Big Data Analytic tools are promising techniques for a future prediction in many aspects of our life. The need for such predictive techniques has been exponentially increasing. even though, there are many challenges and risks are still of concern of researchers and decision makers, the outcome from the use of these techniques will considerable revolutionize our world to a new era of technology.
This document provides an overview of big data, including its definition, characteristics, examples, analysis methods, and challenges. It discusses how big data is characterized by its volume, variety, and velocity. Examples of big data are given from various industries like healthcare, retail, manufacturing, and web/social media. Analysis methods for big data like MapReduce, Hadoop, and HPCC are described and compared. The document also covers privacy and security issues that arise from big data analytics.
Implementation of application for huge data file transferijwmn
Nowadays big data transfers make people’s life difficult. During the big data transfer, people waste so
much time. Big data pool grows everyday by sharing data. People prefer to keep their backups at the cloud
systems rather than their computers. Furthermore considering the safety of cloud systems, people prefer to
keep their data at the cloud systems instead of their computers. When backups getting too much size, their
data transfer becomes nearly impossible. It is obligated to transfer data with various algorithms for moving
data from one place to another. These algorithms constituted for transferring data faster and safer. In this
Project, an application has been developed to transfer of the huge files. Test results show its efficiency and
success.
This document discusses uncertainty in big data analytics. It begins by providing background on big data, defining the common "5 V's" characteristics of big data - volume, variety, velocity, veracity, and value. It then discusses uncertainty, which exists in big data due to noise, incompleteness, and inconsistency in data. The document surveys techniques for big data analytics and how uncertainty impacts machine learning, natural language processing, and other artificial intelligence approaches. It identifies challenges that uncertainty presents and strategies for mitigating uncertainty in big data analytics.
Full Paper: Analytics: Key to go from generating big data to deriving busines...Piyush Malik
This document discusses how analytics can help organizations derive business value from big data. It describes how statistical analysis, machine learning, optimization and text mining can extract meaningful insights from social media, online commerce, telecommunications, smart utility meters, and improve security. While tools exist to analyze big data, challenges remain around data security, privacy, and developing skilled talent. The paper aims to illustrate how existing algorithms can generate value from different industry use cases.
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.
The Pew Research Center’s Internet & American Life Project and Elon University’s Imagining the Internet Center asked digital stakeholders to weigh two scenarios for 2020, select the one most likely to evolve, and elaborate on the choice. One sketched out a relatively positive future where Big Data are drawn together in ways that will improve social, political, and economic intelligence. The other expressed the view that Big Data could cause more problems than it solves between now and 2020
This document provides an overview of predictive analytics and its growing importance. It discusses how advances in technologies like cloud computing and the internet of things are enabling businesses to gather and analyze vast amounts of data. While descriptive and diagnostic analytics describe what happened in the past, predictive analytics uses statistical techniques to create models that forecast future outcomes. The document outlines several key drivers that are pushing predictive analytics towards mainstream adoption over the next few years, including easier-to-use tools, open source software, innovation from startups, and the availability of cloud-based solutions. It concludes that the combination of big data and predictive analytics will continue to accelerate innovation across industries.
Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components.
DOI: 10.13140/RG.2.2.12784.00004
This document provides an overview of big data, including its definition, characteristics, examples, analysis methods, and challenges. It discusses how big data is characterized by its volume, variety, and velocity. Examples of big data are given from various industries like healthcare, retail, manufacturing, and web/social media. Analysis methods for big data like MapReduce, Hadoop, and HPCC are described and compared. The document also covers privacy and security issues that arise from big data analytics.
Implementation of application for huge data file transferijwmn
Nowadays big data transfers make people’s life difficult. During the big data transfer, people waste so
much time. Big data pool grows everyday by sharing data. People prefer to keep their backups at the cloud
systems rather than their computers. Furthermore considering the safety of cloud systems, people prefer to
keep their data at the cloud systems instead of their computers. When backups getting too much size, their
data transfer becomes nearly impossible. It is obligated to transfer data with various algorithms for moving
data from one place to another. These algorithms constituted for transferring data faster and safer. In this
Project, an application has been developed to transfer of the huge files. Test results show its efficiency and
success.
This document discusses uncertainty in big data analytics. It begins by providing background on big data, defining the common "5 V's" characteristics of big data - volume, variety, velocity, veracity, and value. It then discusses uncertainty, which exists in big data due to noise, incompleteness, and inconsistency in data. The document surveys techniques for big data analytics and how uncertainty impacts machine learning, natural language processing, and other artificial intelligence approaches. It identifies challenges that uncertainty presents and strategies for mitigating uncertainty in big data analytics.
Full Paper: Analytics: Key to go from generating big data to deriving busines...Piyush Malik
This document discusses how analytics can help organizations derive business value from big data. It describes how statistical analysis, machine learning, optimization and text mining can extract meaningful insights from social media, online commerce, telecommunications, smart utility meters, and improve security. While tools exist to analyze big data, challenges remain around data security, privacy, and developing skilled talent. The paper aims to illustrate how existing algorithms can generate value from different industry use cases.
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.
