A REVIEW ON CLASSIFICATION OF DATA IMBALANCE USING BIGDATAIJMIT JOURNAL
Classification is one among the data mining function that assigns items in a collection to target categories
or collection of data to provide more accurate predictions and analysis. Classification using supervised
learning method aims to identify the category of the class to which a new data will fall under. With the
advancement of technology and increase in the generation of real-time data from various sources like
Internet, IoT and Social media it needs more processing and challenging. One such challenge in
processing is data imbalance. In the imbalanced dataset, majority classes dominate over minority classes
causing the machine learning classifiers to be more biased towards majority classes and also most
classification algorithm predicts all the test data with majority classes. In this paper, the author analysis
the data imbalance models using big data and classification algorithm
A Review on Classification of Data Imbalance using BigDataIJMIT JOURNAL
Classification is one among the data mining function that assigns items in a collection to target categories or collection of data to provide more accurate predictions and analysis. Classification using supervised learning method aims to identify the category of the class to which a new data will fall under. With the advancement of technology and increase in the generation of real-time data from various sources like Internet, IoT and Social media it needs more processing and challenging. One such challenge in processing is data imbalance. In the imbalanced dataset, majority classes dominate over minority classes causing the machine learning classifiers to be more biased towards majority classes and also most classification algorithm predicts all the test data with majority classes. In this paper, the author analysis the data imbalance models using big data and classification algorithm.
‘Six Sigma Technique’ A Journey Through its Implementationijtsrd
The manufacturing industries all over the world are facing tough challenges for growth, development and sustainability in today’s competitive environment. They have to achieve apex position by adapting with the global competitive environment by delivering goods and services at low cost, prime quality and better price to increase wealth and consumer satisfaction. Cost Management ensures profit, growth and sustainability of the business with implementation of Continuous Improvement Technique like Six Sigma. This leads to optimize Business performance. The method drives for customer satisfaction, low variation, reduction in waste and cycle time resulting into a competitive advantage over other industries which did not implement it. The main objective of this paper ‘Six Sigma Technique A Journey Through Its Implementation’ is to conceptualize the effectiveness of Six Sigma Technique through the journey of its implementation. Aditi Sunilkumar Ghosalkar "‘Six Sigma Technique’: A Journey Through its Implementation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64546.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64546/‘six-sigma-technique’-a-journey-through-its-implementation/aditi-sunilkumar-ghosalkar
Edge Computing in Space Enhancing Data Processing and Communication for Space...ijtsrd
Edge computing, a paradigm that involves processing data closer to its source, has gained significant attention for its potential to revolutionize data processing and communication in space missions. With the increasing complexity and data volume generated by modern space missions, traditional centralized computing approaches face challenges related to latency, bandwidth, and security. Edge computing in space, involving on board processing and analysis of data, offers promising solutions to these challenges. This paper explores the concept of edge computing in space, its benefits, applications, and future prospects in enhancing space missions. Manish Verma "Edge Computing in Space: Enhancing Data Processing and Communication for Space Missions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64541.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64541/edge-computing-in-space-enhancing-data-processing-and-communication-for-space-missions/manish-verma
Big Data Summarization : Framework, Challenges and Possible Solutionsaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value.
We then present background technology for big data summarization brings to us. The objective of this
paper is to discuss the big data summarization framework, challenges and possible solutions as well as
methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of open problems and future directions..
BIG DATA SUMMARIZATION: FRAMEWORK, CHALLENGES AND POSSIBLE SOLUTIONSaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value.We then present background technology for big data summarization brings to us. The objective of this paper is to discuss the big data summarization framework, challenges and possible solutions as well as methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of open problems and future directions..
Big Data Summarization : Framework, Challenges and Possible Solutionsaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value.
We then present background technology for big data summarization brings to us. The objective of this
paper is to discuss the big data summarization framework, challenges and possible solutions as well as
methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of
open problems and future directions..
We have concentrated on a range of strategies, methodologies, and distinct fields of research in this article, all of which are useful and relevant in the field of data mining technologies. As we all know, numerous multinational corporations and major corporations operate in various parts of the world. Each location of business may create significant amounts of data. Corporate decision-makers need access to all of these data sources in order to make strategic decisions.
DOCUMENT SELECTION USING MAPREDUCE Yenumula B Reddy and Desmond HillClaraZara1
Big data is used for structured, unstructured and semi-structured large volume of data which is difficult to manage and costly to store. Using explanatory analysis techniques to understand such raw data, carefully balance the benefits in terms of storage and retrieval techniques is an essential part of the Big Data. The research discusses the MapReduce issues, framework for MapReduce programming model and implementation. The paper includes the analysis of Big Data using MapReduce techniques and identifying a required document from a stream of documents. Identifying a required document is part of the security in a stream of documents in the cyber world. The document may be significant in business, medical, social, or terrorism.
Big data is used for structured, unstructured and semi-structured large volume of data which is difficult to
manage and costly to store. Using explanatory analysis techniques to understand such raw data, carefully
balance the benefits in terms of storage and retrieval techniques is an essential part of the Big Data. The
research discusses the Map Reduce issues, framework for Map Reduce programming model and
implementation. The paper includes the analysis of Big Data using Map Reduce techniques and identifying
a required document from a stream of documents. Identifying a required document is part of the security in
a stream of documents in the cyber world. The document may be significant in business, medical, social, or
terrorism.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
Big data is a prominent term which characterizes the improvement and availability of data in all three formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file includes text and multimedia contents. The primary objective of this big data concept is to describe the extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V” dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity. Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is described with the types of the data, Value which derives the business value and Veracity describes about the quality of the data and data understandability. Nowadays, big data has become unique and preferred research areas in the field of computer science. Many open research problems are available in big data and good solutions also been proposed by the researchers even though there is a need for development of many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper, a detailed study about big data, its basic concepts, history, applications, technique, research issues and tools are discussed.
Big data is a broad term for data sets so large or complex that tr.docxhartrobert670
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set.
Analysis of data sets can find new correlations, to "spot business trends, prevent diseases, combat crime and so on."[1] Scientists, practitioners of media and advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[2]connectomics, complex physics simulations,[3] and biological and environmental research.[4]
Data sets grow in size in part because they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers, and wireless sensor networks.[5]
HYPERLINK "http://en.wikipedia.org/wiki/Big_data" \l "cite_note-6" [6]
HYPERLINK "http://en.wikipedia.org/wiki/Big_data" \l "cite_note-7" [7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data were created;[9] The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.[10]
Work with big data is necessarily uncommon; most analysis is of "PC size" data, on a desktop PC or notebook[11] that can handle the available data set.
Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires "massively parallel software running on tens, hundreds, or even thousands of servers".[12] What is considered "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make Big Data a moving target. Thus, what is considered to be "Big" in one year will become ordinary in later years. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."[13]
Contents
· 1 Definition
· 2 Characteristics
· 3 Architecture
· 4 Technologies
· 5 Applications
· 5.1 Government
· 5.1.1 United States of America
· 5.1.2 India
· 5.1.3 United Kingdom
· 5.2 International development
· 5.3 Manufacturing
· 5.3.1 Cyber-Physical Models
· 5.4 Media
· 5.4.1 Internet of Things (IoT)
· 5.4.2 Technology
· 5.5 Private sector
· 5.5.1 Retail
· 5.5.2 Retail Banking
· 5.5.3 Real Estate
· 5.6 Science
· 5.6.1 Science and Resear ...
A REVIEW ON CLASSIFICATION OF DATA IMBALANCE USING BIGDATAIJMIT JOURNAL
Classification is one among the data mining function that assigns items in a collection to target categories
or collection of data to provide more accurate predictions and analysis. Classification using supervised
learning method aims to identify the category of the class to which a new data will fall under. With the
advancement of technology and increase in the generation of real-time data from various sources like
Internet, IoT and Social media it needs more processing and challenging. One such challenge in
processing is data imbalance. In the imbalanced dataset, majority classes dominate over minority classes
causing the machine learning classifiers to be more biased towards majority classes and also most
classification algorithm predicts all the test data with majority classes. In this paper, the author analysis
the data imbalance models using big data and classification algorithm
A Review on Classification of Data Imbalance using BigDataIJMIT JOURNAL
Classification is one among the data mining function that assigns items in a collection to target categories or collection of data to provide more accurate predictions and analysis. Classification using supervised learning method aims to identify the category of the class to which a new data will fall under. With the advancement of technology and increase in the generation of real-time data from various sources like Internet, IoT and Social media it needs more processing and challenging. One such challenge in processing is data imbalance. In the imbalanced dataset, majority classes dominate over minority classes causing the machine learning classifiers to be more biased towards majority classes and also most classification algorithm predicts all the test data with majority classes. In this paper, the author analysis the data imbalance models using big data and classification algorithm.
‘Six Sigma Technique’ A Journey Through its Implementationijtsrd
The manufacturing industries all over the world are facing tough challenges for growth, development and sustainability in today’s competitive environment. They have to achieve apex position by adapting with the global competitive environment by delivering goods and services at low cost, prime quality and better price to increase wealth and consumer satisfaction. Cost Management ensures profit, growth and sustainability of the business with implementation of Continuous Improvement Technique like Six Sigma. This leads to optimize Business performance. The method drives for customer satisfaction, low variation, reduction in waste and cycle time resulting into a competitive advantage over other industries which did not implement it. The main objective of this paper ‘Six Sigma Technique A Journey Through Its Implementation’ is to conceptualize the effectiveness of Six Sigma Technique through the journey of its implementation. Aditi Sunilkumar Ghosalkar "‘Six Sigma Technique’: A Journey Through its Implementation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64546.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64546/‘six-sigma-technique’-a-journey-through-its-implementation/aditi-sunilkumar-ghosalkar
Edge Computing in Space Enhancing Data Processing and Communication for Space...ijtsrd
Edge computing, a paradigm that involves processing data closer to its source, has gained significant attention for its potential to revolutionize data processing and communication in space missions. With the increasing complexity and data volume generated by modern space missions, traditional centralized computing approaches face challenges related to latency, bandwidth, and security. Edge computing in space, involving on board processing and analysis of data, offers promising solutions to these challenges. This paper explores the concept of edge computing in space, its benefits, applications, and future prospects in enhancing space missions. Manish Verma "Edge Computing in Space: Enhancing Data Processing and Communication for Space Missions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64541.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64541/edge-computing-in-space-enhancing-data-processing-and-communication-for-space-missions/manish-verma
Dynamics of Communal Politics in 21st Century India Challenges and Prospectsijtsrd
Communal politics in India has evolved through centuries, weaving a complex tapestry shaped by historical legacies, colonial influences, and contemporary socio political transformations. This research comprehensively examines the dynamics of communal politics in 21st century India, emphasizing its historical roots, socio political dynamics, economic implications, challenges, and prospects for mitigation. The historical perspective unravels the intricate interplay of religious identities and power dynamics from ancient civilizations to the impact of colonial rule, providing insights into the evolution of communalism. The socio political dynamics section delves into the contemporary manifestations, exploring the roles of identity politics, socio economic disparities, and globalization. The economic implications section highlights how communal politics intersects with economic issues, perpetuating disparities and influencing resource allocation. Challenges posed by communal politics are scrutinized, revealing multifaceted issues ranging from social fragmentation to threats against democratic values. The prospects for mitigation present a multifaceted approach, incorporating policy interventions, community engagement, and educational initiatives. The paper conducts a comparative analysis with international examples, identifying common patterns such as identity politics and economic disparities. It also examines unique challenges, emphasizing Indias diverse religious landscape, historical legacy, and secular framework. Lessons for effective strategies are drawn from international experiences, offering insights into inclusive policies, interfaith dialogue, media regulation, and global cooperation. By scrutinizing historical epochs, contemporary dynamics, economic implications, and international comparisons, this research provides a comprehensive understanding of communal politics in India. The proposed strategies for mitigation underscore the importance of a holistic approach to foster social harmony, inclusivity, and democratic values. Rose Hossain "Dynamics of Communal Politics in 21st Century India: Challenges and Prospects" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64528.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/history/64528/dynamics-of-communal-politics-in-21st-century-india-challenges-and-prospects/rose-hossain
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...ijtsrd
Background and Objective Telehealth has become a well known tool for the delivery of health care in Saudi Arabia, and the perspective and knowledge of healthcare providers are influential in the implementation, adoption and advancement of the method. This systematic review was conducted to examine the current literature base regarding telehealth and the related healthcare professional perspective and knowledge in the Kingdom of Saudi Arabia. Materials and Methods This systematic review was conducted by searching 7 databases including, MEDLINE, CINHAL, Web of Science, Scopus, PubMed, PsycINFO, and ProQuest Central. Studies on healthcare practitioners telehealth knowledge and perspectives published in English in Saudi Arabia from 2000 to 2023 were included. Boland directed this comprehensive review. The researchers examined each connected study using the AXIS tool, which evaluates cross sectional systematic reviews. Narrative synthesis was used to summarise and convey the data. Results Out of 1840 search results, 10 studies were included. Positive outlook and limited knowledge among providers were seen across trials. Healthcare professionals like telehealth for its ability to improve quality, access, and delivery, save time and money, and be successful. Age, gender, occupation, and work experience also affect health workers knowledge. In Saudi Arabia, healthcare professionals face inadequate expert assistance, patient privacy, internet connection concerns, lack of training courses, lack of telehealth understanding, and high costs while performing telemedicine. Conclusions Healthcare practitioners telehealth perceptions and knowledge were examined in this systematic study. Its collection of concerned experts different personal attitudes and expertise would help enhance telehealths implementation in Saudi Arabia, develop its healthcare delivery alternative, and eliminate frequent problems. Badriah Mousa I Mulayhi | Dr. Jomin George | Judy Jenkins "Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in Saudi Arabia: A Systematic Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64535.pdf Paper Url: https://www.ijtsrd.com/medicine/other/64535/assess-perspective-and-knowledge-of-healthcare-providers-towards-elehealth-in-saudi-arabia-a-systematic-review/badriah-mousa-i-mulayhi
