This document discusses the challenges of visualizing big data. It defines big data using the 3V model of volume, velocity and variety. It describes visualization tools like ManyEyes that can be used to analyze large datasets. However, ManyEyes has limitations like limited screen space and difficulties in searching and discovering patterns within large datasets. Future work is needed to address issues in visual representations and improve capabilities for searching, discovering hidden patterns when visualizing big data.
Misusability Measure Based Sanitization of Big Data for Privacy Preserving Ma...IJECEIAES
Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy Preserving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming.
Data Science tutorial for beginner level to advanced level | Data Science pro...IQ Online Training
This is a complete tutorial to learn data science from beginner level to advanced level. Know about the projects that are deployed at each and every level. These are some of the examples of data set and why you should take them.
Cloud computing and networking course: paper presentation -Data Mining for In...Cristian Consonni
This is the presentation for the course "Cloud Computing and Networking" of the ICT Doctoral School of the University of Trento.
The paper presented is
"Data mining for internet of things: A survey."
by Tsai, Chun-Wei, et al.
(Communications Surveys & Tutorials, IEEE 16.1 (2014): 77-97.)
A Survey on Graph Database Management Techniques for Huge Unstructured Data IJECEIAES
Data analysis, data management, and big data play a major role in both social and business perspective, in the last decade. Nowadays, the graph database is the hottest and trending research topic. A graph database is preferred to deal with the dynamic and complex relationships in connected data and offer better results. Every data element is represented as a node. For example, in social media site, a person is represented as a node, and its properties name, age, likes, and dislikes, etc and the nodes are connected with the relationships via edges. Use of graph database is expected to be beneficial in business, and social networking sites that generate huge unstructured data as that Big Data requires proper and efficient computational techniques to handle with. This paper reviews the existing graph data computational techniques and the research work, to offer the future research line up in graph database management.
Misusability Measure Based Sanitization of Big Data for Privacy Preserving Ma...IJECEIAES
Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy Preserving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming.
Data Science tutorial for beginner level to advanced level | Data Science pro...IQ Online Training
This is a complete tutorial to learn data science from beginner level to advanced level. Know about the projects that are deployed at each and every level. These are some of the examples of data set and why you should take them.
Cloud computing and networking course: paper presentation -Data Mining for In...Cristian Consonni
This is the presentation for the course "Cloud Computing and Networking" of the ICT Doctoral School of the University of Trento.
The paper presented is
"Data mining for internet of things: A survey."
by Tsai, Chun-Wei, et al.
(Communications Surveys & Tutorials, IEEE 16.1 (2014): 77-97.)
A Survey on Graph Database Management Techniques for Huge Unstructured Data IJECEIAES
Data analysis, data management, and big data play a major role in both social and business perspective, in the last decade. Nowadays, the graph database is the hottest and trending research topic. A graph database is preferred to deal with the dynamic and complex relationships in connected data and offer better results. Every data element is represented as a node. For example, in social media site, a person is represented as a node, and its properties name, age, likes, and dislikes, etc and the nodes are connected with the relationships via edges. Use of graph database is expected to be beneficial in business, and social networking sites that generate huge unstructured data as that Big Data requires proper and efficient computational techniques to handle with. This paper reviews the existing graph data computational techniques and the research work, to offer the future research line up in graph database management.
Big data is the term that characterized by its increasing
volume, velocity, variety and veracity. All these characteristics
make processing on this big data a complex task. So, for
processing such data we need to do it differently like map reduce
framework. When an organization exchanges data for mining
useful information from this big data then privacy of the data
becomes an important problem. In the past, several privacy
preserving algorithms have been proposed. Of all those
anonymizing the data has been the most efficient one.
Anonymizing the dataset can be done on several operations like
generalization, suppression, anatomy, specialization, permutation
and perturbation. These algorithms are all suitable for dataset
that does not have the characteristics of the big data. To preserve
the privacy of the large dataset an algorithm was proposed
recently. It applies the top down specialization approach for
anonymizing the dataset and the scalability is increasing my
applying the map reduce frame work. In this paper we survey the
growth of big data, characteristics, map-reduce framework and
all the privacy preserving mechanisms and propose future
directions of our research.