The Pew Research Center’s Internet & American Life Project and Elon University’s Imagining the Internet Center asked digital stakeholders to weigh two scenarios for 2020, select the one most likely to evolve, and elaborate on the choice. One sketched out a relatively positive future where Big Data are drawn together in ways that will improve social, political, and economic intelligence. The other expressed the view that Big Data could cause more problems than it solves between now and 2020
This document provides an overview of predictive analytics and its growing importance. It discusses how advances in technologies like cloud computing and the internet of things are enabling businesses to gather and analyze vast amounts of data. While descriptive and diagnostic analytics describe what happened in the past, predictive analytics uses statistical techniques to create models that forecast future outcomes. The document outlines several key drivers that are pushing predictive analytics towards mainstream adoption over the next few years, including easier-to-use tools, open source software, innovation from startups, and the availability of cloud-based solutions. It concludes that the combination of big data and predictive analytics will continue to accelerate innovation across industries.
Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components.
DOI: 10.13140/RG.2.2.12784.00004
Identifying and analyzing the transient and permanent barriers for big datasarfraznawaz
The document discusses identifying and analyzing the transient and permanent barriers for adopting big data. It begins by providing background on big data and its opportunities. It then identifies five transient barriers: data storage and transfer, scalability, data quality, data complexity, and timeliness. The barriers are analyzed in depth. Four permanent barriers are also identified: security, privacy, trust, data ownership, and transparency. The barriers are discussed and the challenges of overcoming the permanent barriers through technology alone are noted.
Efficient Data Filtering Algorithm for Big Data Technology in Telecommunicati...Onyebuchi nosiri
Efficient data filtering algorithm for Big Data technology Telecommunication is a concept aimed at effectively filtering desired information for preventive purposes, the challenges posed by unprecedented rise in volume, variety and velocity of information has necessitated the need for exploring various methods Big Data which is simply a data sets that are so large and complex that traditional data processing tools and technologies cannot cope with is been considered. The process of examining such data to uncover hidden patterns in them was evolved, this was achieved by coming up with an Algorithm comprising of various stages like Artificial neural Network, Backtracking Algorithm, Depth First Search, Branch and Bound and dynamic programming and error check. The algorithm developed gave rise to the flowchart, with each line of block representing a sub-algorithm.
Efficient Data Filtering Algorithm for Big Data Technology in Telecommunicati...Onyebuchi nosiri
This document summarizes an algorithm for efficiently filtering big data in telecommunications networks. It begins by introducing the challenges of unprecedented rises in data volume, variety, and velocity. It then describes an algorithm developed comprising stages like artificial neural networks and graph search methods. The algorithm is represented as a flowchart to filter data for preventative purposes like detecting criminal activity. Overall, the algorithm aims to effectively uncover patterns in large, complex datasets to help telecommunications providers address big data challenges.
World Wide Web plays an important role in providing various knowledge sources to the world, which helps many applications to provide quality service to the consumers. As the years go on the web is overloaded with lot of information and it becomes very hard to extract the relevant information from the web. This gives way to the evolution of the Big Data and the volume of the data keeps increasing rapidly day by day. Data mining techniques are used to find the hidden information from the big data. In this paper we focus on the review of Big Data, its data classification methods and the way it can be mined using various mining methods.
Impact of big data congestion in IT: An adaptive knowledgebased Bayesian networkIJECEIAES
Recent progress on real-time systems are growing high in information technology which is showing importance in every single innovative field. Different applications in IT simultaneously produce the enormous measure of information that should be taken care of. In this paper, a novel algorithm of adaptive knowledge-based Bayesian network is proposed to deal with the impact of big data congestion in decision processing. A Bayesian system show is utilized to oversee learning arrangement toward all path for the basic leadership process. Information of Bayesian systems is routinely discharged as an ideal arrangement, where the examination work is to find a development that misuses a measurably inspired score. By and large, available information apparatuses manage this ideal arrangement by methods for normal hunt strategies. As it required enormous measure of information space, along these lines it is a tedious method that ought to be stayed away from. The circumstance ends up unequivocal once huge information include in hunting down ideal arrangement. A calculation is acquainted with achieve quicker preparing of ideal arrangement by constraining the pursuit information space. The proposed algorithm consists of recursive calculation intthe inquiry space. The outcome demonstrates that the ideal component of the proposed algorithm can deal with enormous information by processing time, and a higher level of expectation rates.
Big data is generated from various sources producing huge volumes of data every minute, including blog posts, YouTube videos, searches, and social media activity. Big data can provide businesses several benefits like instant insights, improved analytics, vast data management, and better decision making. It allows understanding customers better, reducing costs, and increasing operating margins. Big data has applications in many industries like banking for fraud detection, healthcare for personalized medicine, retail for inventory optimization, and transportation for traffic control. Government uses it for claims processing and insurance uses it for customer insights, pricing, and fraud detection.
The document discusses big data challenges faced by organizations. It identifies several key challenges: heterogeneity and incompleteness of data, issues of scale as data volumes increase, timeliness in processing large datasets, privacy concerns, and the need for human collaboration in analyzing data. The document describes surveying various organizations in Pakistan, including educational institutions, telecommunications companies, hospitals, and electrical utilities, to understand the big data problems they face. Common challenges included data errors, missing or incomplete data, lack of data management tools, and issues integrating different data sources. The survey found that while some organizations used big data tools, many educational institutions in particular did not, limiting their ability to effectively manage and analyze their large and growing datasets.
