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.
Big data analytics and its impact on internet usersStruggler Ever
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.
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.
Big data is a term that describes a large or complex
data volume. That data volume can be processes using traditional
data processing software or techniques that are insufficient to deal
with them. But big data is often noisy, heterogeneous, irrelevant
and untrustworthy. As the speed of information growth exceeds
Moore’s Law at the beginning of this new century, excessive data
is making great troubles to human beings. However this data with
special attributes can’t be managed and processed by the current
traditional software system, which become a real problem. In this
paper was discussed some big data challenges and problems that
are faced by organizations. These challenges may relate
heterogeneity, scale, timelines, privacy and human collaboration.
Survey method was used as a theoretical solution framework.
Survey method consists of a questionnaires report. Questionnaires
report consists of all challenges and problems faced by
organizations. After knowing the problem and challenges of
organizations, a solution was given to organization to solve big
data challenges.
Big data analytics and its impact on internet usersStruggler Ever
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.
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.
Big data is a term that describes a large or complex
data volume. That data volume can be processes using traditional
data processing software or techniques that are insufficient to deal
with them. But big data is often noisy, heterogeneous, irrelevant
and untrustworthy. As the speed of information growth exceeds
Moore’s Law at the beginning of this new century, excessive data
is making great troubles to human beings. However this data with
special attributes can’t be managed and processed by the current
traditional software system, which become a real problem. In this
paper was discussed some big data challenges and problems that
are faced by organizations. These challenges may relate
heterogeneity, scale, timelines, privacy and human collaboration.
Survey method was used as a theoretical solution framework.
Survey method consists of a questionnaires report. Questionnaires
report consists of all challenges and problems faced by
organizations. After knowing the problem and challenges of
organizations, a solution was given to organization to solve big
data challenges.
Big Data Analytics: Recent Achievements and New ChallengesEditor IJCATR
The era of Big data is being generated by everything around us at all times. Every digital process and social media
exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming
velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics
capabilities and skills. Big data has become an important issue for a large number of research areas such as data mining,
machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The combination of
big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas
as social media and social networks. These new challenges are focused mainly on problems such as data processing, data
storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and
tracking data, among others. In this paper, discussion about the new concept big data and data analytic their concept, tools
and methodologies that is designed to allow for efficient data mining and information sharing fusion from social media and of
the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media
and big data paradigms.
Identifying and analyzing the transient and permanent barriers for big datasarfraznawaz
Auspiciously, big data analytics had made it possible to generate value from immense amounts of raw data. Organizations are able to seek incredible insights which assist them in effective decision making and providing quality of service by establishing innovative strategies to recognize, examine and address the customers’ preferences. However, organizations are reluctant to adopt big data solutions due to several barriers such as data storage and transfer, scalability, data quality, data complexity, timeliness, security, privacy, trust, data ownership, and transparency. Despite the discussion on big data opportunities, in this paper, we present the findings of our in-depth review process that was focused on identifying as well as analyzing the transient and permanent barriers for adopting big data. Although, the transient barriers for big data can be eliminated in the near future with the advent of innovative technical contributions, however, it is challenging to eliminate the permanent barriers enduringly, though their impact could be recurrently reduced with the efficient and effective use of technology, standards, policies, and procedures.
over the past ten years, data has grown on the Internet, and we are the fuel and haste of this increase. Business owners, they produce apps for us, and we feed these companies with our data, unfortunately, it is all our private data. In the end, we become, through our private data, a commodity that is sold to the highest bidder.
Without security, not even privacy. Ethical oversight and constraints are needed to ensure that an appropriate balance. This article will cover: the contents of big data, what it includes, how data is collected, and the process of involving it on the Internet. In addition, it discuss the analysis of data, methods of collecting it, and factors of ethical challenges. Furthermore, the user's rights, which must be observed, and the privacy the user has.
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.
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.
Big Data Impact on Purchasing and SCM - PASIA World Conference DiscussionBill Kohnen
The volume, velocity and variety of data available is almost unthinkable. 90% of the world’s data is less than 2 years old, we are able analyze less than 5% of it and 80% of what people generally are looking at is less than 6 weeks old. Harnessing this data for effective decision making is a goal for organizations worldwide and has created a 50Billion dollar industry to provide tools and consulting.
Even before “Big Data” Purchasing groups were swimming in data and struggled to put it to effective use. The success of Strategic Sourcing methodology had the effect of also identifying and standardizing the types and format of information that can be used to drive improvement.
This discussion will connect how big data sources and methodology can be used to develop specific and relevant spend analytics. Also presented will be an illustration of how you can use data and tools you already have - to get immediate results and make you better prepared to evaluate the need for more powerful analytic tools.
