Our journal that operates on a monthly basis. It embraces the principles of open access, peer review, and full refereeing to ensure the highest standards of scholarly communication stands as a testament to the commitment of the global scientific community towards advancing research, promoting interdisciplinary collaborations, and enhancing academic excellence.
ISSUES, CHALLENGES, AND SOLUTIONS: BIG DATA MININGcscpconf
Data has become an indispensable part of every economy, industry, organization, business
function and individual. Big Data is a term used to identify the datasets that whose size is
beyond the ability of typical database software tools to store, manage and analyze. The Big
Data introduce unique computational and statistical challenges, including scalability and
storage bottleneck, noise accumulation, spurious correlation and measurement errors. These
challenges are distinguished and require new computational and statistical paradigm. This
paper presents the literature review about the Big data Mining and the issues and challenges
with emphasis on the distinguished features of Big Data. It also discusses some methods to deal
with big data.
Data has become an indispensable part of every economy, industry, organization, business
function and individual. Big Data is a term used to identify the datasets that whose size is
beyond the ability of typical database software tools to store, manage and analyze. The Big
Data introduce unique computational and statistical challenges, including scalability and
storage bottleneck, noise accumulation, spurious correlation and measurement errors. These
challenges are distinguished and require new computational and statistical paradigm. This
paper presents the literature review about the Big data Mining and the issues and challenges
with emphasis on the distinguished features of Big Data. It also discusses some methods to deal
with big data.
Big data - what, why, where, when and howbobosenthil
The document discusses big data, including what it is, its characteristics, and architectural frameworks for managing it. Big data is defined as data that exceeds the processing capacity of conventional database systems due to its large size, speed of creation, and unstructured nature. The architecture for managing big data is demonstrated through Hadoop technology, which uses a MapReduce framework and open source ecosystem to process data across multiple nodes in parallel.
The Big Data Importance – Tools and their UsageIRJET Journal
This document discusses big data, tools for analyzing big data, and opportunities that big data analytics provides. It begins by defining big data and its key characteristics of volume, variety and velocity. It then discusses tools for storing, managing and processing big data like Hadoop, MapReduce and HDFS. Finally, it outlines how big data analytics can be applied across different domains to enable new insights and informed decision making through analyzing large datasets.
This document discusses big data characteristics, issues, challenges, and technologies. It describes the key characteristics of big data as volume, velocity, variety, value, and complexity. It outlines issues related to these characteristics like data volume and velocity. Challenges of big data include privacy and security, data access and sharing, analytical challenges, human resources, and technical challenges around fault tolerance, scalability, data quality, and heterogeneous data. The document also discusses technologies used for big data like Hadoop, HDFS, and cloud computing and provides examples of big data projects.
The document acknowledges and thanks several people who helped with the completion of a seminar report. It expresses gratitude to the seminar guide for being supportive and compassionate during the preparation of the report. It also thanks friends who contributed to the preparation and refinement of the seminar. Finally, it acknowledges profound gratitude to the Almighty for making the completion of the report possible with their blessings.
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.
ISSUES, CHALLENGES, AND SOLUTIONS: BIG DATA MININGcscpconf
Data has become an indispensable part of every economy, industry, organization, business
function and individual. Big Data is a term used to identify the datasets that whose size is
beyond the ability of typical database software tools to store, manage and analyze. The Big
Data introduce unique computational and statistical challenges, including scalability and
storage bottleneck, noise accumulation, spurious correlation and measurement errors. These
challenges are distinguished and require new computational and statistical paradigm. This
paper presents the literature review about the Big data Mining and the issues and challenges
with emphasis on the distinguished features of Big Data. It also discusses some methods to deal
with big data.
Data has become an indispensable part of every economy, industry, organization, business
function and individual. Big Data is a term used to identify the datasets that whose size is
beyond the ability of typical database software tools to store, manage and analyze. The Big
Data introduce unique computational and statistical challenges, including scalability and
storage bottleneck, noise accumulation, spurious correlation and measurement errors. These
challenges are distinguished and require new computational and statistical paradigm. This
paper presents the literature review about the Big data Mining and the issues and challenges
with emphasis on the distinguished features of Big Data. It also discusses some methods to deal
with big data.