IRJET - Big Data Analysis its ChallengesIRJET Journal
This document discusses big data analysis and its challenges. It begins by defining big data and business analytics, noting that large amounts of data are now being generated daily that require new techniques to analyze. It describes some of the key challenges in handling big data, including issues around storage, analysis, and reporting on large, complex datasets. The document then discusses the four Vs of big data - volume, variety, velocity, and veracity. It concludes by noting limitations in current research and opportunities for future work to better understand the impacts of big data and business analytics on competitive advantages.
Big data refers to extremely large data sets that traditional data processing systems cannot handle. Big data is characterized by high volume, velocity, and variety of data. Hadoop is an open-source software framework that allows distributed storage and processing of big data across clusters of computers. A key component of Hadoop is MapReduce, a programming model that enables parallel processing of large datasets. MapReduce allows programmers to break problems into independent pieces that can be processed simultaneously across distributed systems.
Big data refers to extremely large data sets that are too large to be processed using traditional data processing applications. It is characterized by high volume, variety, and velocity. Examples of big data sources include social media, jet engines, stock exchanges, and more. Big data can be structured, unstructured, or semi-structured. Key characteristics include volume, variety, velocity, and variability. Analyzing big data can provide benefits like improved customer service, better operational efficiency, and more informed decision making for organizations in various industries.
This document discusses data quality and why it is important. It begins by defining what high quality data is, noting that data should be "fit for use" and conform to standards. It then discusses five key aspects of data quality - relevance, accuracy, timeliness, comparability, and completeness. The document explains that there are three ways to obtain high quality data: prevention, detection, and repair, but prevention is most effective. It provides a practical example of making a customer database "fit for use" by developing clear requirements and procedures.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today
communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
This document discusses how life insurance companies can leverage big data analytics across their value chain. It begins by explaining how data sources have expanded dramatically in recent years due to factors like the growth of digital devices and the internet of things. It then outlines how big data can be used in various parts of the insurance lifecycle from product development to claims processing. The document presents a four stage framework for life insurers to adopt big data analytics and provides examples of how some companies have realized benefits. It concludes by noting that while insurers recognize big data's potential, many challenges remain in analyzing diverse and voluminous unstructured data.
Big Data refers to large, complex datasets that traditional data processing applications are unable to handle efficiently. Spark is a fast, general engine for large-scale data processing that supports multiple languages and data sources. Spark uses resilient distributed datasets (RDDs) that operate on data stored in cluster memory for faster performance compared to the disk-based MapReduce model. DataFrames provide a distributed collection of data organized into named columns similar to a relational database, enabling SQL-like queries and optimizations.
The document discusses big data, including its definition, types, benefits, and challenges. It describes how big data is generated from a variety of sources and is characterized by its volume, velocity, and variety (the 3Vs). Big data provides benefits like improved customer insights and business optimization. However, it also poses challenges to deal with its huge volume, high velocity, varied types (structured and unstructured), and issues of data veracity (uncertainty). Techniques to address these challenges include using distributed file systems, parallel processing frameworks like Hadoop, and data fusion or advanced mathematics to manage uncertainty.
This document provides information about big data analytics. It defines what data and big data are, explaining that big data refers to extremely large data sets that are difficult to process using traditional data management tools. It discusses the volume, variety, velocity, and veracity characteristics of big data. Examples of big data sources and sizes are provided, such as the terabytes of data generated each day by the New York Stock Exchange and Facebook. The document also covers structured, unstructured, and semi-structured data types; advantages of big data processing; and types of digital advertising.
Encroachment in Data Processing using Big Data TechnologyMangaiK4
Abstract—The nature of big data is now growing and information is present all around us in different kind of forms. The big data information plays crucial role and it provides business value for the firms and its benefits sectors by accumulating knowledge. This growth of big data around all the concerns is high and challenge in data processing technique because it contains variety of data in enormous volume. The tools which are built on the data mining algorithm provides efficient data processing mechanisms, but not fulfill the pattern of heterogeneous, so the emerging tools such like Hadoop MapReduce, Pig, SPARK, Cloudera, Impala and Enterprise RTQ, IBM Netezza and Apache Giraphe as computing tools and HBase, Hive, Neo4j and Apache Cassendra as storage tools useful in classifying, clustering and discovering the knowledge. This study will focused on the comparative study of different data processing tools, in big data analytics and their benefits will be tabulated.
Big data refers to large and complex datasets that are difficult to process using traditional data processing methods. This document discusses the characteristics of big data including volume, variety, velocity, and variability. It provides examples of big data sources like weather data, contracts, financial reports, and clinical trials data. The advantages of big data include unlimited storage and high processing speeds while disadvantages include noise in the data and privacy/security issues. Finally, applications of big data are described across various industries like banking, healthcare, manufacturing, government, retail, transportation, and energy.
Identifying and analyzing the transient and permanent barriers for big datasarfraznawaz
The document discusses identifying and analyzing the transient and permanent barriers for adopting big data. It begins by providing background on big data and its opportunities. It then identifies five transient barriers: data storage and transfer, scalability, data quality, data complexity, and timeliness. The barriers are analyzed in depth. Four permanent barriers are also identified: security, privacy, trust, data ownership, and transparency. The barriers are discussed and the challenges of overcoming the permanent barriers through technology alone are noted.