Finally will conclude with comments on how Big Data along with other disruptive digital trends will create a new required skill sets for Purchasing and Supply Chain Professionals and are transform how operate all ready.
Analysis on big data concepts and applicationsIJARIIT
The term, Big Data ‘ h a s been referred as a large amount of data that cannot be handled by traditional database
systems. It consists of large volumes of data which is been generated at a very fast rate, these cannot be handled and processed by
traditional data management tools, so it requires a new set of tools or frameworks to handle these types of data. Big data
works under V’s namely Volume, Velocity, and Variety. Volume refers to the size of the data whereas Velocity refers to the
speed that the data is being generated. Variety refers to different formats of data that is generated. Mostly in today’s world
thee average volumes of unstructured data like audio, video, image, sensor data etc. One can get these types of data through
social media, enterprise data, and Transactional data. Through Big data analytics, one can able to examine large data sets
containing a variety of data types. Primary goals of big data analytics are to help the organizations to take important decisions
by appointing data scientists and other analytics professionals to analyses large volumes of data. Challenges one can face
during large volume of data, especially machine-generated data, is exploding, how fast that data is growing every year, with
new sources of data that are emerging. Through the article, the authors intend to decipher the notions in an intelligible
manner embodying in text several use-cases and illustrations
With many organisations considering getting on the Hadoop bandwagon, this document provides an overview of the planned use cases for Hadoop, an illustration of some of the common technology components, suggestions on when Hadoop is worth considering, some the challenges organisations are experiencing, cost considerations and finally, how an organisation should position for a Big Data initiative. Any organisation considering a Big Data initiative with Hadoop should thoroughly consider each of these areas before embarking on a course of action.
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
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.
the influence of machine language and data science in the emerging worldijtsrd
The study describes the machine learning language with respect to big data sciences. The process of machine learning has evolved to have grown significantly to progress in information science. This progress has led to conquer different domains and are capable of solving myriad problems and upgrading the applicative properties. Hence, the present study is drafted to highlight the importance of machine learning process and language. Anitha. S "The Influence of Machine Language and Data Science in the Emerging World" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31907.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31907/the-influence-of-machine-language-and-data-science-in-the-emerging-world/anitha-s
Isolating values from big data with the help of four v’seSAT Journals
Abstract
Big Data refers to the massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. It includes business transactions, e-mail messages, photos, surveillance videos and activity logs. It also includes unstructured text posted on the Web, such as blogs and social media. Big Data has shown lot of potential in real world industry and research community. We support the power and Potential of it in solving real world problems. However, it is imperative to understand Big Data through the lens of 4 Vs. 4th V as ‘Value’ is desired output for industry challenges and issues. We provide a brief survey study of 4 Vs. of Big Data in order to understand Big Data and extract Value concept in general. Finally we conclude by showing our vision of improved healthcare, a product of Big Data Utilization, as a future work for researchers and students, while moving forward.
Keywords: Big Data, Surveillance videos, blogs, social media, four Vs.
An Investigation on Scalable and Efficient Privacy Preserving Challenges for ...IJERDJOURNAL
ABSTRACT:- Big data is a relative term describing a situation where the volume, velocity and variety of data exceed an organization’s storage or compute capacity for accurate and timely decision making. Big data refers to huge amount of digital information collected from multiple and different sources. With the development of application of Internet/Mobile Internet, social networks, Internet of Things, big data has become the hot topic of research across the world, at the same time; big data faces security risks and privacy protection during collecting, storing, analyzing and utilizing. Since a key point of big data is to access data from multiple and different domains security and privacy will play an important role in big data research and technology. Traditional security mechanisms, which are used to secure small scale static data, are inadequate. So the question is which security and privacy technology is adequate for efficient access to big data. This paper introduces the functions of big data, and the security threat faced by big data, then proposes the technology to solve the security threat, finally, discusses the applications of big data in information security. Main expectation from the focused challenges is that it will bring a novel focus on the big data infrastructure.
Big Data Analytics: Recent Achievements and New ChallengesEditor IJCATR
The era of Big data is being generated by everything around us at all times. Every digital process and social media
exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming
velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics
capabilities and skills. Big data has become an important issue for a large number of research areas such as data mining,
machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The combination of
big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas
as social media and social networks. These new challenges are focused mainly on problems such as data processing, data
storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and
tracking data, among others. In this paper, discussion about the new concept big data and data analytic their concept, tools
and methodologies that is designed to allow for efficient data mining and information sharing fusion from social media and of
the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media
and big data paradigms.