Big data - what, why, where, when and howbobosenthil
The document discusses big data, including what it is, its characteristics, and architectural frameworks for managing it. Big data is defined as data that exceeds the processing capacity of conventional database systems due to its large size, speed of creation, and unstructured nature. The architecture for managing big data is demonstrated through Hadoop technology, which uses a MapReduce framework and open source ecosystem to process data across multiple nodes in parallel.
The Big Data Importance – Tools and their UsageIRJET Journal
This document discusses big data, tools for analyzing big data, and opportunities that big data analytics provides. It begins by defining big data and its key characteristics of volume, variety and velocity. It then discusses tools for storing, managing and processing big data like Hadoop, MapReduce and HDFS. Finally, it outlines how big data analytics can be applied across different domains to enable new insights and informed decision making through analyzing large datasets.
This document discusses big data characteristics, issues, challenges, and technologies. It describes the key characteristics of big data as volume, velocity, variety, value, and complexity. It outlines issues related to these characteristics like data volume and velocity. Challenges of big data include privacy and security, data access and sharing, analytical challenges, human resources, and technical challenges around fault tolerance, scalability, data quality, and heterogeneous data. The document also discusses technologies used for big data like Hadoop, HDFS, and cloud computing and provides examples of big data projects.
The document acknowledges and thanks several people who helped with the completion of a seminar report. It expresses gratitude to the seminar guide for being supportive and compassionate during the preparation of the report. It also thanks friends who contributed to the preparation and refinement of the seminar. Finally, it acknowledges profound gratitude to the Almighty for making the completion of the report possible with their blessings.
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.
This document summarizes a research paper on big data and Hadoop. It begins by defining big data and explaining how the volume, variety and velocity of data makes it difficult to process using traditional methods. It then discusses Hadoop, an open source software used to analyze large datasets across clusters of computers. Hadoop uses HDFS for storage and MapReduce as a programming model to distribute processing. The document outlines some of the key challenges of big data including privacy, security, data access and analytical challenges. It also summarizes advantages of big data in areas like understanding customers, optimizing business processes, improving science and healthcare.
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
This document discusses issues, opportunities, and challenges related to big data. It provides an overview of big data characteristics like volume, variety, velocity, and veracity. It also describes Hadoop and HDFS for distributed storage and processing of big data. The document outlines issues in big data like storage, management, and processing challenges due to scale. Opportunities in big data analytics are also presented. Finally, challenges like heterogeneity, scale, timeliness, and ownership are discussed along with approaches like Hadoop, Spark, NoSQL databases, and Presto for tackling big data problems.
This document discusses scheduling algorithms for processing big data using Hadoop. It provides background on big data and Hadoop, including that big data is characterized by volume, velocity, and variety. Hadoop uses MapReduce and HDFS to process and store large datasets across clusters. The default scheduling algorithm in Hadoop is FIFO, but performance can be improved using alternative scheduling algorithms. The objective is to study and analyze various scheduling algorithms that could increase performance for big data processing in Hadoop.
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.
A Review Paper on Big Data and Hadoop for Data Scienceijtsrd
Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, technqiues and frameworks. Hadoop is an open source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Mr. Ketan Bagade | Mrs. Anjali Gharat | Mrs. Helina Tandel "A Review Paper on Big Data and Hadoop for Data Science" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29816.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/29816/a-review-paper-on-big-data-and-hadoop-for-data-science/mr-ketan-bagade
How do data analysts work with big data and distributed computing frameworks.pdfSoumodeep Nanee Kundu
This document discusses how data analysts work with big data and distributed computing frameworks. It begins by defining big data and explaining the challenges it poses in terms of volume, velocity, variety and veracity. It then explores how distributed computing frameworks like Hadoop, Spark and Storm enable data analysts to process large and complex datasets in parallel across clusters of machines. The role of data analysts is described as collecting, analyzing and interpreting data to generate insights. Analysts collaborate with data engineers and scientists, using tools like Python, SQL and Tableau. Examples are given of how big data is applied in domains like healthcare, IoT and fraud detection. Case studies on Google's PageRank and Twitter analytics are also summarized.
1) The document discusses using k-means clustering to analyze big data. K-means is an algorithm that partitions data into k clusters based on similarity.
2) It provides background on big data characteristics like volume, variety, and velocity. It also discusses challenges of heterogeneous, decentralized, and evolving data.
3) The document proposes applying k-means clustering to big data to map data into clusters according to its properties in a fast and efficient manner. This allows statistical analysis and knowledge extraction from large, complex datasets.