Efficient Data Filtering Algorithm for Big Data Technology in Telecommunicati...Onyebuchi nosiri
Efficient data filtering algorithm for Big Data technology Telecommunication is a concept aimed at effectively filtering desired information for preventive purposes, the challenges posed by unprecedented rise in volume, variety and velocity of information has necessitated the need for exploring various methods Big Data which is simply a data sets that are so large and complex that traditional data processing tools and technologies cannot cope with is been considered. The process of examining such data to uncover hidden patterns in them was evolved, this was achieved by coming up with an Algorithm comprising of various stages like Artificial neural Network, Backtracking Algorithm, Depth First Search, Branch and Bound and dynamic programming and error check. The algorithm developed gave rise to the flowchart, with each line of block representing a sub-algorithm.
Efficient Data Filtering Algorithm for Big Data Technology in Telecommunicati...Onyebuchi nosiri
This document summarizes an algorithm for efficiently filtering big data in telecommunications networks. It begins by introducing the challenges of unprecedented rises in data volume, variety, and velocity. It then describes an algorithm developed comprising stages like artificial neural networks and graph search methods. The algorithm is represented as a flowchart to filter data for preventative purposes like detecting criminal activity. Overall, the algorithm aims to effectively uncover patterns in large, complex datasets to help telecommunications providers address big data challenges.
World Wide Web plays an important role in providing various knowledge sources to the world, which helps many applications to provide quality service to the consumers. As the years go on the web is overloaded with lot of information and it becomes very hard to extract the relevant information from the web. This gives way to the evolution of the Big Data and the volume of the data keeps increasing rapidly day by day. Data mining techniques are used to find the hidden information from the big data. In this paper we focus on the review of Big Data, its data classification methods and the way it can be mined using various mining methods.
Impact of big data congestion in IT: An adaptive knowledgebased Bayesian networkIJECEIAES
Recent progress on real-time systems are growing high in information technology which is showing importance in every single innovative field. Different applications in IT simultaneously produce the enormous measure of information that should be taken care of. In this paper, a novel algorithm of adaptive knowledge-based Bayesian network is proposed to deal with the impact of big data congestion in decision processing. A Bayesian system show is utilized to oversee learning arrangement toward all path for the basic leadership process. Information of Bayesian systems is routinely discharged as an ideal arrangement, where the examination work is to find a development that misuses a measurably inspired score. By and large, available information apparatuses manage this ideal arrangement by methods for normal hunt strategies. As it required enormous measure of information space, along these lines it is a tedious method that ought to be stayed away from. The circumstance ends up unequivocal once huge information include in hunting down ideal arrangement. A calculation is acquainted with achieve quicker preparing of ideal arrangement by constraining the pursuit information space. The proposed algorithm consists of recursive calculation intthe inquiry space. The outcome demonstrates that the ideal component of the proposed algorithm can deal with enormous information by processing time, and a higher level of expectation rates.
Big data is generated from various sources producing huge volumes of data every minute, including blog posts, YouTube videos, searches, and social media activity. Big data can provide businesses several benefits like instant insights, improved analytics, vast data management, and better decision making. It allows understanding customers better, reducing costs, and increasing operating margins. Big data has applications in many industries like banking for fraud detection, healthcare for personalized medicine, retail for inventory optimization, and transportation for traffic control. Government uses it for claims processing and insurance uses it for customer insights, pricing, and fraud detection.
The document discusses big data challenges faced by organizations. It identifies several key challenges: heterogeneity and incompleteness of data, issues of scale as data volumes increase, timeliness in processing large datasets, privacy concerns, and the need for human collaboration in analyzing data. The document describes surveying various organizations in Pakistan, including educational institutions, telecommunications companies, hospitals, and electrical utilities, to understand the big data problems they face. Common challenges included data errors, missing or incomplete data, lack of data management tools, and issues integrating different data sources. The survey found that while some organizations used big data tools, many educational institutions in particular did not, limiting their ability to effectively manage and analyze their large and growing datasets.
IRJET - Big Data Analysis its ChallengesIRJET Journal
This document discusses big data analysis and its challenges. It begins by defining big data and business analytics, noting that large amounts of data are now being generated daily that require new techniques to analyze. It describes some of the key challenges in handling big data, including issues around storage, analysis, and reporting on large, complex datasets. The document then discusses the four Vs of big data - volume, variety, velocity, and veracity. It concludes by noting limitations in current research and opportunities for future work to better understand the impacts of big data and business analytics on competitive advantages.
Big data refers to extremely large data sets that traditional data processing systems cannot handle. Big data is characterized by high volume, velocity, and variety of data. Hadoop is an open-source software framework that allows distributed storage and processing of big data across clusters of computers. A key component of Hadoop is MapReduce, a programming model that enables parallel processing of large datasets. MapReduce allows programmers to break problems into independent pieces that can be processed simultaneously across distributed systems.
Big data refers to extremely large data sets that are too large to be processed using traditional data processing applications. It is characterized by high volume, variety, and velocity. Examples of big data sources include social media, jet engines, stock exchanges, and more. Big data can be structured, unstructured, or semi-structured. Key characteristics include volume, variety, velocity, and variability. Analyzing big data can provide benefits like improved customer service, better operational efficiency, and more informed decision making for organizations in various industries.