Identifying and analyzing the transient and permanent barriers for big datasarfraznawaz
Auspiciously, big data analytics had made it possible to generate value from immense amounts of raw data. Organizations are able to seek incredible insights which assist them in effective decision making and providing quality of service by establishing innovative strategies to recognize, examine and address the customers’ preferences. However, organizations are reluctant to adopt big data solutions due to several barriers such as data storage and transfer, scalability, data quality, data complexity, timeliness, security, privacy, trust, data ownership, and transparency. Despite the discussion on big data opportunities, in this paper, we present the findings of our in-depth review process that was focused on identifying as well as analyzing the transient and permanent barriers for adopting big data. Although, the transient barriers for big data can be eliminated in the near future with the advent of innovative technical contributions, however, it is challenging to eliminate the permanent barriers enduringly, though their impact could be recurrently reduced with the efficient and effective use of technology, standards, policies, and procedures.
over the past ten years, data has grown on the Internet, and we are the fuel and haste of this increase. Business owners, they produce apps for us, and we feed these companies with our data, unfortunately, it is all our private data. In the end, we become, through our private data, a commodity that is sold to the highest bidder.
Without security, not even privacy. Ethical oversight and constraints are needed to ensure that an appropriate balance. This article will cover: the contents of big data, what it includes, how data is collected, and the process of involving it on the Internet. In addition, it discuss the analysis of data, methods of collecting it, and factors of ethical challenges. Furthermore, the user's rights, which must be observed, and the privacy the user has.
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.
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.
Big Data Impact on Purchasing and SCM - PASIA World Conference DiscussionBill Kohnen
The volume, velocity and variety of data available is almost unthinkable. 90% of the world’s data is less than 2 years old, we are able analyze less than 5% of it and 80% of what people generally are looking at is less than 6 weeks old. Harnessing this data for effective decision making is a goal for organizations worldwide and has created a 50Billion dollar industry to provide tools and consulting.
Even before “Big Data” Purchasing groups were swimming in data and struggled to put it to effective use. The success of Strategic Sourcing methodology had the effect of also identifying and standardizing the types and format of information that can be used to drive improvement.
This discussion will connect how big data sources and methodology can be used to develop specific and relevant spend analytics. Also presented will be an illustration of how you can use data and tools you already have - to get immediate results and make you better prepared to evaluate the need for more powerful analytic tools.
Finally will conclude with comments on how Big Data along with other disruptive digital trends will create a new required skill sets for Purchasing and Supply Chain Professionals and are transform how operate all ready.
Analysis on big data concepts and applicationsIJARIIT
The term, Big Data ‘ h a s been referred as a large amount of data that cannot be handled by traditional database
systems. It consists of large volumes of data which is been generated at a very fast rate, these cannot be handled and processed by
traditional data management tools, so it requires a new set of tools or frameworks to handle these types of data. Big data
works under V’s namely Volume, Velocity, and Variety. Volume refers to the size of the data whereas Velocity refers to the
speed that the data is being generated. Variety refers to different formats of data that is generated. Mostly in today’s world
thee average volumes of unstructured data like audio, video, image, sensor data etc. One can get these types of data through
social media, enterprise data, and Transactional data. Through Big data analytics, one can able to examine large data sets
containing a variety of data types. Primary goals of big data analytics are to help the organizations to take important decisions
by appointing data scientists and other analytics professionals to analyses large volumes of data. Challenges one can face
during large volume of data, especially machine-generated data, is exploding, how fast that data is growing every year, with
new sources of data that are emerging. Through the article, the authors intend to decipher the notions in an intelligible
manner embodying in text several use-cases and illustrations
With many organisations considering getting on the Hadoop bandwagon, this document provides an overview of the planned use cases for Hadoop, an illustration of some of the common technology components, suggestions on when Hadoop is worth considering, some the challenges organisations are experiencing, cost considerations and finally, how an organisation should position for a Big Data initiative. Any organisation considering a Big Data initiative with Hadoop should thoroughly consider each of these areas before embarking on a course of action.