This document summarizes a research paper on using k-means clustering to analyze big data. It begins with an introduction to big data and its characteristics. It then discusses related work on big data storage, mining, and analytics. The HACE theorem for defining big data is presented. The k-means clustering algorithm is explained as an efficient method for partitioning big data into groups. The proposed system uses k-means clustering followed by data mining and classification modules. Experimental results on two datasets show that the recursive k-means approach finds clusters closer to the actual number than the iterative approach. In conclusion, clustering is effective for handling big data attributes like heterogeneity and complexity, and k-means distribution helps distribute data into appropriate clusters.
Big Data Processing with Hadoop : A ReviewIRJET Journal
1. This document provides an overview of big data processing with Hadoop. It defines big data and describes the challenges of volume, velocity, variety and variability.
2. Traditional data processing approaches are inadequate for big data due to its scale. Hadoop provides a distributed file system called HDFS and a MapReduce framework to address this.
3. HDFS uses a master-slave architecture with a NameNode and DataNodes to store and retrieve file blocks. MapReduce allows distributed processing of large datasets across clusters through mapping and reducing functions.
This document provides a review of Hadoop storage and clustering algorithms. It begins with an introduction to big data and the challenges of storing and processing large, diverse datasets. It then discusses related technologies like cloud computing and Hadoop, including the Hadoop Distributed File System (HDFS) and MapReduce processing model. The document analyzes and compares various clustering techniques like K-means, fuzzy C-means, hierarchical clustering, and Self-Organizing Maps based on parameters such as number of clusters, size of clusters, dataset type, and noise.
The document is a project report submitted by Suraj Sawant to his college on the topic of "Map Reduce in Big Data". It discusses the objectives, introduction and importance of big data and MapReduce. MapReduce is a programming model used for processing large datasets in a distributed manner. The document provides details about the various stages of MapReduce including mapping, shuffling and reducing data. It also includes diagrams to explain the execution process and parallel processing in MapReduce.
Big Data Paradigm - Analysis, Application and ChallengesUyoyo Edosio
This document discusses big data, including its exponential growth driven by the internet and cheaper computing. It defines big data based on its volume, velocity, and variety (the 3 Vs). Hadoop and MapReduce are presented as tools for analyzing large and diverse datasets. The challenges of big data analysis are also discussed, such as noise, privacy issues, and infrastructure costs.
This document provides an overview of big data. It begins with an introduction that defines big data as massive, complex data sets from various sources that are growing rapidly in volume and variety. It then discusses the brief history of big data and provides definitions, describing big data as data that is too large and complex for traditional data management tools. The document outlines key aspects of big data including the sources, types, applications, and characteristics. It discusses how big data is used in business intelligence to help companies make better decisions. Finally, it describes the key aspects a big data platform must address such as handling different data types, large volumes, and analytics.
Data modeling techniques used for big data in enterprise networksDr. Richard Otieno
This document discusses data modeling techniques for big data in enterprise networks. It begins by defining big data and its characteristics, including volume, velocity, variety, veracity, value, variability, visualization and more. It then discusses various data modeling techniques and models that can be used for big data, including relational, non-relational, network, hierarchical and others. Finally, it examines some limitations in modeling big data for enterprise networks and calls for continued research on developing new modeling techniques to better handle the complexities of big data.
The document is a seminar report submitted by Nikita Sanjay Rajbhoj to Prof. Ashwini Jadhav at G.S. Moze College of Engineering. It discusses big data, including its definition, characteristics, architecture, technologies, and applications. The report includes an abstract, introduction, and sections on definition, characteristics, architecture, technologies, and applications of big data. It also includes references, acknowledgements, and certificates.
This document discusses big data, including what it is, its characteristics, advantages, and challenges. Big data refers to extremely large data sets that cannot be processed with traditional data processing tools. It is characterized by its volume, variety, velocity, variability, and veracity. Big data has advantages in fields like predicting diseases and improving transportation safety. However, challenges include storing large amounts of data from various sources and processing it quickly. The document outlines tools used for big data like Hadoop and MongoDB and concludes that big data plays a vital role in today's world.
https://jst.org.in/index.html
Our journal has academic journals form a crucial nexus. Educators leverage the latest research findings to enrich their teaching methodologies, ensuring that students are exposed to the most current and relevant information. Simultaneously, these educators contribute to the body of knowledge through their own research, creating a perpetual cycle of growth.
https://ijaast.com/index.html
Our journal has open-access nature of IJAAST fosters global collaboration. Researchers from diverse geographical locations can engage with and build upon each other's work, transcending borders to collectively address the challenges and opportunities in agricultural science and technology.