This document discusses data quality and why it is important. It begins by defining what high quality data is, noting that data should be "fit for use" and conform to standards. It then discusses five key aspects of data quality - relevance, accuracy, timeliness, comparability, and completeness. The document explains that there are three ways to obtain high quality data: prevention, detection, and repair, but prevention is most effective. It provides a practical example of making a customer database "fit for use" by developing clear requirements and procedures.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today
communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
This document discusses how life insurance companies can leverage big data analytics across their value chain. It begins by explaining how data sources have expanded dramatically in recent years due to factors like the growth of digital devices and the internet of things. It then outlines how big data can be used in various parts of the insurance lifecycle from product development to claims processing. The document presents a four stage framework for life insurers to adopt big data analytics and provides examples of how some companies have realized benefits. It concludes by noting that while insurers recognize big data's potential, many challenges remain in analyzing diverse and voluminous unstructured data.
Big Data refers to large, complex datasets that traditional data processing applications are unable to handle efficiently. Spark is a fast, general engine for large-scale data processing that supports multiple languages and data sources. Spark uses resilient distributed datasets (RDDs) that operate on data stored in cluster memory for faster performance compared to the disk-based MapReduce model. DataFrames provide a distributed collection of data organized into named columns similar to a relational database, enabling SQL-like queries and optimizations.
The document discusses big data, including its definition, types, benefits, and challenges. It describes how big data is generated from a variety of sources and is characterized by its volume, velocity, and variety (the 3Vs). Big data provides benefits like improved customer insights and business optimization. However, it also poses challenges to deal with its huge volume, high velocity, varied types (structured and unstructured), and issues of data veracity (uncertainty). Techniques to address these challenges include using distributed file systems, parallel processing frameworks like Hadoop, and data fusion or advanced mathematics to manage uncertainty.
This document provides information about big data analytics. It defines what data and big data are, explaining that big data refers to extremely large data sets that are difficult to process using traditional data management tools. It discusses the volume, variety, velocity, and veracity characteristics of big data. Examples of big data sources and sizes are provided, such as the terabytes of data generated each day by the New York Stock Exchange and Facebook. The document also covers structured, unstructured, and semi-structured data types; advantages of big data processing; and types of digital advertising.
Encroachment in Data Processing using Big Data TechnologyMangaiK4
Abstract—The nature of big data is now growing and information is present all around us in different kind of forms. The big data information plays crucial role and it provides business value for the firms and its benefits sectors by accumulating knowledge. This growth of big data around all the concerns is high and challenge in data processing technique because it contains variety of data in enormous volume. The tools which are built on the data mining algorithm provides efficient data processing mechanisms, but not fulfill the pattern of heterogeneous, so the emerging tools such like Hadoop MapReduce, Pig, SPARK, Cloudera, Impala and Enterprise RTQ, IBM Netezza and Apache Giraphe as computing tools and HBase, Hive, Neo4j and Apache Cassendra as storage tools useful in classifying, clustering and discovering the knowledge. This study will focused on the comparative study of different data processing tools, in big data analytics and their benefits will be tabulated.
Big data refers to large and complex datasets that are difficult to process using traditional data processing methods. This document discusses the characteristics of big data including volume, variety, velocity, and variability. It provides examples of big data sources like weather data, contracts, financial reports, and clinical trials data. The advantages of big data include unlimited storage and high processing speeds while disadvantages include noise in the data and privacy/security issues. Finally, applications of big data are described across various industries like banking, healthcare, manufacturing, government, retail, transportation, and energy.
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...IJSCAI Journal
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
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researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
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Al-Khouri, A.M. (2014) "Privacy in the Age of Big Data: Exploring the Role of Modern Identity Management Systems". World Journal of Social Science, Vol. 1, No. 1, pp. 37-47.
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Big data analytics and its impact on internet users
1. Big Data Analytics
and Its Impact on Internet Users
Salaheddin Khiri M.Beskri1, Sharafaldeen Mohamed Ashoury 2
UniversityUtara Malaysia
Email Address
1
Eng.salaheddin@Gmail.com
2
shraf82@Gmail.com
I. INTRODUCTION
In the new era of globalization, the amount of digital data
has been tremendously bursting and that, consequently, has
made the process of analyzing it a serious challenge when it
comes to productivity growth, innovation and consumer
surplus whereby they rely on data analysis to make a right
decision. Big data has become 21th century challenge, and it
consists of verities of imperfect complex and unstructured
data. It’s development communities’ and policymakers’
concern to figure out a solution for this matter. Big data
doesn’t mean only how much data in the store. Big Data is
about predicting current and future issues in all aspects of life,
to answer questions that used to be considered out of reach.
Big Data is a combination of 3 components (Three V’s), as
shown in Fig. 1, namely:
1) Volume: the incoming amount of data expedites
exponentially in terms of size to the Petabytes1. Smart meters
and heavy industrial equipment like machine sensors generate
similar data volumes as well as social media and millions of
millions of other traditional databases.
2) Variety: data is generated in different formats from
different sources (e.g. Images, Audio, Video and Texts) and
that reflects one of Big Data characteristics.