The Impact of Digital Media on the Decentralization of Power and the Erosion ...ijtsrd
The impact of digital media on the distribution of power and the weakening of traditional gatekeepers has gained considerable attention in recent years. The adoption of digital technologies and the internet has resulted in declining influence and power for traditional gatekeepers such as publishing houses and news organizations. Simultaneously, digital media has facilitated the emergence of new voices and players in the media industry. Digital medias impact on power decentralization and gatekeeper erosion is visible in several ways. One significant aspect is the democratization of information, which enables anyone with an internet connection to publish and share content globally, leading to citizen journalism and bypassing traditional gatekeepers. Another aspect is the disruption of conventional media industry business models, as traditional organizations struggle to adjust to the decrease in advertising revenue and the rise of digital platforms. Alternative business models, such as subscription models and crowdfunding, have become more prevalent, leading to the emergence of new players. Overall, the impact of digital media on the distribution of power and the weakening of traditional gatekeepers has brought about significant changes in the media landscape and the way information is shared. Further research is required to fully comprehend the implications of these changes and their impact on society. Dr. Kusum Lata "The Impact of Digital Media on the Decentralization of Power and the Erosion of Traditional Gatekeepers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64544.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64544/the-impact-of-digital-media-on-the-decentralization-of-power-and-the-erosion-of-traditional-gatekeepers/dr-kusum-lata
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...ijtsrd
This research investigates the nexus between online discussions on Dr. B.R. Ambedkars ideals and their impact on social inclusion among college students in Gurugram, Haryana. Surveying 240 students from 12 government colleges, findings indicate that 65 actively engage in online discussions, with 80 demonstrating moderate to high awareness of Ambedkars ideals. Statistically significant correlations reveal that higher online engagement correlates with increased awareness p 0.05 and perceived social inclusion. Variations across colleges and a notable effect of college type on perceived social inclusion highlight the influence of contextual factors. Furthermore, the intersectional analysis underscores nuanced differences based on gender, caste, and socio economic status. Dr. Kusum Lata "Online Voices, Offline Impact: Ambedkar's Ideals and Socio-Political Inclusion - A Study of Gurugram District" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64543.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64543/online-voices-offline-impact-ambedkars-ideals-and-sociopolitical-inclusion--a-study-of-gurugram-district/dr-kusum-lata
Problems and Challenges of Agro Entreprenurship A Studyijtsrd
Noting calls for contextualizing Agro entrepreneurs problems and challenges of the agro entrepreneurs and for greater attention to the Role of entrepreneurs in agro entrepreneurship research, we conduct a systematic literature review of extent research in agriculture entrepreneurship to overcome the study objectives of complications of agro entrepreneurs through various factors, Development of agriculture products is a key factor for the overall economic growth of agro entrepreneurs Agro Entrepreneurs produces firsthand large scale employment, utilizes the labor and natural resources, This research outlines the problems of Weather and Soil Erosions, Market price fluctuation, stimulates labor cost problems, reduces concentration of Price volatility, Dependency on Intermediaries, induces Limited Bargaining Power, and Storage and Transportation Costs. This paper mainly devoted to highlight Problems and challenges faced for the sustainable of Agro Entrepreneurs in India. Vinay Prasad B "Problems and Challenges of Agro Entreprenurship - A Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64540.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64540/problems-and-challenges-of-agro-entreprenurship--a-study/vinay-prasad-b
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...ijtsrd
Disclosure is a process through which a business enterprise communicates with external parties. A corporate disclosure is communication of financial and non financial information of the activities of a business enterprise to the interested entities. Corporate disclosure is done through publishing annual reports. So corporate disclosure through annual reports plays a vital role in the life of all the companies and provides valuable information to investors. The basic objectives of corporate disclosure is to give a true and fair view of companies to the parties related either directly or indirectly like owner, government, creditors, shareholders etc. in the companies act, provisions have been made about mandatory and voluntary disclosure. The IT sector in India is rapidly growing, the trend to invest in the IT sector is rising and employment opportunities in IT sectors are also increasing. Therefore the IT sector is expected to have fair, full and adequate disclosure of all information. Unfair and incomplete disclosure may adversely affect the entire economy. A research study on disclosure practices of IT companies could play an important role in this regard. Hence, the present research study has been done to study and review comparative analysis of total corporate disclosure of selected IT companies of India and to put forward overall findings and suggestions with a view to increase disclosure score of these companies. The researcher hopes that the present research study will be helpful to all selected Companies for improving level of corporate disclosure through annual reports as well as the government, creditors, investors, all business organizations and upcoming researcher for comparative analyses of level of corporate disclosure with special reference to selected IT companies. Dr. Vaibhavi D. Thaker "Comparative Analysis of Total Corporate Disclosure of Selected IT Companies of India" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64539.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64539/comparative-analysis-of-total-corporate-disclosure-of-selected-it-companies-of-india/dr-vaibhavi-d-thaker
The Impact of Educational Background and Professional Training on Human Right...ijtsrd
This study investigated the impact of educational background and professional training on human rights awareness among secondary school teachers in the Marathwada region of Maharashtra, India. The key findings reveal that higher levels of education, particularly a master’s degree, and fields of study related to education, humanities, or social sciences are associated with greater human rights awareness among teachers. Additionally, both pre service teacher training and in service professional development programs focused on human rights education significantly enhance teacher’s knowledge, skills, and competencies in promoting human rights principles in their classrooms. Baig Ameer Bee Mirza Abdul Aziz | Dr. Syed Azaz Ali Amjad Ali "The Impact of Educational Background and Professional Training on Human Rights Awareness among Secondary School Teachers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64529.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64529/the-impact-of-educational-background-and-professional-training-on-human-rights-awareness-among-secondary-school-teachers/baig-ameer-bee-mirza-abdul-aziz
A Study on the Effective Teaching Learning Process in English Curriculum at t...ijtsrd