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.
This document summarizes a research paper on big data and Hadoop. It begins by defining big data and explaining how the volume, variety and velocity of data makes it difficult to process using traditional methods. It then discusses Hadoop, an open source software used to analyze large datasets across clusters of computers. Hadoop uses HDFS for storage and MapReduce as a programming model to distribute processing. The document outlines some of the key challenges of big data including privacy, security, data access and analytical challenges. It also summarizes advantages of big data in areas like understanding customers, optimizing business processes, improving science and healthcare.
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
This document discusses issues, opportunities, and challenges related to big data. It provides an overview of big data characteristics like volume, variety, velocity, and veracity. It also describes Hadoop and HDFS for distributed storage and processing of big data. The document outlines issues in big data like storage, management, and processing challenges due to scale. Opportunities in big data analytics are also presented. Finally, challenges like heterogeneity, scale, timeliness, and ownership are discussed along with approaches like Hadoop, Spark, NoSQL databases, and Presto for tackling big data problems.
This document discusses scheduling algorithms for processing big data using Hadoop. It provides background on big data and Hadoop, including that big data is characterized by volume, velocity, and variety. Hadoop uses MapReduce and HDFS to process and store large datasets across clusters. The default scheduling algorithm in Hadoop is FIFO, but performance can be improved using alternative scheduling algorithms. The objective is to study and analyze various scheduling algorithms that could increase performance for big data processing in Hadoop.
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.
A Review Paper on Big Data and Hadoop for Data Scienceijtsrd
Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, technqiues and frameworks. Hadoop is an open source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Mr. Ketan Bagade | Mrs. Anjali Gharat | Mrs. Helina Tandel "A Review Paper on Big Data and Hadoop for Data Science" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29816.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/29816/a-review-paper-on-big-data-and-hadoop-for-data-science/mr-ketan-bagade
How do data analysts work with big data and distributed computing frameworks.pdfSoumodeep Nanee Kundu
This document discusses how data analysts work with big data and distributed computing frameworks. It begins by defining big data and explaining the challenges it poses in terms of volume, velocity, variety and veracity. It then explores how distributed computing frameworks like Hadoop, Spark and Storm enable data analysts to process large and complex datasets in parallel across clusters of machines. The role of data analysts is described as collecting, analyzing and interpreting data to generate insights. Analysts collaborate with data engineers and scientists, using tools like Python, SQL and Tableau. Examples are given of how big data is applied in domains like healthcare, IoT and fraud detection. Case studies on Google's PageRank and Twitter analytics are also summarized.
1) The document discusses using k-means clustering to analyze big data. K-means is an algorithm that partitions data into k clusters based on similarity.
2) It provides background on big data characteristics like volume, variety, and velocity. It also discusses challenges of heterogeneous, decentralized, and evolving data.
3) The document proposes applying k-means clustering to big data to map data into clusters according to its properties in a fast and efficient manner. This allows statistical analysis and knowledge extraction from large, complex datasets.
This document summarizes a research paper on using k-means clustering to analyze big data. It begins with an introduction to big data and its characteristics. It then discusses related work on big data storage, mining, and analytics. The HACE theorem for defining big data is presented. The k-means clustering algorithm is explained as an efficient method for partitioning big data into groups. The proposed system uses k-means clustering followed by data mining and classification modules. Experimental results on two datasets show that the recursive k-means approach finds clusters closer to the actual number than the iterative approach. In conclusion, clustering is effective for handling big data attributes like heterogeneity and complexity, and k-means distribution helps distribute data into appropriate clusters.
Big Data Processing with Hadoop : A ReviewIRJET Journal
1. This document provides an overview of big data processing with Hadoop. It defines big data and describes the challenges of volume, velocity, variety and variability.
2. Traditional data processing approaches are inadequate for big data due to its scale. Hadoop provides a distributed file system called HDFS and a MapReduce framework to address this.
3. HDFS uses a master-slave architecture with a NameNode and DataNodes to store and retrieve file blocks. MapReduce allows distributed processing of large datasets across clusters through mapping and reducing functions.