3) Velocity: it is about the speed of how much data
incomes. Different sources with different speeds of incoming
data from outer objects makes data becomes bigger faster and
harder to be analysed.
Fig. 1 A Big Data Characteristic
1
1 Petabyte= 1,000,000 Megabytes
II. TYPE OF BIG DATA
There are various sources that data is flown and generated
from through different types of channels every second.
Weblogs, social media, email, sensors, photographs and
transactional data are some examples of type of big data and
they are classified, as follows [5] :
Traditional enterprise data: including ERP databases and
traditional information systems.
Machine-generated /sensor data: surveillance cameras,
industrial systems, weblogs.
Social data: consumers’ comments, Twitter, social media
platforms like Facebook, Space, Instagram.
However, Multimedia contents have played a main role in
the data’s extreme growth. Each second of high definition
video, for instance, adds 2000 times as many bytes as a page
of text [4]. In addition, social media (e.g. Facebook), since the
rate of new users per month has reached to 100,000 users in
2012 [3], lots of various pieces of data have been uploaded to
the internet and shared between users globally. Also, medical
images, maps, video files, data that is stored in data
warehouses are other types of Big Data.
In other words, big data is can be in any form of data,
namely structured and unstructured data such pictures, audio
video files, text, and log files and many more.
III. BENEFITS OF BIG DATA
Our world encounters everyday new issues, in terms of
digital world and numerous techniques are invented to
overcome those issues to benefit our digital world in order to
achieve its goals. Big Data, as a solution, will bring in
numerous opportunities to our world in different aspects of
life.
Basically, advanced analytics enables you to create and
develop models that can be used to predict answers for many
critical questions. For instance, a developed model for
statistics that consists of combination of consumer buying
behaviour with consumer profiles can then utilized to predict
future behaviour of consumers.
Companies that struggle everyday to maintain their
competitive advantage in the market are the ones who will
definitely benefit from Big Data in many ways [6] such as:
2. A. Detect, prevent and remediate financial fraud.
Big Data is used by many organizations to reveal and
indicate any attempts or criminal fraud that are trying to harm
their networking system and servers and also predicts most
likely future attacks against their systems. So that,
organizations have embraced the most powerful analytics
technique.
B. Maintaining customer life time value
Marketing campaigns are mainly used by companies to
gain customers value and to avoid losses. Financial services
companies operate and run campaigns to billions of potential
customers. Therefore the amount of information is
tremendously increasing and the attempts to process data with
traditional tools have become failure. Consequently, that
failure limits companies’ businesses growth since customers
lifetime value is short and limited
Businesses managers have turned into high-performance
analytics. Tremendous gains institutions have achieved by
developing and compressing their analytic model and
implementing further validation for the variables in the model
to obtain greater reliability in their models [6].
C. Improve delinquent collections.
Prepaid phone services are widely spread among consumers
around the world. In the US mobile telecom market, post-pay
phone services are dominant among Americans. So, that will
impose telecom companies to conduct an exhaustive search to
trace consumers’ debts. The term high-performance analytics,
again, changed the way in which the US mobile telecom
companies calculate and determine how much credits
consumers owe them and how much credits left [6].
IV. CHALLENGES IN BIG DATA ANALYTICS
In the past, the amount of data wasn’t that huge. Variety,
velocity and volume were never a serious issue when it comes
to data process and analysis. Nowadays, new data types are
founded and used in our systems. Besides, the amount of data
income has been tremendously increasing and that resulted in
huge size of repositories need to be processed. Moreover,
decision makers have been looking for techniques to process
and analyse their data and that, consequently, led to the use of
Big Data analytics.
To utilize Big Data analytics, organizations should take
major challenges into account prior to Big Data analytics
techniques deployment. Whereby, there are many challenges
organizations could encounter in dealing with big data,
though, in below discussions, the main challenges are
highlighted and explored, as followings:
A. Internet Users privacy.
Privacy is the most sensitive issue for organizations as well
as for people around the world. “Because privacy is a pillar of
democracy, we must remain alert to the possibility that it
might be compromised by the rise of new technologies, and
put in place all necessary safeguards.” [3]. Social networks
and mobile phones have been widely used to exchange
information and that is likely to result in misusing that
information spontaneously or intently by others.
B. Data Acquisition and sharing
Backing up data in magnetic tapes and keeping it in a secret
store, will exhaust the operation of accessing the data. “An
Indonesian mobile carrier estimated that it would take up to
half a day of work to extract one day’s worth of backup data
currently stored on magnetic tapes.” [3]. After all, the stored
data cannot be accessed or transferred.
In addition, due to seeking for a success in businesses and
to achieve competitive advantage, companies always look for
the right partners to compete their rivals and bring in a success
to their businesses. For being a partner, private data should be
shared and exchanged between partners and these are two
issues have be secured during the engagement period to fulfill
the promise namely, reliable access to data streams and get
access to back up data for retrospective analysis and data
training purposes. Also, inter-comparability of data and interoperability of systems are other technical issues that are
encountered in accessing and sharing data but they are less
problematic than obtaining access or license to access data by
partners [3].
C. Data analysis.
The analysis type that was implemented and the type of
decision that is going to be informed are main variables that
revolutionize the relevance and severity of the number of
analytical challenges. In science research, scientists build their
decision on a collected data from different sources and,
moreover, policymakers may ask a question such as what is
data telling us? So that, the answer will tell them either change
the companies policies or not.