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
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.
the influence of machine language and data science in the emerging worldijtsrd
The study describes the machine learning language with respect to big data sciences. The process of machine learning has evolved to have grown significantly to progress in information science. This progress has led to conquer different domains and are capable of solving myriad problems and upgrading the applicative properties. Hence, the present study is drafted to highlight the importance of machine learning process and language. Anitha. S "The Influence of Machine Language and Data Science in the Emerging World" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31907.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31907/the-influence-of-machine-language-and-data-science-in-the-emerging-world/anitha-s
Isolating values from big data with the help of four v’seSAT Journals
Abstract
Big Data refers to the massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. It includes business transactions, e-mail messages, photos, surveillance videos and activity logs. It also includes unstructured text posted on the Web, such as blogs and social media. Big Data has shown lot of potential in real world industry and research community. We support the power and Potential of it in solving real world problems. However, it is imperative to understand Big Data through the lens of 4 Vs. 4th V as ‘Value’ is desired output for industry challenges and issues. We provide a brief survey study of 4 Vs. of Big Data in order to understand Big Data and extract Value concept in general. Finally we conclude by showing our vision of improved healthcare, a product of Big Data Utilization, as a future work for researchers and students, while moving forward.
Keywords: Big Data, Surveillance videos, blogs, social media, four Vs.
An Investigation on Scalable and Efficient Privacy Preserving Challenges for ...IJERDJOURNAL
ABSTRACT:- Big data is a relative term describing a situation where the volume, velocity and variety of data exceed an organization’s storage or compute capacity for accurate and timely decision making. Big data refers to huge amount of digital information collected from multiple and different sources. With the development of application of Internet/Mobile Internet, social networks, Internet of Things, big data has become the hot topic of research across the world, at the same time; big data faces security risks and privacy protection during collecting, storing, analyzing and utilizing. Since a key point of big data is to access data from multiple and different domains security and privacy will play an important role in big data research and technology. Traditional security mechanisms, which are used to secure small scale static data, are inadequate. So the question is which security and privacy technology is adequate for efficient access to big data. This paper introduces the functions of big data, and the security threat faced by big data, then proposes the technology to solve the security threat, finally, discusses the applications of big data in information security. Main expectation from the focused challenges is that it will bring a novel focus on the big data infrastructure.
The software development process is complete for computer project analysis, and it is important to the evaluation of the random project. These practice guidelines are for those who manage big-data and big-data analytics projects or are responsible for the use of data analytics solutions. They are also intended for business leaders and program leaders that are responsible for developing agency capability in the area of big data and big data analytics .
For those agencies currently not using big data or big data analytics, this document may assist strategic planners, business teams and data analysts to consider the value of big data to the current and future programs.
This document is also of relevance to those in industry, research and academia who can work as partners with government on big data analytics projects.
Technical APS personnel who manage big data and/or do big data analytics are invited to join the Data Analytics Centre of Excellence Community of Practice to share information of technical aspects of big data and big data analytics, including achieving best practice with modeling and related requirements. To join the community, send an email to the Data Analytics Centre of Excellence
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.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective supply
of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the
different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective supply
of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the
different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
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.
What exactly is big data? What exactly is big data? .pptxTusharSengar6
big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three “Vs.” Put simply, big data is larger, more complex data sets, especially from new data sources.
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.
The authors in the present paper conduct a comprehensive study to explore the impact of big data analytics in key domains namely, Health Care (HC), Retail Industry (RI), Public Governance (PG), Pubic Security & Safety (PSS) and Personal Location Tracking (PLT). Initially, the study looks at the insights of data sources along with their characteristics in each domain. Later, it presents the highly productive and competitive big data applications with innovative big data technologies. Subsequently, the study showcases the impact of big data on each domain to capture value addition in its services. Finally, the study put forwards many more research opportunities as all these domains differ in their complexity and development in the usage of big data analytics
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.
The authors in the present paper conduct a comprehensive study to explore the impact of big data analytics
in key domains namely, Health Care (HC), Retail Industry (RI), Public Governance (PG), Pubic Security &
Safety (PSS) and Personal Location Tracking (PLT). Initially, the study looks at the insights of data sources
along with their characteristics in each domain. Later, it presents the highly productive and competitive big
data applications with innovative big data technologies. Subsequently, the study showcases the impact of
big data on each domain to capture value addition in its services. Finally, the study put forwards many
more research opportunities as all these domains differ in their complexity and development in the usage of
big data analytics.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
2. Variety makes big data really big. Big data comes from a great variety of sources and generally has in three types: structured, semi structured and unstructured. Structured data inserts a data warehouse already tagged and easily sorted but unstructured data is random and difficult to analyze. Semi- structured data does not conform to fixed fields but contains tags to separate data elements [4,17].
Volume or the size of data now is larger than terabytes and petabytes. The grand scale and rise of data outstrips traditional store and analysis techniques [4,16].
Velocity is required not only for big data, but also all processes. For time limited processes, big data should be used as it streams into the organization in order to maximize its value [4,16].
During in the intensity of this information, another component is the verification of data flow. It is difficult to control large data so data security must be provided. In addition, after producing and processing of big data, it should create a plus value for the organization.