This document provides a review of Hadoop storage and clustering algorithms. It begins with an introduction to big data and the challenges of storing and processing large, diverse datasets. It then discusses related technologies like cloud computing and Hadoop, including the Hadoop Distributed File System (HDFS) and MapReduce processing model. The document analyzes and compares various clustering techniques like K-means, fuzzy C-means, hierarchical clustering, and Self-Organizing Maps based on parameters such as number of clusters, size of clusters, dataset type, and noise.
The document is a project report submitted by Suraj Sawant to his college on the topic of "Map Reduce in Big Data". It discusses the objectives, introduction and importance of big data and MapReduce. MapReduce is a programming model used for processing large datasets in a distributed manner. The document provides details about the various stages of MapReduce including mapping, shuffling and reducing data. It also includes diagrams to explain the execution process and parallel processing in MapReduce.
Big Data Paradigm - Analysis, Application and ChallengesUyoyo Edosio
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1. Journal of Science and Technology (JST)
www.jst.org.in 34 | Page
Volume 2, Issue 2, March - April 2017, PP 34-40
www.jst.org.in
A Survey on Big Data Challenges In The Context Of Predictive
Analytics
M.S.Sudheer
(Assistant Professor Shri Vishnu Engineering College For Women Bhimavaram)
Abstract: Information is producing from various assets in a quick fashion. In request to know how much
information is advancing we require predictive analytics. When the information is semi organized or
unstructured the ordinary business insight calculations or instruments are not useful. In this paper, we have
attempted to call attention to the difficulties when we utilize business knowledge devices
Keywords- big data, predictive analytics, business knowledge devices
I. INTRODUCTION
These days we are confronting the issues with Big Data because of its attributes (i.e., VVVVs Volume,
Velocity, Variety and Veracity) and this information is semi organized or unstructured. Huge information by
name itself saying that it contains extensive volume of information which is hard to prepare or examine the
information with customary foundation. It is additionally hard to store that tremendous measure of information
with the conventional foundation. Because of this the adaptability issues may emerges and preparing and
investigation of that immense measure of information are the difficulties here. We can't anticipate how much
measure of information we need to aciquisit, store, prepare and break down through our conventional methods.
It is the ideal opportunity for the use of Predictive investigation which predicts the measure of information
producing from various areas. (Ex: web based business, managing an account, producing, Health part, informal
organizations). Enormous information is characterized in a few ways. It is a huge volume of information or
monstrous information or extensive volume of information. In addition it is unstructured or semi organized and
it requires all the more continuous examination. Because of the high volume of huge information additional
computational difficulties are Posed. Information preprocessing is not that much simple in huge information as
like in customary information. Assortment of huge information postures distinctive difficulties.
•Noise correspondence is high to the point that it might rule the huge information.
•Suspicious connection between's various information focuses even though there is no relationship exists
between them.
•Discrimination between the Traditional information and Big Data in light of the fact that the structure of the
huge information is semi organized or unstructured.
•Velocity of the datasets must be examined and prepared at the speed that matches with the information
Production in light of the fact that the speed with which the information comes is erratic.
•Whenever information is absolved from information producing gadgets it must be put away, changed, handled
and examination must be done on the information yet with the huge volume of the information it is impractical
to store that much measure of information with the conventional framework. With this test adaptability issue
may emerges.
The definitions by a few investigators from various associations are as per the following.
Attributive Definition
[1]In 2011 IDC characterizes enormous information as "Large information advances depicts new era of
innovations and models, intended to monetarily remove an incentive from vast volume of a wide assortment of
information, by empowering high speed catch, revelation and examination". This definition depicts about the
qualities of huge information i.e., Volume, Variety, Velocity and Value. According to the META bunch inquire
about report in 2001 information development and difficulties are in three dimensional i.e., expanding volume,
speed and assortment.
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Comparative Definition
[1] In 2011, McKinney's report characterized enormous information as "Datasets whose size is past the
capacity of run of the mill database programming apparatuses to catch, store, oversee and dissect ".
Engineering Definition
[1] The National Institute of models and Technology (NIST) characterize the enormous information as
"Large information is the place the information volume, securing speed, or information portrayal restricts the
capacity to perform powerful examination utilizing conventional social methodologies. It requires utilization of
level scaling for productive handling.
Super Byte To Giga Byte
II. HISTORY OF BIG DATA
In 1970s to 1980s the greater part of the chronicled information is put away for business investigation
that range is expanded from MB to GB. Database machine which is combination of equipment and
programming to tackle the examination issues. After some timeframe these database machines couldn't adapt up
to the information produced from the information sources.