Security issues associated with big data in cloud computingIJNSA Journal
In this paper, we discuss security issues for cloud
computing, Big data, Map Reduce and Hadoop
environment. The main focus is on security issues i
n cloud computing that are associated with big
data. Big data applications are a great benefit to
organizations, business, companies and many
large scale and small scale industries.We also disc
uss various possible solutions for the issues
in cloud computing security and Hadoop. Cloud compu
ting security is developing at a rapid pace
which includes computer security, network security,
information security, and data privacy.
Cloud computing plays a very vital role in protecti
ng data, applications and the related
infrastructure with the help of policies, technolog
ies, controls, and big data tools
.
Moreover,
cloud computing, big data and its applications, adv
antages are likely to represent the most
promising new frontiers in science.
Evaluation of a Multiple Regression Model for Noisy and Missing Data IJECEIAES
The standard data collection problems may involve noiseless data while on the other hand large organizations commonly experience noisy and missing data, probably concerning data collected from individuals. As noisy and missing data will be significantly worrisome for occasions of the vast data collection then the investigation of different filtering techniques for big data environment would be remarkable. A multiple regression model where big data is employed for experimenting will be presented. Approximation for datasets with noisy and missing data is also proposed. The statistical root mean squared error (RMSE) associated with correlation coefficient (COEF) will be analyzed to prove the accuracy of estimators. Finally, results predicted by massive online analysis (MOA) will be compared to those real data collected from the following different time. These theoretical predictions with noisy and missing data estimation by simulation, revealing consistency with the real data are illustrated. Deletion mechanism (DEL) outperforms with the lowest average percentage of error.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
A Survey of Agent Based Pre-Processing and Knowledge RetrievalIOSR Journals
Abstract: Information retrieval is the major task in present scenario as quantum of data is increasing with a
tremendous speed. So, to manage & mine knowledge for different users as per their interest, is the goal of every
organization whether it is related to grid computing, business intelligence, distributed databases or any other.
To achieve this goal of extracting quality information from large databases, software agents have proved to be
a strong pillar. Over the decades, researchers have implemented the concept of multi agents to get the process
of data mining done by focusing on its various steps. Among which data pre-processing is found to be the most
sensitive and crucial step as the quality of knowledge to be retrieved is totally dependent on the quality of raw
data. Many methods or tools are available to pre-process the data in an automated fashion using intelligent
(self learning) mobile agents effectively in distributed as well as centralized databases but various quality
factors are still to get attention to improve the retrieved knowledge quality. This article will provide a review of
the integration of these two emerging fields of software agents and knowledge retrieval process with the focus
on data pre-processing step.
Keywords: Data Mining, Multi Agents, Mobile Agents, Preprocessing, Software Agents
Face recognition for presence system by using residual networks-50 architectu...IJECEIAES
Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.
Anonymization of data using mapreduce on cloudeSAT Journals
Abstract In computer world cloud services are provided by the service providers. The user wants to share the private data which are stored in cloud server for different reasons like data mining, data analysis etc. These can bring the privacy concern. Privacy preservation can be satisfied by Anonymizing data sets through generalization to satisfy privacy requirements by using k-anonymity technique which is a widely used type of privacy preserving techniques. At present days the data of cloud applications are increasing their scale day by day concern with Big Data trend. So it is very difficult thing to accept, manage, maintain and process the large scaled data with-in the required time stamps. Thus for privacy preserving on privacy sensitive , large scaled data is very difficult task for existing anonymization techniques because they will not manage the scaled data sets. This approach addresses the anonymization problem on large scale cloud data sets using two phase top down specialization approach and MapReduce framework. Innovative MapReduce jobs are carefully designed in both phases of this technique to achieve specialization computation on scalable data sets. Scalability and efficiency of Top Down Specialization (TDS) is significantly increased over the existing approach. Keywords: Top Down Specialization, MapReduce, Data Anonymization, Cloud Computing, Privacy Preservation
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
An Comprehensive Study of Big Data Environment and its Challenges.ijceronline
Big Data is a data analysis methodology enabled by recent advances in technologies and Architecture. Big data is a massive volume of both structured and unstructured data, which is so large that it's difficult to process with traditional database and software techniques. This paper provides insight to Big data and discusses its nature, definition that include such features as Volume, Velocity, and Variety .This paper also provides insight to source of big data generation, tools available for processing large volume of variety of data, applications of big data and challenges involved in handling big data
Big data is the term that characterized by its increasing
volume, velocity, variety and veracity. All these characteristics
make processing on this big data a complex task. So, for
processing such data we need to do it differently like map reduce
framework. When an organization exchanges data for mining
useful information from this big data then privacy of the data
becomes an important problem. In the past, several privacy
preserving algorithms have been proposed. Of all those
anonymizing the data has been the most efficient one.