The human analyst’s input is critical whether it is fabricated
or real. In many cases, decisions have gone mistakenly
incorrect because of data analysis mismatch. A good example
is Google Flu Trends, whose ability to “detect influenza
epidemics in areas with a large population of web search
users” was. Data were compared by a group of medical
experts from Google Flu Trends from 2003 to 2008 used data
from two different networks2 and found that the Google Flu
Trends researchers did not predict actual flu very well even
though “they did a very good job at predicting nonspecific
respiratory illnesses (bad colds and other infections like
SARS) that seem like the flu. The mismatch was due to the
presence of infections causing symptoms that resemble those
of influenza, and the fact that influenza is not always
associated with influenza-like symptoms” [3].
2
The CDC's influenza-like-illness surveillance network and the CDC's
virologic surveillance system.
3. V. BIG DATA ANALYTICS RISKS
A. Data Privacy
In fact, Data acquired from different sources to be analysed
belongs to Internet users. Knowingly or unknowingly
institutions use Internet user’s public and private data to make
bad or good decisions.
B. Making False Decisions
In addition, Big Data analytics’ results are predictions. Any
failure in analysing data or mistake throughout a process of
Big Data analysis will result in false decisions. Thereby,
institutions takes false actions based on false decisions.
Even though results were achieved and critical questions
are answered, the need for results verification is still and
critical step in the whole process. Decision makers do not
want to dare risking their businesses by relying on unverified
results as a matter of fact. So that, data scientists review the
whole process of the knowledge discovery phase by retracing
the used methods, understanding the results and critically
undergo the analysis into quality’s tests.
C. Over-dependence on data
Big Data Analytics are just predictions. Therefore, treating
results as necessity may help institution avoid lots of losses.
Big Data Analytic’s processes require talent, lots of work and
validation. In other words, it’s an iterative process and the
need for other analytics’ techniques is necessary.
VI. BIG DATA ANALYTIC PROCESS
Since Big Data consists of tremendous amount of data
whereby traditional processors cannot handle it, big data
analytic process must be carried out into two phases [2], as
shown in Fig.2.
A. Knowledge Discover
In this phase, data has to be undergone certain preprocesses to make it meaningfully coherent. There are 5 steps
an organization should go through before proceeding to the
second phase, namely:
1) Acquisition:
Acquiring data needed to be analysed from different
repositories is the first step. In order to acquire the data, an
access to information and also methods to gather the data are
required. Tracking websites, machine sensors, and system or
application log files and writing inquiries to a search engine.
In some cases, data from external sources of an institution
might be required as well.
2) Pre-processing
To achieve trustworthy and useful results, data has to be
organized and classified based on its format.
3) Integration
In this step, data is completely retrieved and organized.
Whereby, redundant and clustering data are eliminated and
data becomes smaller representative sample.
4) Analysis
In the analysis step, data is analysed by describing and
predicting broad trends. In addition, researchers start
searching for relationships among data looking for answers for
their questions. Answers could be factors such as customers’
tendency in buying mobile phone.
5) Interpretation
Fig. 2 Big Data Analytic Processes
B. Application implementation
In this phase, after the data has went through series of
processes with help of certain algorithms, Data, in this phase,
are fed into an application owned by an institution to
determine what and how the institution should act. For
instance, It may predict the customer behaviour while buying
their products. It may predict what shops or market they prefer
and what may buy. Locations also can be predicted while
travelling or driving on a road. So that, based on that,
companies decide the next and proper actions to increase
customer value lifetime and draw customers’ attentions to
their products . In one word, the institution reaps the benefits
in this phase.
VII.
TECHNIQUE/APPROACH TO OVERCOME THE
CHALLENGES
Consumers are mainly affected by the all above discussed
issues whereby businesses architectures consists of private
information about their stakeholders and the privacy is
growing as the value of big data becomes more apparent. On
the hand, organizations that would like to deliver the value of
big data have to adopt a flexible multidisciplinary approach.
Thus, there are verities of techniques and approaches have
been developed by either academics or companies to analyse,
manage, gather and represent data visually. Below are some of
techniques and solutions, which are deployed by [1], for the
discussed challenges and issues, namely:
4. A. NOSQL Databases
NOSQL databases are segregated from Structured Query
Language (SQL) that relational databases (RDBMS) use. SQL
is a complementary for relational databases and is considered
as the domain-specific language for ad hoc queries, whereas
non-relational databases can use whatever they want because
SQL is not included and it can be, if needed, included [1].
Relational databases can’t maintain its performance when it
comes to a tremendous amount of data and lots of transactions
in a very small time unit
However, No-SQL databases have created a divided
solutions consisted of [1]:
1) Not Only SQL (NoSQL) solutions:
2) SQL solutions
NoSQL and SQL solutions have been combined to achieve
the highest performance at processing transactions. Whereby,
institution can maintain privacy. “Oracle corporation’
solutions, for instance, implemented these techniques in its
solutions for enterprises and successfully met all the
challenges” [1], as shown in Fig. 3.
research to collect data is another added costs for institutions
whereas using the Ineternet to elicit customers feedback from
various and active communities reduces the amount of time
and that consequently will reduce the staffing costs and
research expenses.