There are some questions and important answers summarized below that from the TDWI survey which is asked to the data management professionals [12].
After the organization applied some form of big data analytics, these benefits occur: better aimed marketing, more straight business insights, client based segmentation, recognition of sales and market chances,
While implementing big data analytics, these issues are potential barriers: inexpert stuff, cost, privation of business sponsorship, hard to designing analytic systems, lack of current database software in analytics
Whereas significant crowd define big data now and in future is an opportunity because of exhaustive analytics, some of them see big data as problem because of managing
Big data types that stored and using with advanced techniques today are: structured, semi structured, complex, event and unstructured data
While replacing analytics platforms, these problems occur: cannot fit to big volumes of data, cannot support to needed analytic models, data loading is too slow, requirement of advanced analytics platform, IT cannot catch up with demands
As can be seen from the survey that big data analysis still needs more attention. Analyzing big data can require hundreds of servers running massively paralel software. That actually distinguishes big data, aside from its variety, volume and velocity, is the potential to analyze it to reveal new insights to optimize decision making.
B. Big Data Samples
Examples in the literature are available in are astronomy, atmospheric science, genomics, biogeochemical, biological science and research, life sciences, medical records, scientific research, government, natural disaster and resource management, private sector, military surveillance, private sector, financial services, retail, social networks, web logs, text, document, photography, audio, video, click streams, search indexing, call detail records, POS information, RFID, mobile phones, sensor networks and telecommunications [20]. Organizations in any industry have big data can benefit from its careful analysis to gain insights and depths to solve real problems [8].
McKinsey Global Institute specified the potential of big data in five main topics [9]:
Healthcare: clinical decision support systems, individual analytics applied for patient profile, personalized medicine, performance based pricing for personnel, analyze disease patterns, improve public health
Public sector: creating transparency by accessible related data, discover needs, improve performance, customize actions for suitable products and services, decision making with automated systems to decrease risks, innovating new products and services
Retail: in store behavior analysis, variety and price optimization, product placement design, improve performance, labor inputs optimization, distribution and logistics optimization, web based markets
Manufacturing: improved demand forecasting, supply chain planning, sales support, developed production operations, web search based applications
Personal location data: smart routing, geo targeted advertising or emergency response, urban planning, new business models
Web provides kind of opportunities for big data too. For example; social network analysis such as understanding user intelligence for more targeted advertising, marketing campaigns and capacity planning, customer behavior and buying patterns also sentiment analytics. According to these inferences firms optimization their content and recommendation engine [1]. Some companies such as Google and Amazon publishing articles related to their work. Inspired by the writings published, developers are developing similar technologies as open source software such as Lucene, Solr, Hadoop and HBase. Facebook, Twitter and LinkedIn are going a step further thereby publishing open source projects for big data like Cassandra, Hive, Pig, Voldemort, Storm, IndexTank.
In addition, predictive analytics on traffic flows or identify guilties and threats from different video, audio and data feeds are advantages of big data again [3].
In 2012, Obama regime announced big data initiatives of more than $200 million in research and development investments for National Science Foundation, National Institutes of Health, Department of Defense, Department of Energy and United States Geological Survey. The investments were launched to take a step forward instruments and methods for access, organize and collect findings from vast volumes of digital data [14]. 43
3. C. Methods
Most enterprises are facing lots of new data, which arrives in many different forms. Big data has the potential to provide insights that can transform every business. Big data has generated a whole new industry of supporting architectures such as MapReduce. MapReduce is a programming framework for distributed computing which was created by Google using the divide and conquer method to break down complex big data problems into small units of work and process them in parallel [13]. MapReduce can be divided into two stages [10]:
Map Step: The master node data is chopped up into many smaller subproblems. A worker node processes some subset of the smaller problems under the control of the JobTracker node and stores the result in the local file system where a reducer is able to access it.
Reduce Step: This step analyzes and merges input data from the map steps. There can be multiple reduce tasks to parallelize the aggregation, and these tasks are executed on the worker nodes under the control of the JobTracker.
Hadoop created to inspire by BigTable which is Google’s data storage system, Google File System and MapReduce [6]. Hadoop is Java based framework and heterogeneous open source platform. It is not a replacement for database, warehouse or ETL (Extract, Transform, Load) strategy. Hadoop includes a distributed file system, analytics and data storage platforms and a layer that manages parallel computation, workflow and configuration administration [8,22]. It is not designed for real time complex event processing like streams. HDFS (Hadoop Distributed File System) runs across the nodes in a Hadoop cluster and connects together the file systems on many input and output data nodes to make them into one big file system [4,13,19].