“One Language sets you in a corridor for life. Two languages open every door along the way” Frank Smith English as a foreign language or as a second language has been ruling in India since the period of Lord Macaulay. But the question is how much we teach or learn English properly in our culture. Is there any scope to use English as a language rather than a subject How much we learn or teach English without any interference of mother language specially in the classroom teaching learning scenario in West Bengal By considering all these issues the researcher has attempted in this article to focus on the effective teaching learning process comparing to other traditional strategies in the field of English curriculum at the secondary level to investigate whether they fulfill the present teaching learning requirements or not by examining the validity of the present curriculum of English. The purpose of this study is to focus on the effectiveness of the systematic, scientific, sequential and logical transaction of the course between the teachers and the learners in the perspective of the 5Es programme that is engage, explore, explain, extend and evaluate. Sanchali Mondal | Santinath Sarkar "A Study on the Effective Teaching Learning Process in English Curriculum at the Secondary Level of West Bengal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd62412.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/62412/a-study-on-the-effective-teaching-learning-process-in-english-curriculum-at-the-secondary-level-of-west-bengal/sanchali-mondal
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...ijtsrd
This paper reports on a study which was conducted to investigate the role of mentoring and its influence on the effectiveness of the teaching of Physics in secondary schools in the South West Region of Cameroon. The study adopted the convergent parallel mixed methods design, focusing on respondents in secondary schools in the South West Region of Cameroon. Both quantitative and qualitative data were collected, analysed separately, and the results were compared to see if the findings confirm or disconfirm each other. The quantitative analysis found that majority of the respondents 72 of Physics teachers affirmed that they had more experienced colleagues as mentors to help build their confidence, improve their teaching, and help them improve their effectiveness and efficiency in guiding learners’ achievements. Only 28 of the respondents disagreed with these statements. With majority respondents 72 agreeing with the statements, it implies that in most secondary schools, experienced Physics teachers act as mentors to build teachers’ confidence in teaching and improving students’ learning. The interview qualitative data analysis summarized how secondary school Principals use meetings with mentors and mentees to promote mentorship in the school milieu. This has helped strengthen teachers’ classroom practices in secondary schools in the South West Region of Cameroon. With the results confirming each other, the study recommends that mentoring should focus on helping teachers employ social interactions and instructional practices feedback and clarity in teaching that have direct measurable impact on students’ learning achievements. Andrew Ngeim Sumba | Frederick Ebot Ashu | Peter Agborbechem Tambi "The Role of Mentoring and Its Influence on the Effectiveness of the Teaching of Physics in Secondary Schools in the South West Region of Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64524.pdf Paper Url: https://www.ijtsrd.com/management/management-development/64524/the-role-of-mentoring-and-its-influence-on-the-effectiveness-of-the-teaching-of-physics-in-secondary-schools-in-the-south-west-region-of-cameroon/andrew-ngeim-sumba
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...ijtsrd
This study primarily focuses on the design of a high side buck converter using an Arduino microcontroller. The converter is specifically intended for use in DC DC applications, particularly in standalone solar PV systems where the PV output voltage exceeds the load or battery voltage. To evaluate the performance of the converter, simulation experiments are conducted using Proteus Software. These simulations provide insights into the input and output voltages, currents, powers, and efficiency under different state of charge SoC conditions of a 12V,70Ah rechargeable lead acid battery. Additionally, the hardware design of the converter is implemented, and practical data is collected through operation, monitoring, and recording. By comparing the simulation results with the practical results, the efficiency and performance of the designed converter are assessed. The findings indicate that while the buck converter is suitable for practical use in standalone PV systems, its efficiency is compromised due to a lower output current. Chan Myae Aung | Dr. Ei Mon "Design Simulation and Hardware Construction of an Arduino-Microcontroller Based DC-DC High-Side Buck Converter for Standalone PV System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64518.pdf Paper Url: https://www.ijtsrd.com/engineering/mechanical-engineering/64518/design-simulation-and-hardware-construction-of-an-arduinomicrocontroller-based-dcdc-highside-buck-converter-for-standalone-pv-system/chan-myae-aung
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadikuijtsrd
Energy becomes sustainable if it meets the needs of the present without compromising the ability of future generations to meet their own needs. Some of the definitions of sustainable energy include the considerations of environmental aspects such as greenhouse gas emissions, social, and economic aspects such as energy poverty. Generally far more sustainable than fossil fuel are renewable energy sources such as wind, hydroelectric power, solar, and geothermal energy sources. Worthy of note is that some renewable energy projects, like the clearing of forests to produce biofuels, can cause severe environmental damage. The sustainability of nuclear power which is a low carbon source is highly debated because of concerns about radioactive waste, nuclear proliferation, and accidents. The switching from coal to natural gas has environmental benefits, including a lower climate impact, but could lead to delay in switching to more sustainable options. “Carbon capture and storage” can be built into power plants to remove the carbon dioxide CO2 emissions, but this technology is expensive and has rarely been implemented. Leading non renewable energy sources around the world is fossil fuels, coal, petroleum, and natural gas. Nuclear energy is usually considered another non renewable energy source, although nuclear energy itself is a renewable energy source, but the material used in nuclear power plants is not. The paper addresses the issue of sustainable energy, its attendant benefits to the future generation, and humanity in general. Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku "Sustainable Energy" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64534.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/64534/sustainable-energy/paul-a-adekunte
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...ijtsrd
This paper aims to outline the executive regulations, survey standards, and specifications required for the implementation of the Sudan Survey Act, and for regulating and organizing all surveying work activities in Sudan. The act has been discussed for more than 5 years. The Land Survey Act was initiated by the Sudan Survey Authority and all official legislations were headed by the Sudan Ministry of Justice till it was issued in 2022. The paper presents conceptual guidelines to be used for the Survey Act implementation and to regulate the survey work practice, standardizing the field surveys, processing, quality control, procedures, and the processes related to survey work carried out by the stakeholders and relevant authorities in Sudan. The conceptual guidelines are meant to improve the quality and harmonization of geospatial data and to aid decision making processes as well as geospatial information systems. The established comprehensive executive regulations will govern and regulate the implementation of the Sudan Survey Geomatics Act in all surveying and mapping practices undertaken by the Sudan Survey Authority SSA and state local survey departments for public or private sector organizations. The targeted standards and specifications include the reference frame, projection, coordinate systems, and the guidelines and specifications that must be followed in the field of survey work, processes, and mapping products. In the last few decades, there has been a growing awareness of the importance of geomatics activities and measurements on the Earths surface in space and time, together with observing and mapping the changes. In such cases, data must be captured promptly, standardized, and obtained with more accuracy and specified in much detail. The paper will also highlight the current situation in Sudan, the degree to which survey standards are used, the problems encountered, and the errors that arise from not using the standards and survey specifications. Kamal A. A. Sami "Concepts for Sudan Survey Act Implementations - Executive Regulations and Standards" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63484.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63484/concepts-for-sudan-survey-act-implementations--executive-regulations-and-standards/kamal-a-a-sami
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...ijtsrd