Giga Byte to Terra Byte
In 1980s with the immense measure of information creating from the information producing gadgets.
Customary database machine couldn't deal with the information so Data parallelization was proposed to broaden
the capacity abilities, enhances the execution by circulating information on various databases. The new database
systems are 1) shared memory databases 2) Shared plate databases 3) shared nothing databases. In these three
methods shared nothing engineering was succeeded which was based on an arranged design comprises of
individual processor, memory and plate.
Land Byte to Peta Byte
Amid the late 1990s Internet period starts which comprises of parcel of unstructured or semi organized
information and it is to be questioned and ordered in a precise way however parallel databases gave the little
support for enormous information as they did not regard handle the organized information. To address the
difficulties postured by semi organized information Google made Google File framework and Map lessen
handling model which empowers programmed parallelization and appropriation of substantial scale calculation
application to expansive groups of item servers.
Peta Byte To Exa Byte
Presently the pattern of the information lies at Tera Byte to Peta Byte. It might go to exa byte soon.
Presently the present pattern of instruments can deal with the information upto Peta byte. No apparatus had been
created to adapt up to the bigger datasets.
By and by we are utilizing Map lessening worldview to prepare the monstrous datasets. Delineate is
very versatile [1] programming worldview fit for preparing gigantic datasets by parallel execution on an
extensive number of processing hubs. It was advanced by google yet now it is utilized by Apache Hadoop.
The benefits of guide lessen worldview are
1) Scalability.
2) Simplicity.
3) Fault resistance.
With the above favorable circumstances of the guide lessen there are a few obstructions are found with this
worldview.
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1) It can't manage the Big Data since it does exclude the abnormal state dialects like SQL.
2) It can't actualize the iterative calculations.
3) It can't bolster for iterative impermanent information investigation.
4) It can't deal with stream Processing.
[2] Li et al. introduced an audit of methodologies concentrated on the support of circulated information
administration and preparing utilizing MapReduce. They examined usage of database administrators in
MapReduce and DBMS executions utilizing MapReduce, while this paper is worried with distinguishing
MapReduce challenges in Big Data.
[2]Doulkeridis and Nørvåg studied the best in class in enhancing the execution of MapReduce
processingand assessed bland MapReduce shortcomings and difficulties.
[2]Sakr et likewise studied ways to deal with information handling in view of the MapReduce
worldview. Additionally, they examined frameworks which give decisive programming interfaces on top of
MapReduce.
Mapreduce Review
[2] MapReduce is a programming worldview for handling huge informational collections in dispersed
conditions. In the MapReduce worldview delineate completes separating and sorting. Lessen work completes
gathering and conglomeration operations. The Map work parts the report into words and for each word in an
archive it produces (Key, esteem) match. The Reduce capacity is in charge of amassing data got from guide
capacities.
MapReduce flow
One hub in the worldview is chosen as ace hub and its is in charge of doling out the work to the
specialists. The info information is isolated into parts and the ace hubs allocates the information to the Map
workers.[2] The guide specialist prepare the comparing split and create key/esteem combine and thinks of them
to the middle records. The ace informs the lessen specialists about the area of information and diminish laborers
read information and process as indicated by the decrease work lastly composes information to yield records.
The MapReduce execution done by the Hadoop.It actualizes on the highest point of the Hadoop disseminated
document framework.
MapReduce challenges
The primary difficulties with the MapReduce worldview are
1)Data Storage
2)Big information examination
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Information Storage
In the prior days for conventional information we have utilized RDBMS for the capacity reason. It is
not appropriate for enormous information in light of its assortment of characteristics.[1] RDBMS frameworks
confronts the difficulties when it is taking care of huge information are giving Horizontal adaptability,
accessibility and execution required by huge information applications. MapReduce gives computational
adaptability yet it relies on upon information stockpiling in an appropriated record framework like Google File
System(GFS) and Hadoop Distributed File System(HDFS).So no longer it underpins SQL.NOSQL(Not just
SQL) and NEWSQL are recently risen information stockpiling frameworks. These information stockpiling
frameworks are valuable to Big information. The fundamental Big information limitations are composition
adaptability and successful scaling over an extensive number of frameworks. The MapReduce worldview is
itself Schema free and list free which will give great outcomes when contrasted with the customary systems yet
because of the absence of files it might give poor execution when contrasted with the social databases.