As seen Fig. 1 and Fig. 2, Hadoop offers [21]:
HDFS: A highly fault tolerant distributed file system that is responsible for storing data on the clusters.
MapReduce: A powerful parallel programming technique for distributed processing on clusters.
HBase: A scalable, distributed database for random read/write access.
Pig: A high level data processing system for analyzing data sets that occur a high level language.
Hive: A data warehousing application that provides a SQL like interface and relational model.
Sqoop: A project for transferring data between relational databases and Hadoop.
Avro: A system of data serialization.
Oozie: A workflow for dependent Hadoop jobs.
Chukwa: A Hadoop subproject as data accumulation system for monitoring distributed systems.
Flume: A reliable and distributed streaming log collection.
ZooKeeper: A centralized service for providing distributed synchronization and group services
HPCC (High Performance Computing Cluster) Systems distributed data intensive open source computing platform and provides big data workflow management services. Unlike Hadoop, HPCC’s data model defined by user. The key to complex problems can be stated easily with high level ECL basis. HPCC ensure that ECL is executed at the maximum elapsed time and nodes are processed in parallel. Furthermore HPCC Platform does not require third party tools like GreenPlum, Cassandra, RDBMS, Oozie, etc [22].
The three main HPCC components are [22]:
HPCC Data Refinery (Thor) is a massively parallel ETL engine that enables data integration on a scale and provides batch oriented data manipulation.
HPCC Data Delivery Engine (Roxie) is a massively parallel, high throughput, ultra fast, low latency, allows efficient multi user retrieval of data and structured query response engine.
Enterprise Control Language (ECL) is automatically distributes workload between nodes, has automatic synchronization of algorithms, develop extensible machine learning library, has simple usage programming language optimized for big data operations and query transactions.
Figure 2 indicates comparisons between HPCC Systems Platform and Hadoop in terms of architecture and stacks. According to reference [22], some differences summarized below:
HPCC clusters can be exercised in Thor and Roxie. Hadoop clusters perform with MapReduce processing.
In HPCC environments ECL is primary programming language. However, Hadoop MapReduce processes are based on Java language.
HPCC platform builds multikey and multivariate indexes on Distributed File System. Hadoop HBase procures column oriented database.
Data warehouse abilities used in HPCC Roxie for structural queries and analyzer applications on the other hand Hadoop Hive provide data warehouse abilities and allow data to be loaded into HDFS.
On the same hardware configuration a 400-node system, HPCC success is 6 minutes 27 seconds and Hadoop success is 25 minutes 28 seconds. This result showed that HPCC faster than Hadoop for this comparison.
44
4. Figure 2. Comparison between HPCC Systems Platform and Hadoop architecture [22]
D. Knowledge Discovery from Big Data
Knowledge Discovery from Data (KDD) entitle as some operations designed to get information from complicated data sets [6]. Reference [18] outlines the KDD at nine steps:
1. Application domain prior to information and defining purpose of process from customer’s perspective.
2. Generate subset data point for knowledge discovery.
3. Removing noise, handling missing data fields, collecting required information to model and calculating time information and known changes.
4. Finding useful properties to present data depending on purpose of job.
5. Mapping purposes to a particular data mining methods.
6. Choose data mining algorithm and method for searching data patterns.
7. Researching patterns in expressional form.
8. Returning any steps 1 through 7 for iterations also this step can include visualization of patterns.
9. Using information directly, combining information into another system or simply enlisting and reporting.
Reference [6] analyzes knowledge discovery from big data in three principles using Hadoop. These are:
1. KDD includes a variety of analysis methods as distributed programming, pattern recognition, data mining, natural language processing, sentiment analysis, statistical and visual analysis and human computer interaction. Therefore architecture must support various methods and analysis techniques.
Statistical analysis interested in summarizing massive datasets, understanding data and defining models for prediction.
Data mining correlate with discovering useful models in massive data sets by itself, machine learning combine with data mining and statistical methods enabling machines to understand datasets.
Visual analysis is developing area in which large datasets are serviced to users in challenging ways will be able to understand relationships.
2. A comprehensive KDD architecture must procure to keep and operate process line.
Preparation of data and batch analytics are made, for proper troubleshooting with errors, missing values and unusable format.
Processing structured and semi structured data
3. It is cardinal that making results accessible and foolproof. For this reason following approaches are used to overcome this issue.