The discussions between ellipsoid and geoid have invoked many researchers during the recent decades, especially during the GNSS technology era, which had witnessed a great deal of development but still geoid undulation requires more investigations. To figure out a solution for Sudans local geoid, this research has tried to intake the possibility of determining the geoid model by following two approaches, gravimetric and geometrical geoid model determination, by making use of GNSS leveling benchmarks at Khartoum state. The Benchmarks are well distributed in the study area, in which, the horizontal coordinates and the height above the ellipsoid have been observed by GNSS while orthometric heights were carried out using precise leveling. The Global Geopotential Model GGM represented in EGM2008 has been exploited to figure out the geoid undulation at the benchmarks in the study area. This is followed by a fitting process, that has been done to suit the geoid undulation data which has been computed using GNSS leveling data and geoid undulation inspired by the EGM2008. Two geoid surfaces were created after the fitting process to ensure that they are identical and both of them could be counted for getting the same geoid undulation with an acceptable accuracy. In this respect, statistical operation played an important role in ensuring the consistency and integrity of the model by applying cross validation techniques splitting the data into training and testing datasets for building the geoid model and testing its eligibility. The geometrical solution for geoid undulation computation has been utilized by applying straightforward equations that facilitate the calculation of the geoid undulation directly through applying statistical techniques for the GNSS leveling data of the study area to get the common equation parameters values that could be utilized to calculate geoid undulation of any position in the study area within the claimed accuracy. Both systems were checked and proved eligible to be used within the study area with acceptable accuracy which may contribute to solving the geoid undulation problem in the Khartoum area, and be further generalized to determine the geoid model over the entire country, and this could be considered in the future, for regional and continental geoid model. Ahmed M. A. Mohammed. | Kamal A. A. Sami "Towards the Implementation of the Sudan Interpolated Geoid Model (Khartoum State Case Study)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63483.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63483/towards-the-implementation-of-the-sudan-interpolated-geoid-model-khartoum-state-case-study/ahmed-m-a-mohammed
Activating Geospatial Information for Sudans Sustainable Investment Mapijtsrd
Sudan is witnessing an acceleration in the processes of development and transformation in the performance of government institutions to raise the productivity and investment efficiency of the government sector. The development plans and investment opportunities have focused on achieving national goals in various sectors. This paper aims to illuminate the path to the future and provide geospatial data and information to develop the investment climate and environment for all sized businesses, and to bridge the development gap between the Sudan states. The Sudan Survey Authority SSA is the main advisor to the Sudan Government in conducting surveying, mappings, designing, and developing systems related to geospatial data and information. In recent years, SSA made a strategic partnership with the Ministry of Investment to activate Geospatial Information for Sudans Sustainable Investment and in particular, for the preparation and implementation of the Sudan investment map, based on the directives and objectives of the Ministry of Investment MI in Sudan. This paper comes within the framework of activating the efforts of the Ministry of Investment to develop technical investment services by applying techniques adopted by the Ministry and its strategic partners for advancing investment processes in the country. Kamal A. A. Sami "Activating Geospatial Information for Sudan's Sustainable Investment Map" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63482.pdf Paper Url: https://www.ijtsrd.com/engineering/information-technology/63482/activating-geospatial-information-for-sudans-sustainable-investment-map/kamal-a-a-sami
Educational Unity Embracing Diversity for a Stronger Societyijtsrd
In a rapidly changing global landscape, the importance of education as a unifying force cannot be overstated. This paper explores the crucial role of educational unity in fostering a stronger and more inclusive society through the embrace of diversity. By examining the benefits of diverse learning environments, the paper aims to highlight the positive impact on societal strength. The discussion encompasses various dimensions, from curriculum design to classroom dynamics, and emphasizes the need for educational institutions to become catalysts for unity in diversity. It highlights the need for a paradigm shift in educational policies, curricula, and pedagogical approaches to ensure that they are reflective of the diverse fabric of society. This paper also addresses the challenges associated with implementing inclusive educational practices and offers practical strategies for overcoming barriers. It advocates for collaborative efforts between educational institutions, policymakers, and communities to create a supportive ecosystem that promotes diversity and unity. Mr. Amit Adhikari | Madhumita Teli | Gopal Adhikari "Educational Unity: Embracing Diversity for a Stronger Society" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64525.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64525/educational-unity-embracing-diversity-for-a-stronger-society/mr-amit-adhikari
Integration of Indian Indigenous Knowledge System in Management Prospects and...ijtsrd
The diversity of indigenous knowledge systems in India is vast and can vary significantly between different communities and regions. Preserving and respecting these knowledge systems is crucial for maintaining cultural heritage, promoting sustainable practices, and fostering cross cultural understanding. In this paper, an overview of the prospects and challenges associated with incorporating Indian indigenous knowledge into management is explored. It is found that IIKS helps in management in many areas like sustainable development, tourism, food security, natural resource management, cultural preservation and innovation, etc. However, IIKS integration with management faces some challenges in the form of a lack of documentation, cultural sensitivity, language barriers legal framework, etc. Savita Lathwal "Integration of Indian Indigenous Knowledge System in Management: Prospects and Challenges" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63500.pdf Paper Url: https://www.ijtsrd.com/management/accounting-and-finance/63500/integration-of-indian-indigenous-knowledge-system-in-management-prospects-and-challenges/savita-lathwal
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...ijtsrd
The COVID 19 pandemic has highlighted the crucial need of preventive measures, with widespread use of face masks being a key method for slowing the viruss spread. This research investigates face mask identification using deep learning as a technological solution to be reducing the risk of coronavirus transmission. The proposed method uses state of the art convolutional neural networks CNNs and transfer learning to automatically recognize persons who are not wearing masks in a variety of circumstances. We discuss how this strategy improves public health and safety by providing an efficient manner of enforcing mask wearing standards. The report also discusses the obstacles, ethical concerns, and prospective applications of face mask detection systems in the ongoing fight against the pandemic. Dilip Kumar Sharma | Aaditya Yadav "DeepMask: Transforming Face Mask Identification for Better Pandemic Control in the COVID-19 Era" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64522.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/64522/deepmask-transforming-face-mask-identification-for-better-pandemic-control-in-the-covid19-era/dilip-kumar-sharma
Streamlining Data Collection eCRF Design and Machine Learningijtsrd
Efficient and accurate data collection is paramount in clinical trials, and the design of Electronic Case Report Forms eCRFs plays a pivotal role in streamlining this process. This paper explores the integration of machine learning techniques in the design and implementation of eCRFs to enhance data collection efficiency. We delve into the synergies between eCRF design principles and machine learning algorithms, aiming to optimize data quality, reduce errors, and expedite the overall data collection process. The application of machine learning in eCRF design brings forth innovative approaches to data validation, anomaly detection, and real time adaptability. This paper discusses the benefits, challenges, and future prospects of leveraging machine learning in eCRF design for streamlined and advanced data collection in clinical trials. Dhanalakshmi D | Vijaya Lakshmi Kannareddy "Streamlining Data Collection: eCRF Design and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63515.pdf Paper Url: https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
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1. Velocity: speed with which the data is generated
and speed with which data moves around or the
speed with which the data is processed. For
example data generated on social networking site
facebook on which 140 billion photos are being
uploaded daily [1].