Anonymizing the dataset can be done on several operations like
generalization, suppression, anatomy, specialization, permutation
and perturbation. These algorithms are all suitable for dataset
that does not have the characteristics of the big data. To preserve
the privacy of the large dataset an algorithm was proposed
recently. It applies the top down specialization approach for
anonymizing the dataset and the scalability is increasing my
applying the map reduce frame work. In this paper we survey the
growth of big data, characteristics, map-reduce framework and
all the privacy preserving mechanisms and propose future
directions of our research.
Security issues associated with big data in cloud computingIJNSA Journal
In this paper, we discuss security issues for cloud
computing, Big data, Map Reduce and Hadoop
environment. The main focus is on security issues i
n cloud computing that are associated with big
data. Big data applications are a great benefit to
organizations, business, companies and many
large scale and small scale industries.We also disc
uss various possible solutions for the issues
in cloud computing security and Hadoop. Cloud compu
ting security is developing at a rapid pace
which includes computer security, network security,
information security, and data privacy.
Cloud computing plays a very vital role in protecti
ng data, applications and the related
infrastructure with the help of policies, technolog
ies, controls, and big data tools
.
Moreover,
cloud computing, big data and its applications, adv
antages are likely to represent the most
promising new frontiers in science.
Evaluation of a Multiple Regression Model for Noisy and Missing Data IJECEIAES
The standard data collection problems may involve noiseless data while on the other hand large organizations commonly experience noisy and missing data, probably concerning data collected from individuals. As noisy and missing data will be significantly worrisome for occasions of the vast data collection then the investigation of different filtering techniques for big data environment would be remarkable. A multiple regression model where big data is employed for experimenting will be presented. Approximation for datasets with noisy and missing data is also proposed. The statistical root mean squared error (RMSE) associated with correlation coefficient (COEF) will be analyzed to prove the accuracy of estimators. Finally, results predicted by massive online analysis (MOA) will be compared to those real data collected from the following different time. These theoretical predictions with noisy and missing data estimation by simulation, revealing consistency with the real data are illustrated. Deletion mechanism (DEL) outperforms with the lowest average percentage of error.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
A Survey of Agent Based Pre-Processing and Knowledge RetrievalIOSR Journals
Abstract: Information retrieval is the major task in present scenario as quantum of data is increasing with a
tremendous speed. So, to manage & mine knowledge for different users as per their interest, is the goal of every
organization whether it is related to grid computing, business intelligence, distributed databases or any other.
To achieve this goal of extracting quality information from large databases, software agents have proved to be
a strong pillar. Over the decades, researchers have implemented the concept of multi agents to get the process
of data mining done by focusing on its various steps. Among which data pre-processing is found to be the most
sensitive and crucial step as the quality of knowledge to be retrieved is totally dependent on the quality of raw
data. Many methods or tools are available to pre-process the data in an automated fashion using intelligent
(self learning) mobile agents effectively in distributed as well as centralized databases but various quality
factors are still to get attention to improve the retrieved knowledge quality. This article will provide a review of
the integration of these two emerging fields of software agents and knowledge retrieval process with the focus
on data pre-processing step.
Keywords: Data Mining, Multi Agents, Mobile Agents, Preprocessing, Software Agents
Face recognition for presence system by using residual networks-50 architectu...IJECEIAES
Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.