Using open source and popular standards
Use WEB based architectures
Publicly available results
III. PRIVACY AND SECURITY ISSUES
In May 2012, Intel IT Center surveyed 200 IT managers in large companies to find out how they were approaching big data analytics [7]. They asked IT managers what standards they would like to see addressed for big data analytics and the answers were: data security, technology to keep customers’ data private, data transparency, performance benchmarking, data and system interoperability. There were answers concerns via third party cloud vendors regarding; data security and privacy concerns, company policy prevents me from outsourcing data storage and analytics, overall costs and I’m doing my data management/ analytics in house don’t plan to outsource. According the survey apprehensions ordinarily about security. 45
5. The ruining of traditional defensive environments united with attackers' abilities to survive traditional security systems requires organizations to adopt an intelligence driven security model that is more risk aware, contextual and agile. Intelligence driven security relies on big data analytics. Big data involve both the breadth of sources and the information depth needed for programs to specify risks accurately, to defend against illegal activity and advanced cyber threats. A big data driven security model has the following characteristics [15] :
Internal and external data sources that multiply in value and create a synergistic learning effect.
Automated tools that collect diverse data types and normalize them.
Analytics engines manage to process massive volumes of fast changing data in real time.
Advanced monitoring systems that continuously analyse high value systems, resources and make considerations based on behavior and risk models.
Active controls such as need additional user authentication, blocking data transfers or simplification analysts' decision making.
Centralized warehouse where all security related data is made available for security analysts to query.
Standardized views into demonstrations of compromise that are created in machine readable form and can be shared at scale by trusted sources.
N-tier infrastructures that create scalability across vectors and have ability to process large and complex searches and queries.
High degree of integration via security and risk management tools to facilitate detailed investigations of potential problems.
Reference [5] states how developing a holistic and confident approach for big data is:
To begin of a management project, companies need to place and describe data sources origination, created and access authorizations.
To categorize discovered as its importance.
To guarantee that records are archived and protected according to standards and regulations.
To develop policies related data processing, such as defining stored data types, store time, storehouse and data accessed types.
By keeping data in one place, it occurs a target for attackers to sabotage the organization. It required that big data stores are rightly controlled. To ensure authentication a cryptographically secure communication framework has to be implemented. Controls should be using principle of reduced privileges, especially for access rights, except for an administrator who have permission data to physical access. For effective access controls, they should be continuously observed and switched as change employees organization roles so employees do not aggregate immoderate rights that could be misused. Other security procedures are needed to capture and analyze network traffic such as metadata, packet capture, flow and log information.. Organizations should guaranteed investments in security products using agile technologies based analytics not static equipments. Another problem is associated with organizing compliance of data protection laws. Organizations have to consider legal branching for storing data [5,11,15].
However, big data have security advantages. When organizations categorize knowledge, they control data according to specified by the regulations such as imposing store periods. This allows organizations to select data that has neither little value nor any need to be kept so that it is no longer available for theft. Another benefit is massive data can be mined for threats such as evidence of malware, anomalies, or phishing [5].
IV. OVERALL EVALUATION
The amount of data has been increasing and data set analyzing become more competitive. The challenge is not only to collect and manage vast volume and different type of data, but also to extract meaningful value from it [10]. Also needed, managers and analysts with an excellent insight of how big data can be applied. Companies must accelerate employment programs, while making significant investments in the education and training of key personnel [2].
Through TDWI Big Data Analytics survey, benefits of big data are: better aimed marketing, more straight business insights, client based segmentation, recognition of sales and market chances, automated decision making, definitions of customer behaviors, greater return on investments, quantification of risks and market trending, comprehension of business alteration, better planning and forecasting, identification consumer behavior from click streams and production yield extension [12].
In addition, TDWI array potential barriers to implementing big data analytics like: inexpert stuff and cannot find to hire big data experts, cost, privation of business sponsorship, hard to designing analytic systems, lack of current database software in analytics and fast process time, scalability problems, incapable to make big data usable for end users, data load cannot fast enough in current database software, lack of compelling business case [12].
According to the, Intel IT Center Big Data Analytics survey, there are several challenges for big data: data growth, data infrastructure, data governance/policy, data integration, data velocity, data variety, data compliance/regulation and data visualization [7].
In addition, Intel IT Center specify obstacles of big data as: security concerns, capital/operational expenses, increased network bottlenecks, shortage of skilled data science professionals, unmanageable data rate, data replication capabilities, lack of compression capabilities, greater network latency and insufficient CPU power [7]. 46
6. In spite of potential barriers, challenges and obstacles of big data, it has great importance today and in the future.
V. CONCLUSION
In this article, an overview of big data's content, scope, samples, methods, advantages and challenges and discusses privacy concern have been reviewed. The results have shown that even if available data, tools and techniques available in the literature, there are many points to be considered, discussed, improved, developed, analyzed, etc. Besides, the critical issue of privacy and security of the big data is the big issue will be discussed more in future.