2. Volume: it represents the large amount of data
generated from different sources. These sources
can be multimedia files, log files, sensors etc.
3. Variety: it represents the heterogeneous data that
is generated at a large scale. The data can be
structured, unstructured or semi-structured.
4. Veracity: represents the quality of data that is
collected can vary greatly that may affect the
proper analysis of the data.
5. Value: it’s not worthy if we can’t convert the raw
information into the some valuable or useful
information. This characteristic describes about
the quality of data that we retrieve from the raw
information.
Figure 1: 5V’s of Big Data
These 5 characteristics explain about the big data. Big
Data Analytics or BDA helps in examining the large
amount of data so that it can uncover the hidden
patterns, correlations between the different data and
other insights [5]. This examining of the data took
place in different phases which requires various tools
and techniques to do so. BDA helps in effective cost
reduction, faster and better decision making in the
business and new products and services can be
designed and provided on the basis of the analysis of
the data [5]. These different phases help in collecting,
cleansing and processing of the data.
Figure 2: Big Data Analytics phases
6. Phase 1: Data generation: at this phase the data is
being collected from the different sources like
sensors, IoT devices, log files, web servers, a
group of people or from the community. For this
purpose Parallel Data Generation Framework tool
is used to generate and distribute the data. This
generation process of data is continuous.
7. Phase 2: Data Acquisition & Storage: Data
acquisition is the process of gathering, filtering
and cleaning of the data before putting it into a
data warehouse or any other storage solution.
Software tools used during data acquisition phase
is Storm that consists of three nodes: Nimbus,
Zookeeper, Supervisor Nodes. Other tool used are
Kafka, Flume, Hadoop Common, Hadoop
Distributed File System (HDFS), Hadoop YARN,
Hadoop Map Reduce. Data Storage is a storage
infrastructure which is specially designed for
storing, managing and extracting the massive
amount of data [6]. For the storage purpose of Big
Data Hadoop, NoSQL and Cassandra analytics
engines are used. Apache Hadoop Distributed File
System is most used analytics engine which is
combined with the flavor of the NoSQL database
[7].
8. Phase 3: Data Processing: for the processing of
the data Map Reduce component of the Apache
Hadoop is used. It is the processing pillar of the
Hadoop having the two functions Map and
Reduce which splits the data into independent
chunks to process, sort and retrieve. Big data
techniques used to process the data are reporting,
batch analytics, online analytical processing, data
mining, text mining, complex event
processing(CEP), predictive analysis etc. Tools
used are Google Chubby, Apache Hadoop, HDFS
(Hadoop Distributed File System), Hadoop
YARN, MPI(Message Passing Interface), Spark,
Kafka, Apache Flume, Apache Chukwa,
Facebook Scribe etc.
9. Phase 4: Data Querying:the data which is stored
and processed in the previous steps is retrieved.
The data is gathered from various sources and
aggregated with the help of the HDFS and Map
Reduce. Data tools used are HIVE for data
summation, querying and analysis, IMPALA
allows user to perform the low latency queries
effectively, HAWQ big tasks are divided into the
smaller ones and these smaller tasks are
distributed to the MPP SQL processing unit for
execution, Drill it can handle up to 10,000 servers
for the efficient querying it supports HBase,
MongoDB, MapR-DB, HDFS, MapR-FS,
Amazon S3, Azure Blob Storage etc., Tajo
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designed for scalable ad hoc queries, online
aggregation of data and ETLon large datasets that
are stored on the HDFS and other data sources,
Apache Pig designed to analyze large data sets
that consists of the high level languages that
expresses the analysis program.
10. Phase 5: Data Analysis: in this phase large and
variety of data sets are examined so that we can
uncover the hidden patterns, find the unknown
correlations between the varying data, what are
the market trends and some other useful
information that helps to make the organizations
better decisions for their firms. Tools that are
used in this phase are Hadoop YARN, Kafka, Pig,
HIVE, HBase, Spark, Hadoop Map Reduce etc.
Hence these are the different phases that works
during the analysis of the big data.
III. RELATED WORKS
Tiwarkhede et.al., 2013, stated about the concepts of
the big data, its 3V’s velocity, volume and variety.
Paper provides a b rief description about how the
generated data can be divided into various big data
applications such as Structured analysis, Text
Analytics, Web Analytics, Multimedia Analytics and
Mobile Analytics. These analytics applications
describes about how the data is being generated from
different fields. There are many techniques also
through which we can analyze the datasets and some
techniques are machine learning. Techniques that are
discussed in this paper are A/B Testing in it control
group is compared to the various test groups,
Classification in it new data sets are categorized and
assigned to the predefined classes, Crowd Sourcing
data collected is submitted by the gropu of people or
the community, Data Mining in it patterns of data are
extracted [8].
Ahlawat et.al., 2016, stated about the various
definitions of the big data given by the researchers,
5V’s of the big data, importance of the big data,
various data forms that are available in big data.
Manyika et.al, 2011 describes the big data as the
amount of data that is beyond the ability of the
technology to store, manage and process the data
efficiently. Tech America Foundation, 2014 describes
the big data as the huge amount of data that is having
high velocity, is complex, having variety, and having
a huge volume that can be captured, stored,
distributed and managed efficiently. Data Forms of
the big data that are available are Structured Form
where whole data is organized in the entity form,
Semi-structured data may be available in many
formats, Un Structured Format in this data has no
format and sequence. various tools and techniques
used in big data are also described and they are
Association rule learning(discovering interesting
relationships), Data Mining(searching or digging into
a data file), Cluster Analysis(divides the group of
people or community), Crowd Sourcing(info is
gathered from large group of people), Machine
Learning(algorithms are crafted), Text Analysis
(unstructured text data is converted into meaningful
data), EDWs(enterprise data warehouse),
Visualization products(represents the result
visually),Map Reduce (processing of the data),
Hadoop(store and process big data in distributed
environment), NoSQL (helps in analyzing and
accessing massive amount of data)[9].
Thomas et.al., 2015, stated about the concepts of the
big data, Parallel data flow model and Map Reduce
and various analytics use cases. Parallel Data flow
Model used for the parallel programming which
makes the programming easy it works on the shared
nothing cluster of the computers in the data centre
and machines which are involved can communicate
through the simple data messages stream without the
need of expensive shared memory. Map Reduce is the
heart of the Hadoop and provides the great scalability
to work over the thousands of the servers. It allows
the user to write the traditional code into C, Java,
Python, Perl and requires a file system to read. The
best big analytics use cases discussed in this paper are
Semantic Analysis, 3600
view of customer, Ad hoc
Data Analysis, Real Time Analytics, Multi-Channel
Marketing, Customer Micro Segmentation, Ad Fraud
Detection, Click Stream Analysis, Data Warehouse
Modernization, Big Data and Predictive Modelling
[10].