Anonymization of data using mapreduce on cloudeSAT Journals
Abstract In computer world cloud services are provided by the service providers. The user wants to share the private data which are stored in cloud server for different reasons like data mining, data analysis etc. These can bring the privacy concern. Privacy preservation can be satisfied by Anonymizing data sets through generalization to satisfy privacy requirements by using k-anonymity technique which is a widely used type of privacy preserving techniques. At present days the data of cloud applications are increasing their scale day by day concern with Big Data trend. So it is very difficult thing to accept, manage, maintain and process the large scaled data with-in the required time stamps. Thus for privacy preserving on privacy sensitive , large scaled data is very difficult task for existing anonymization techniques because they will not manage the scaled data sets. This approach addresses the anonymization problem on large scale cloud data sets using two phase top down specialization approach and MapReduce framework. Innovative MapReduce jobs are carefully designed in both phases of this technique to achieve specialization computation on scalable data sets. Scalability and efficiency of Top Down Specialization (TDS) is significantly increased over the existing approach. Keywords: Top Down Specialization, MapReduce, Data Anonymization, Cloud Computing, Privacy Preservation
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
An Comprehensive Study of Big Data Environment and its Challenges.ijceronline
Big Data is a data analysis methodology enabled by recent advances in technologies and Architecture. Big data is a massive volume of both structured and unstructured data, which is so large that it's difficult to process with traditional database and software techniques. This paper provides insight to Big data and discusses its nature, definition that include such features as Volume, Velocity, and Variety .This paper also provides insight to source of big data generation, tools available for processing large volume of variety of data, applications of big data and challenges involved in handling big data
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
Big data is a prominent term which characterizes the improvement and availability of data in all three formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file includes text and multimedia contents. The primary objective of this big data concept is to describe the extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V” dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity. Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is described with the types of the data, Value which derives the business value and Veracity describes about the quality of the data and data understandability. Nowadays, big data has become unique and preferred research areas in the field of computer science. Many open research problems are available in big data and good solutions also been proposed by the researchers even though there is a need for development of many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper, a detailed study about big data, its basic concepts, history, applications, technique, research issues and tools are discussed.
Application and Methods of Deep Learning in IoTIJAEMSJORNAL
In this talk, we provide a comprehensive overview of how to use a subset of advanced AI techniques, most specifically Deep Learning (DL), to bolster analytics as well as learning in the IoT URL. First and foremost, we define a development environment that integrates big data designs with deep learning models to promote rapid experimentation. There are three main promises made in the proposal: To begin, it illustrates a big data engineering that facilitates big data assortment in the same way that businesses facilitate deep learning models. Then, the language for creating a data perspective is shown, one that transforms the many streams of large data into a format that can be used by an advanced learning system. Third, it demonstrates the success of the framework by applying the tool to a wide range of deep learning use cases. We provide a generalized basis for a variety of DL architectures using numerical examples. We also evaluate and summarize major published research projects that made use of DL in the IoT context. Wonderful Internet of Things gadgets that have integrated DL into their prior knowledge are often discussed.
Big Data Handling Technologies ICCCS 2014_Love Arora _GNDU Love Arora
Big data came into existence when the traditional relational database systems were not able to handle the unstructured data (weblogs, videos, photos, social updates, human behaviour) generated today by organisation, social media, or from any other data generating source. Data that is so large in volume, so diverse in variety or moving with such velocity is called Big data. Analyzing Big Data is a challenging task as it involves large distributed file systems which should be fault tolerant, flexible and scalable. The technologies used by big data application to handle the massive data are Hadoop, Map Reduce, Apache Hive, No SQL and HPCC. These technologies handle massive amount of data in MB, PB, YB, ZB, KB, and TB.
In this research paper various technologies for handling big data along with the advantages and disadvantages of each technology for catering the problems in hand to deal the massive data has discussed.
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.
A REVIEW ON CLASSIFICATION OF DATA IMBALANCE USING BIGDATAIJMIT JOURNAL
Classification is one among the data mining function that assigns items in a collection to target categories
or collection of data to provide more accurate predictions and analysis. Classification using supervised
learning method aims to identify the category of the class to which a new data will fall under. With the
advancement of technology and increase in the generation of real-time data from various sources like
Internet, IoT and Social media it needs more processing and challenging. One such challenge in
processing is data imbalance. In the imbalanced dataset, majority classes dominate over minority classes
causing the machine learning classifiers to be more biased towards majority classes and also most
classification algorithm predicts all the test data with majority classes. In this paper, the author analysis
the data imbalance models using big data and classification algorithm
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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.