Although this paper clearly has not resolved the entire subject about this substantial topic, hopefully it has provided some useful discussion and a framework for researchers.
REFERENCES
[1] A. Vailaya, "What’s All the Buzz Around “Big Data?”", IEEE Women in Engineering Magazine, December 2012, pp. 24-31,
[2] B. Brown, M. Chui and J. Manyika, "Are you Ready for the era of ‘Big Data’? " McKinsey Quarterly, McKinsey Global Institute, October 2011
[3] B.Gerhardt, K. Griffin and R. Klemann, "Unlocking Value in the Fragmented World of Big Data Analytics", Cisco Internet Business Solutions Group, June 2012,
http://www.cisco.com/web/about/ac79/docs/sp/Information- Infomediaries.pdf
[4] C. Eaton, D. Deroos, T. Deutsch, G. Lapis and P.C. Zikopoulos, Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, Mc Graw-Hill Companies, 978-0-07-179053-6, 2012
[5] C. Tankard, "Big Data Security", Network Security Newsletter, Elsevier, ISSN 1353-4858, July 2012
[6] E. Begoli and J. Horey, "Design Principles for Effective Knowledge Discovery from Big Data", Software Architecture (WICSA) and European Conference on Software Architecture (ECSA) Joint Working IEEE/IFIP Conference on, Helsinki, August 2012
[7] Intel IT Center, "Peer Research: Big Data Analytics", Intel’s IT Manager Survey on How Organizations Are Using Big Data, August 2012,
http://www.intel.com/content/dam/www/public/us/en/documents/reports/data-insights-peer-research-report.pdf
[8] Intel IT Center, "Planning Guide: Getting Started with Hadoop", Steps IT Managers Can Take to Move Forward with Big Data Analytics, June 2012
http://www.intel.com/content/dam/www/public/us/en/documents/guides/ getting-started-with-hadoop-planning-guide.pdf
[9] J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh and A.H. Byers, "Big data: The next frontier for innovation, competition, and productivity", McKinsey Global Institute, 2011, http://www.mckinsey.com/~/media/McKinsey/dotcom/Insights%20and%20pubs/MGI/Research/Technology%20and%20Innovation/Big%20Data/MGI_big_data_full_report.ashx
[10] K. Bakshi, "Considerations for Big Data: Architecture and Approach", Aerospace Conference IEEE, Big Sky Montana, March 2012
[11] M. Smith, C. Szongott, B. Henne and G. Voigt , "Big Data Privacy Issues in Public Social Media", Digital Ecosystems Technologies (DEST), 6th IEEE International Conference on, Campione d'Italia, June 2012
[12] P. Russom, "Big Data Analytics ", TDWI Best Practices Report, TDWI Research, Fourth Quarter 2011,
http://tdwi.org/research/2011/09/best-practices-report-q4-big-data- analytics/asset.aspx
[13] R.D. Schneider, Hadoop for Dummies Special Edition, John Wiley&Sons Canada, 978-1-118-25051-8, 2012
[14] R. Weiss and L.J. Zgorski, "Obama Administration Unveils “Big Data” Initiative:Announces $200 Million in new R&D Investments", Office of Science and Technology Policy Executive Office of the President, March 2012
[15] S. Curry, E. Kirda, E. Schwartz, W.H. Stewart and A. Yoran, "Big Data Fuels Intelligence Driven Security", RSA Security Brief, January 2013
http://www.emc.com/collateral/industry-overview/big-data-fuels- intelligence-driven-security-io.pdf
[16] S. Madden, "From Databases to Big Data", IEEE Internet Computing, June 2012, v.16, pp.4-6
[17] S. Singh and N. Singh, "Big Data Analytics", 2012 International Conference on Communication, Information & Computing Technology Mumbai India, IEEE, October 2011
[18] U. Fayyad, G. Piatetsky-Shapiro and P. Smyth, "From Data Mining to Knowledge Discovery in Databases", American Association for Artificial Intelligence, AI Magazine, Fall 1996, pp. 37- 54
[19] V. Borkar, M.J. Carey and C. Li, "Inside “Big Data Management”: Ogres, Onions, or Parfaits?", EDBT/ICDT 2012 Joint Conference Berlin Germany, 2012
[20] http://en.wikipedia.org/wiki/Big_data , last access 11.03.2013
[21] http://hadoop.apache.org/ , last access 11.03.2013
[22] http://hpccsystems.com/ , last access 11.03.2013
[23] http://www.humanfaceofbigdata.com/ , last access 11.03.2013
47