Beakta, 2015, have studied about the 4V’s of big data,
challenges of the big data, Hadoop and Map Reduce.
This paper mainly concerns about the Hadoop and
Map Reduce that are used for the storage and
processing of the big data. In this the storage is
associated with the HDFS (Hadoop Distributed File
System) and processing is associated with the Map
Reduce(Map and Reduce) these two functions divides
the data into the independent chunks and reduce
functions collects the answer from the different
chunks and aggregate them to produce the useful
information. Some applications of the big data are
classification analysis, cluster analysis, evolution
analysis and outer analysis [11].
Bhosale et.al., 2014, stated about the architecture of
Hadoop and Map Reduce, and several other
components of the Hadoop. Hadoop is a
programming framework that is developed by the
Google’s map reduce that is a software where
application is break down into various parts. Current
system of hadoop is Apache Hadoop Ecosystem.
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Hadoop architecture is basically divided into two
layers i.e. HDFS layer and Map reduce layer. HDFS
layer can store huge amount of information, it can
also survive the failure of significant parts of the
storage infrastructure without losing data. Hadoop
creates the clusters of machines and coordinate the
work among them. If one fails then it continues it
work by shifting the work to remaining machines.
Map Reduce is the processing pillar of hadoop
system. This framework allows the specification of
operation to be applied on a huge data set, it divides
the data and problem and run it in parallel [12].
Bendre et.al., 2016, this paper describes about the big
data, big data analytics, cloud computing and Apache
Hadoop. Different phases of the BDA have been
discussed that are data generation, data acquisition &
storage, data processing, data querying and data
analytics. It includes the brief description about the
tools or techniques used during different phases. Data
generated from different sources like sensors, log
files, multimedia files etc. Data acquisition and
storage phase includes Kafka, flume, HDFS, Hadoop
Common, Hadoop YARN, Map Reduce etc. Data
Processing phase includes Apache Kafka, Apache
Flume, Apache Chukwa etc. Data querying phase
includes IMPALA, HIVE, PIG, DRILL, HBase,
Google Cloud Storage, Tajo, Azure Blob Storage etc.
Big Data Analytics classes are also described in this
paper that are Structured Data Analytics, Text
Analytics, Multimedia Analytics, Network Analytics,
Web Data Analytics and Mobile Analytics [13].
Zhuming Bi et.al, 2014, in this paper the concepts of
the big data, big data analytics, how the data is being
collected from the different sources and how the IOT
is making possible for the cloud computing so that it
can acquire the data from the different sources. As the
data is increasing day by day therefore to manage the
data cloud computing is offering the reliable services
or the technologies like NoSQL, Map Reduce like
technologies are needed to tackle with the big data
and to retrieve the big data. Data is being collected,
data is managed and then data is utilized and all this
happens with the help of the different tools that are
developed to analyze and retrieve the big data. BDA
has been explained as the process of inspecting,
cleaning, transforming and modeling the big data.
The BDA tools have been designed so that it can take
into account the increase in volume of the requests,
size of the data, computational load, the type of the
user and the locality. Talia (2013) the BDA tools can
be discussed with respect to the following
(1) Programming abstracts (2) interoperability and
openness of the data and tools (3) System Integration
(4) Annotation Mechanisms. Software and the
platforms are the driving forces of the BD that is big
data. Four primary technologies for the processing of
the big data are Grid Computing, in-database
processing, in-memory analytics And the Hadoop.
Two architectures have been discussed that deals with
the BDA they are the
RDBMS and Map Reduce/ Hadoop. Hadoop is being
described that it being used for the distribution,
storage, query processing and management of the
data. Hadoop have two components (1) HDFS i.e.
hadoop distributed file system used for the storage
purpose of the data (2) Map Reduce which is also
known as the processing pillar of the Hadoop and it
also consists of the two functions known as the Map
function and Reduce function. The BDA tools helps
in the efficient capture of the system information,
processing and utilization of the information. The ten
core technologies stated in this paper for the
processing are Google Refine, data serialization such
as Avro, data storage such as Amazon S3, the cloud
such as Azure, NoSQL such as Hypertable, Map
Reduce such as Pig, data processing such as
Mechanical Turk, natural language processing such as
National Language Toolkit, Machine learning such as
Mahout and visualization such as Graph Viz [14].
Kaur et.al, 2017 stated about the algorithms used in
data mining for the big data, the types of data mining
system, about the issues and challenges and problems
of big data in data mining. Algorithms that are used
are classification tree, logistic regression, neural
networks, clustering techniques. The types of data
mining system are categorized according to the types
of data sources mined, according to data model used,
according to sort of knowledge discovered, according
to excavation techniques used. The problem about the
big data is how the huge amount of data get explored
so that we can explore useful information from that
large data. Issues are poor data quality, security,
higher cost, less flexibility etc. Solutions to the big
data are Hadoop which allow massive amount of
storage for any kind of data, Cloud era it allows
companies to access the data from large databases,
Monod it manages the data that is unstructured or
changes frequently [15].
Singh et.al, 2017 stated about the tools that are used
in big data. These tools are Apache Hadoop is an
open source framework based on java developed and
maintained by apache foundation it’s used for the
massive analysis of data and storage of data in a
cluster. Microsoft HD Insight this tool is provided by
the Microsoft for big data solution and this is also
powered by Apache Hadoop, NoSQL it is used to
handle the unstructured data that does not follow any
particular schema and provides improved
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performance in storing huge amount of data, HIVE
it’s an associated library of hadoop and it also
supports query language known as HiveSQL that
provide query solutions on big data, Sqoop it’s a tool
that connects Hadoop with various relational
databases to transfer data, Polybase it allows data
analysts to use the very commonly known T-SQL, in
a very commonly used development environment -
SQL Server Management Studio to query data stored
in a Hadoop cluster[16].
Kaur et.al,2017 stated about the tools and techniques
for the big data.
Hadoop: important technique and it’s a programming
framework developed by Google’s Map reduce.
Hadoop is used to handle the data with the help of
divide and conquer method. Hadoop includes two
steps: map (divides the data into number of sub parts)
and reduce(collects all the answers from sub parts and
combine them to get an appropriate output).
HDFC: its hadoop distributed file system it have
client-server architecture and process the large
amount of data.
HPCC: its high performance computing cluster used
to manage complex problems. It’s a single platform
system, having single architecture and single
programming language to process the data. Some
components of HPCC are HPCC data refinery, HPCC
data delivery, enterprise control language.
Grid computing: it’s the technique in which
computers are interconnected and share resources to
each other. This technique is used with the help of
hadoop.
Data mining: technique that is used to extract useful
information from large datasets.
R tool: R is free software programming language
which is used for statistical computing and graphics.
KEEL: It is Knowledge Extraction based on
Evolutionary Learning and its application software of
machine learning tools. It helps to solve the data
mining problems with the use of evolutionary
algorithms.