1. International Journal For Technological Research In Engineering
Volume 1, Issue 4, December - 2013
ISSN (Online) : 2347 - 4718
CHALLENGES AND ISSUES DURING VISUALIZATION OF BIG DATA
Shilpa1 , Manjit Kaur2
(M.tech), Department of Computer Science and Engineering
2 Faculty, Department of Computer Science and Engineering
LPU, Jalandhar, India
1 Student
Abstract: Big data is a collection of massive and complex Velocity
data sets. There are many computational methods which are
How many data is generated. 150k per second to google,
used to get the meaningful information from the large data
twitter and facebook. Velocity defines the motion of the
sets. To discover the hidden patterns from the data sets from
data. During 2012, 2.5 quintillion bytes of data were
the massive data collection many technologies are used. Vicreated every day.
sual analysis is used for the purpose of visualization of data
Variety
sets in graphics or charts etc. by the visualization of the data;
Data comes from the various sources in the form of strucit is very easy to make the decisions and attractive form of
tured and unstructured data such as text, images, videos,
the data.
logs, social media and so on.
Keywords: Visualization, Visual Analytics, Social Media,
Big Data, Hidden Patterns.
Veracity
The data generated from the various sources can be of
various forms of combination of raw data, missing values,
I. INTRODUCTION
dirty data etc.
In today era large amount of information in various forms
from variety of sources is generated. To make it meaningful
visual analysis is applied and various computational methods
are used. To makes it understandable and meaningful big
data analytics is performed for the processing of complex and
massive data sets.
Big data analytics analyze the large amount of information
used to uncover the hidden patterns and the other information
which is useful and important information for the use.
II.
DIMENSIONS OF BIG DATA
III.
CHALLENGES AND OPPORTUNITIES
As there exist large amount of information, we face the various
challenges and problems in the processing of massive and
complex data sets.
The challenges include the unstructured data, real time analytics, fault tolerance, processing and storage of the data and
many more. The main challenges and opportunities are as
follows :
a) Storage and processing issues.
b) Data Accessing and sharing of information.
c) Complexity of data.
The main causes of complexities with big data are as follows :
• Human Perception
• Limited Screen space
Human perception means difficulty in extracting the useful
information when the visualized objects become large. In
limited screen space, the visibilities of objects are not proper.
In short, the challenges with big data are as follows :
Figure 1: Dimensions of Big data
The Big data is defined by the 3V which are as follows :
Volume
Large amount of information generation in Zetabytes and
petabytes. Per Day facebook is collecting 500 terabytes
data and 400 millions tweets per day on twitter and 3
billion facebook likes and comments etc.
www.ijtre.com
a) Acquiring and storing large amount of data.
b) Extracting useful information.
c) Aggregation and integration by representation.
d) Querying , data modeling and analysis.
e) Interpretation of the date to acquire the meaningful information.
Copyright 2013.All rights reserved.
174
2. International Journal For Technological Research In Engineering
Volume 1, Issue 4, December - 2013
IV.
ISSN (Online) : 2347 - 4718
VISUALIZATION OF THE DATA
Visualization is the visual representation of the data to make it
attractive and understandable easily.
Main objectives of visualization are as follows :
a) Understanding of data properly which is recorded.
b) Graphical representation of data.
c) To run the search query to locate the location of the text .
d) For discovering the hidden patterns.
Figure 2: Word Tree Visualization
e) Visibility of all the data items.
V.
TOOLS FOR THE VISUALIZATION
ManyEyes for the visualization is a visualization tool
launched by the IBM research and IBM Cognos software group.
It is web Based tool used for the purpose of visualization and
discovery. It is free available tool as a service on IBM’s alpha
Work Service website. By using this tool we can make the VA
[B3] and easily make the decisions.
This tool can be used for structure as well as unstructured
data for the visual analysis.
User can use the online available data as well as can upload their own data for the visual analysis. ManyEyes is a
community-powered tool. There is more than 150,000 data sets
availability online and pre-visualized.