The principle challenge identified with MapReduce and Data stockpiling is the absence of
institutionalized SQL-like dialect. The new research bearing is to give a SQL-like dialect on top of the Hadoop.
Another instrument Mahout intends to manufacture adaptable machine learning libraries on top of MapReduce.
This method gives intense handling abilities however it needs information administration highlights like
propelled ordering and advanced streamlining agent. Another issue is the mix between customary databases,
MapReduce and Hadoop is unrealistic.
Machine Learning
III. ENORMOUS DATA ANALYTICS
Manmade brainpower came into the presence in the year 1990. ML is a piece of AI that ceaselessly
watches a progression of activities over a timeframe and puts this learning to use by contriving approaches to
play out the comparable things in a superior way in another condition.
Arther Samuel in 1959said ML is a field of study that gives the PCs the capacity to learn without being
expressly customized. The Applications of ML in every day life are Google maps, Netflix, Applications utilized
for discourse and signal acknowledgment, Facial acknowledgment, web seek and so on. The presence of
enormous information allows to assemble more savvy basic leadership systems.ML calculations are intended to
be utilized on littler datasets with suspicion that the whole information could be in the memory. So as to address
the huge information issues machine learning calculations are not legitimate on the grounds that the information
size is not tantamount with the customary information. Some ML calculations which are characteristically
parallel are versatile to MapReduce worldview however different calculations are not in a position to deal with
the enormous information. A portion of the insufficient ML calculations are
1)Iterative Graph calculations.
2)Gradient plummet calculations.
3)Expectation amplification.
To address the weaknesses of MapReduce worldview, elective models are
•Pergel and giraph are elective models in view of Bulk synchronous parallel worldview.
•Spark is another option display in light of circulated datasets reflections which utilizes memory to refresh
shared states and gives the execution like inclination drop.
•Haloop and Twister are both expansions to Hadoop for the MapReduce execution to better bolsteriterative
calculations.
Iterative methodologies in ML calculations for enormous information have been proposed however
Integration and contrary qualities amongst apparatuses and structures are the new research openings.
Usage Challenges For ML Calculations
•Lack of mastery who applies these calculations to business issues.
•Lack of the way of life that can apply the machine learning procedure to everyday operations.
•Availability of the correct information from different operations and procedures.
•Lack of innovative skill in huge information utilizing ML calculations.
Way to deal with execution of Machine Learning Framework:
•Priotorize business challenges
•Build information foundation
•Prepare and comprehend the information
5. www.jst.org.in 38 | Page
Journal of Science and Technology
•Develop right machine learning models
•Setup the huge information stage
•Test show for constant change
•Deploy and screen arrangement
[3] The critical piece of ML calculations is Predictive Modeling. ML calculations with prescient
displaying additionally are utilized as a part of various organizations in assembling units for the blame
detachment and to anticipate the deficiencies in the framework. It is additionally one of the best savvy choices
making frameworks. MapReduce with prescient displaying has constrained value when we have related
information. It functions admirably when the information is handled independently. As large information
innovation develops organizations are pulled in towards prescient investigation to make profound engagement
with the clients, streamline forms and lessen operational expenses. Up to now Enterprises utilized Business
knowledge for upper hand for auxiliary datasets put away.
Ex: Cognos in social database administration frameworks.
Because of the heterogeneous way of enormous information associations can't adapt up to the
advancements to break down the information then prescient investigation came into picture. "Prescient
examination can be characterized as an arrangement of cutting edge advancements that empower associations to
utilize both put away and constant to move from an authentic, elucidating perspective to a forward looking point
of view of what's in store."
Prescient examination can be separated into two sorts
Table1: Applications of Predictive analytics
Industries Use cases
Energy and utilities Energy consumption patterns and management
Financial services Fraud identification , loan defaults and investment analysis
Food and beverage Supply chain demand prediction for creating, packaging and shipping
time-sensitive products.
Health care Rehospitalization and risk patterns in health-related data
Insurance Fraud identification and individualized policies based on vehicle
telemetry.
Manufacturing Quality assurance optimization and machine failure and downtime
predictions
Transportation Service and delivery route optimization
Marketing Consumer behavior prediction, churn analysis, consumption, and
propensity to spend
Travel Buying experience optimization, upselling, and customized offers and
packages.
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Journal of Science and Technology
1)Predictive investigation [4]It worries about what will happen, consider the possibility that situations and
hazard appraisal. The uses of prescient examination are Forecasting, Hypothesis testing, Risk demonstrating and
affinity displaying.