WEKA: Waikato Environment for Knowledge
Analysis. For solving the data mining problems
WEKA works on the machine learning algorithms
[17].
Chen et.al., 2014, stated about the general
background of the big data, related technologies such
as cloud computing, Internet Of Things, data centers
and Hadoop. This paper also reviews about the four
phases of the value chain of big data i.e. data
generation, data acquisition, data storage and data
analysis. Relationship between Cloud Computing and
Big Data is that development of the cloud computing
provides solution for the storage and processing of the
big data. The distributed storage technology based on
the cloud computing can effectively manage the big
data. Relationship between IoT and Big Data is that
big data is generated by the IoT devices. Report was
given by the Intel that pointed out that IoT has three
different features that conform the big data
diagram(1) various terminal generate massive amount
of data (2) data that is generated by IoT is generally
structured or semi-structured (3) data generated by
IoT is useful only when it is analyzed. But the data
processing capacity of IoT has fallen so it becomes
necessary to accelerate the big data technologies to
promote the development of IoT. Data centers in the
big data provides a back stage support, the growth of
big data applications accelerates innovation and
revolution of data centers, data centers also
strengthens the soft capacities like capacities of
acquisition, processing, organization, analysis and
application of big data. Hadoop is used in big data for
the storage and processing purpose and for this
different components of hadoop are used [31].
TRIFU et.al., 2014, stated about the big data
characteristics given by 4V’s volume, velocity,
veracity, variety. Different tools have been briefed
used for the big data efficient processing, storage and
analysis. These are NoSQL Databases means “Not
Only SQL” which uses wide column store, document,
key value structures or other type of structure.
MongoDB can manage the large number of data sets
with low maintenance. Cassandra is a key and column
oriented and used for the storage purpose of the big
data. Big Table is a distributed store system used for
managing structured data designed for a very large
scale. HBase is known as hadoop database also used
for the storage of the massive amount of data and is a
open source clone to the big table. Map Reduce
Model helps in processing large data sets in parallel.
Hadoop is a Map Reduce system developed by
YAHOO after Google’s Map Reduce infrastructure.
Various uses of big data are in Healthcare, Marketing,
Education, Transportation etc.[32].
Gencer et.al., 2015, stated about the scope of the big
data i.e. what was the past of the big data, what is
present situation of big data and what will be the
future of the big data. The paper shows that the
increase in the big data violently increases in the year
2011. At present the search for the big data is on its
peak or we can say that the big data becomes the most
important term of the IT industry. The increase in
interest of the big data is increasing day by day but
the decrease in interest of data mining day by day. In
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today of the Big data they have shown the work of the
various authors and their researches like Quian et.al.
first introduces the “gra nular computing”. After that
they have defined encoded decision table and
discussed some criteria. Google presented the another
big data study that “Google Flu Trends” which was
helpful in analyzing worldwide flu trends by using
Google Search Terms. The interest in big data is
increasing day by day. There will be increase in the
studies of the big data in the field of industries from
automotive and communication to finance and health
will increase in future. Hence it will become most
important in the future to manage the big data very
efficiently [33].
Chong et.al., 2015, presents an overview of big data
analytics, programming model, storage and
application of big data. The paper states about the
infrastructure of the big data which includes different
phases like data acquisition and storage, data
processing, data analysis, data querying. The
programming model of the big data are Map Reduce,
Graph processing Model, Stream Processing Model
etc. The big data analytics means that we have to
extract the meaningful information from the bunch of
the data collected from different sources. Data
analytics can be done in different classes like
Descriptive Analysis, Predictive analysis, Prescriptive
analysis etc. The benchmarking of the big data drawn
the attention of the researchers and practitioners.
Benchmarking can be grouped in two types:
component benchmarks and system benchmarks.
Various applications of the big data are also discussed
in the field of Business, Social Application, Scientific
application [34].
O. Chan, 2013, stated about the concepts of the big
data and its characteristics. This paper also gives
overview about the Big Data Analytics, NoSQL,
Hadoop, Distributed File System and Map Reduce.
This paper describes about the characteristics of the
big data that are volume, velocity, variety, veracity,
value. An overview about the architecture of the big
data is given that describes that architecture is based
on the client server architecture. HBase/ Hadoop
Cluster Architecture for big data also described which
states that it consists of the master and slave nodes.
This architecture is used for the storage and
processing purpose of the big data. Big Data
Analytics architecture is also described which
consists of the different components like Map Reduce
Analytics, Hadoop Cluster HDFS, Real-Time
NoSQL, ETL, BI Analytics etc. all having their own
functionality. It also explains how different type of
data captured through different systems, how data is
being captured or collected through different sources,
cleansed, processed and analyzed. Hence this paper
reviews about the concepts of the Big Data Analytics
and its architecture [34].
IV. CONCLUSION
Today, all the IT professionals, engineers and
researchers are working on big data. Big data is term
of concerning about large volume of complex data
sets. In order to solve problems of big data
challenges, many researchers proposed a different
system models, techniques for big data. The high
performance computing paradigm is required to
manage the huge amount of data being generated in
different fields. In the coming years the existing tools
and techniques will not cooperate with the increasing
size of the data hence in future the alternates will be
needed for the existing tools and techniques. In future
growth rate of the data is going to be very huge
therefore new discoveries for the tools and techniques
will be needed to manage the enormous growth of the
data.
REFERENCES
[1] http://asperasoft.com/the-human-face-of-big-
data/
[2] https://www.bernardmarr.com/default.asp?cont
entID=1076
[3] https://en.wikipedia.org/wiki/Big_data
[4] https://www.slideshare.net/BernardMarr/14022
8-big-data-volume-velocity-variety-varacity-
value
[5] https://www.sas.com/en_us/insights/analytics/b
ig-data-analytics.html#dmhistory [6]
https://link.springer.com/chapter/10.1007/978-
3-319-21569-3_4
[6] http://www.computerweekly.com/podcast/Big-
data-storage-Defining-big-data-and-the-type-
of-storage-it-needs
[7] Ankita S. Tiwarkhede, Prof. Vinit Kakde,” A
review paper on big data analytics”,
International Journal of Science and Research
(IJSR) ISSN (Online): 2319-7064 Index
Copernicus Value (2013): 6.14 | Impact Factor
(2013): 4.438.
[8] Tanvi Ahlawat and Dr. Radha Krishna
Rambola School Of Computing Science and
Engineering Galgotias University, Greater
Noida,” Literature review on Big Data”,
International Journal of Advancement in
Engineering Technology, Management and
Applied Science ,Volume 3, Issue 5 May 2016,
ISSN No :2349-3224.
[9] Mrs. Mereena Thomas Assistant Professor,
department of computer applications, SNGIST
college, N.Paravur, Kerala, India,”A Review
Paper on Big Data”, International Research