In this tool Visual analytics of big data is done using
ManyEyes Tools and data set is uploaded from the spreadsheets or can be text file. But there is a problem related to the
proper visualization of data.
Many Eyes is a bet on the power of human visual intelligence
to find patterns. Our goal is to "democratize" visualization and
to enable a new social kind of data analysis.
With data generation of the large amount of data, there
are many challenges and opportunities which are urgent to
improve these challenges.
As I have study about the ManyEyes tool there is a problem
of searching and proper visualization of the data.
VI.
MANYEYES
With the help of ManyEyes the complex data is render that
require the high level data input for the Visual analytics. IBM’s
ManyEyes is used to visualize the data when to make the
decisions at a time we can make the interactive picture for the
proper understanding.
A.
From this visualization we can easily see the text but
difficult to analyze the occurrence of the text location in
the given data. This is a searching issue in Word Tree of
the Manyeyes Visualization tool.
We can see the next word also easily.
Figure 3: Wordtree
2. TreeMap
A tree map is a visualization tool for the hierarchical
structures. It shows the attributes of leaf nodes in effective
ways. Tree map enable the user to discover the hidden
patterns and exceptions.
a) Data Set
The data to visualize is taken from the
spreadsheet after the uploading of the
data in Many Eyes TreeMap it visualized and then display in TreeMap format.
Components of ManyEyes
1. Word Tree
A word tree is a visualization tool for unstructured data
such as a book data, article, speech or poem. By the use of
this tool we can pick word or phrase and we can analyze
the different context where these words appear.
The given below visualization is a word tree of the NIST
Guide IDPS, using search item "Wireless" the visualization
is created.
www.ijtre.com
Figure 4: Spreadsheet Data 1
In the TreeMap, we can see the information about
the person by just clicking on that column or we
Copyright 2013.All rights reserved.
175
3. International Journal For Technological Research In Engineering
Volume 1, Issue 4, December - 2013
can search.The disadvantage in this visualization is
limited screen space.
ISSN (Online) : 2347 - 4718
include the searching, discovering hidden patterns.
VIII.
CONSLUSION
Visualization is a process of graphical representation of the
large data sets. The various tools are used for the visual analytics. The graphical representation helps in understanding of the
data and help in making decisions.
References
Figure 5: Spreadsheet Data 2
The whole data will upload after upload the visualization
will display which is shown as below :
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Akram W. Shared disk big data analytics with apache
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[2] Garlasu D., Sandulescu V., Halcu I., Neculoiu G. A big
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[3] Sagiroglu S., Sinanc D. Big data: A review. Collaboration Technologies and Systems (CTS) International Conference,
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bigdata/.
[6] http://www.cloudcomputingpath.com/
challenges-and-opportunities-with-bigdata/.
Figure 6: TreeMap Visualization
3. Network Diagram
Network diagram display the relationship between the
various entities used in the data sets. Network diagram
only telling about the name not detail information about
that person it need to be recover.
[7] Grosso P., de Laat C., Membrey P. Addressing big data
issues in scientific data infrastructure. Collaboration Technologies and Systems (CTS) International Conference, 2013.
[8] Aditya B. Patel, Manashvi Birla, Ushma Nair. Addressing
big data problem using hadoop and map reduce. Engineering (NUiCONE) Nirma University International Conference,
2012.
[9] Szczuka Marcin. Ifsa world congress and nafips annual
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[10] Tien J.M. Big data: Unleashing information. Service Systems and Service Management (ICSSSM) 10th International
Conference, 2013.
[11] http://www.intel.in/content/dam/www/
public/us/en/documents/white-papers/bigdata-visualization-turning-big-data-intobig-insights.pdf.
Figure 7: Network Diagra Visualization
VII.
[12] http://blogs.computerworld.com/businessintelligenceanalytics/23159/datavisualization-picture-worth-billion-bytes.
FUTURE SCOPE
[13] http://smallbusiness.yahoo.com/advisor/
applying-big-data-visualization-dataAs the visual analytics of the data helps in better understanding.
mining-054555947.html.
There are various issues regarding visual representations that
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176