However, all of the techniques that we’ve listed are part of
numerous techniques that can be applied to big data.
VIII.
BIG DATA ANALYTIC AND ITS SERIOUS IMPACTS ON
THE INTERNET USERS
Data is manmade. Every day billions of new data are
entered to the Internet from different sources beginning with
social media, which is the most used source, ending with
machine sensors (e.g. Surveillance Cameras). Therefore, Big
Data Analytics definitely impact the Internet users as a bottom
line for companies, for instance. Internet users can be
influenced by this technique in many way namely:
• In marketing district: Companies have been looking for
the best ways to sell their products using advertisements and
the Internet has become the most targeted place for
advertising their products and services. So that, Big Data
Analytics have given another reason for companies to mainly
use the Internet as a medium to reach the bottom lines. A
pregnant who is walking on a street passing a baby affairs’
shop might receive a message on her phone using GPS from a
shop advertising their products since that woman is considered
as a potential customer [3]. Moreover, Consumer Tendency
can be predicted using Big Data Analytics.
• In politics district: Internet users’ tendency towards
candidates in presidency election can be predicted using Big
Data Analytics. “Before the votes were cast, New York Times
blogger Nate Silver predicted, with 90%+ confidence, that
Obama would win the election” said [7]. The blogger used a
Big Data Analytic tool to predict the result.
Fig. 3. Oracle’s Big Data Solutions
B. Cloud Computing
Cloud computing has established a new era of less
expensive and ease-to-use technology. It’s a pool of
computing resources which can be utilized on users’ demand.
Consequently, it has become a target for analysing and
predicting users’ insights. Cloud computing has become a
significant place for Big Data analytics tools [4]. Since
customers who subscribed to cloud applications, cloud
application providers have benefitted from the external data
sources by analysing them with their operational systems[4].
C. Crowdsourcing
Data is gained from numerous sources. Based on certain
criteria that institutions have predetermined, labours are
needed to collect data and implement series of processes to
prepare data for analysis. Using formal focus group or tren
• In smart healthcare district: The lack of follow-up with
patients after leaving a hospital and failure to provide patients
with necessary information upon leaving a hospital have
increased patients’ readmission rates. Patients, since s/he
admitted to a hospital and filled in an admission form then
after staying for period of time and left the hospital, a plenty
of information about a patients can help predicting the
likelihood of the patient’s readmission. Using Big Data
analytics, hospitals are managed to reduce admissions that
might occur in the next 30 days after the patient’s discharge
[2].
• In education enhancing district: Big Data Analytics have
intelligently impacted the students learning amount. Big Data
Analytics have showed teachers which student needs more
attention, exercises and learning materials as well as any
changes in classes if it is needed. In addition, Big Data
analytics brought enhancements into education system by
predicting students’ admission rate and students’ dropout rate
[2].
5. • In network security, Internet is a network of networks
where billions of users conduct everyday different
transactions on it whether harmful or useful. Hence, the need
for network security has been rising as well as the risks have.
Big Data Analytics has improved the network security,
protecting networks from malfunctions, attacks and suspicious
activities. Moreover, Big Data Analytics predicts any future
attack or threat likely to harm a network system [2]. Big Data
analytic gathers system’s log files of a network and it is
processed in few steps as shown in Fig. 2. Since log files
includes all attempts to access a server, attempts to download
or upload a file, attempts to access specific files, system logins
or any email emissions, predicting potential threats will be
estimated and the network administrators takes cautious
measures against them.
IX. BIG DATA ANALYTICS LIMITATIONS
Big Data analytics has brought a new era of prediction’s
techniques. Whereby, Big Data saves lots of time and money
in favour of decision makers to predict a potential future of
any aspect of our life. Bid Data still has limitations namely:
1) Specific context is critical.
Big Data might save time and money but without the
specific context it is useless [8]. If an institution is trying to
figure out an answer for a question using Big Data Analytic
and the answer is not in the acquired data, Big Data becomes
useless in that situation.
2) The three V’s of data should be considered.
Big Data three Vs should be considered before thinking of
using its technologies. If an institution’s data is just an amount
of data, Big Data analytic results are unreliable since velocity
and variety do not exist in data [8].
3) Traditional Analytics cannot be replaced
Traditional analytic methods such as Oracle, MySQL, MS
SQL and more can’t be replaced with Big Data analytic [8].
Traditional applications and systems are still used widely to
answers questions decision makers may seek for. Most
institutions run their own applications and systems and they
assigned strong databases to maintain tremendous amount of
data. Therefore, answers can be found easily in their systems
rather than wasting time on proving Big Data Analytics
requirements and implementing its complex processes.
However, large-scale institution may still need both traditional
analytics and Big Data analytics. Thus, they are considered
complementary.
CONCLUSION
Big data analytic is a promising predicting technology for
many aspects of life such as marketing, politics, healthcare
systems, network security, education, and many more. Big
Data analytics will benefit many institutions that have
incompletely unanswered questions. In spite of its advantages,
companies should take into account its risks and challenges
prior to adoption’s phase. Privacy, making false decisions,
over dependence on Big Data analytics’ results might be
repercussions that unaware institutions might encounter.
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