2)Prescriptive investigation It worries about what might happen in view of various choices, situations and
afterward picking best alternatives and improving again that methodology.
Ex:1) Customer cross-channel streamlining.
2) best-activity related offers.
3) Portfolio and business streamlining.
4) Risk administration.
Prescient investigation can be utilized to bolster major vital choices and furthermore for little strategy
choices. Prescient examination can be useful in CRM (Customer Relationship administration), ERP (Enterprise
Resource Planning).The underneath table demonstrates how the prescient investigation are useful in ventures.
From the businesses perspective Intel begins fabricating their items as per the Big information insight.
Intel appropriation for Apache hadoop is intended to upgrade enormous information administration and handling
on Intel design. It coordinates with
• Existing information distribution centers and enormously parallel preparing frameworks.
• Business Intelligence devices and systematic motors.
• Data devices like Extract, Transfer and Load instruments (ETL).
• Integrates with Mahout (ML Library).
• Expand explanatory capacities as required.
• Assess hazard to information security and protection.
• Develop the aptitudes to convey an incentive to the business association.
IV. WORKING OF PRESCIENT INVESTIGATION
Business Intelligence utilizes deductive techniques to investigate the information and comprehended about
the current examples and connections. Be that as it may, these deductive strategies are valuable for organized
information on the opposite side prescient investigation utilizes inductive approach for the most part worries
about the information revelation as opposed to examples and relationship between datasets. Prescient
examination utilizes techniques like machine learning, neural systems, mechanical technology and
computational knowledge. Inductive strategies utilize calculations to perform complex computations particularly
on the tremendous and shifted datasets.
Associations utilize prescient investigation for
• Decision making
• Solving business issues
• Optimizing business procedures and diminishing operational expenses
• Engaging more profound with client and improving the client encounter
• Identifying new item and market openings
• Reducing dangers by suspecting and alleviating issues before they happen
Not just the associations we have part of extension in creating prescient models for the gushing
information which is produced from sensors, value-based and web. One of the assets for enormous information
is gushing information. To create occasion recognition procedures and prescient models for blue-penciled
information is a test. Intel and different organizations they have built up some prescient explanatory motors for
producing more income and more over they had built up the systematic motors by applying some static business
controls on datasets. Our goal is attempt to build up a hearty prescient model with some dynamic learning
systems for huge information investigation.
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V. CONCLUSION
From the above difficulties we can realize that execution of business knowledge apparatuses and
calculations are restricted to the typical datasets. Moreover that a large portion of the organizations utilizing
static learning systems over their datasets. For huge information these forecast strategies with static learning
procedures not reasonable. In future we will attempt to chip away at Machine learning strategies for prescient
analytics with element learning procedures.
REFERENCES
[1] Toward Scalable Systems for Big Data Analytics: A Technology Tutorial han hu1 , yonggang wen2 , (senior member, ieee), tat-seng
chua1 , and xuelong li3 , (fellow, ieee)
[2] Challenges for MapReduce in Big Data Katarina Grolinger1 , Michael Hayes1 , Wilson A. Higashino1,2, Alexandra L'Heureux1 David
S. Allison1,3,4,5, Miriam A.M. Capretz1
[3] Predictive Analytics 101: Next-Generation Big Data Intelligence
[4]Using Big Data for Machine learning Analytics in manufacturing
[5]Using Big Data Predictive Analytics to Optimize Sales- white Paper
[6]Open Challenges for Data Stream Mining Research
[7]Twitter Analytics: A Big Data Management Perspective Oshini Goonetilleke†, Timos Sellis†, Xiuzhen Zhang†, Saket Sathe§
[8]What is Tumblr: A Statistical Overview and Comparison Yi Chang†, Lei Tang§, Yoshiyuki Inagaki†, Yan Liu‡
[9] Change Detection in Streaming Data in the Era of Big Data: Models andIssues
[10] Big Data Analytics with Hadoop to analyze Targeted Attacks on Enterprise Data Bhawna Gupta Dr. Kiran Jyoti
Mr. M. S. Sudheer is a Faculty of Shri Vishnu Engineering College For Women
Bhimavaram. He received his graduation from JNTUK University In the year 2010
and Post Graduation from JNTUK in the year 2012. His research interests are Cloud
Computing, Big data Analytics, Wireless Sensor Networks.