Cloud computing is a powerful technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software.
Digital assets include any outputs of business activities that have value, such as intellectual property, knowledge, data collections, and digital information created each year. They should be preserved for several reasons: increasing significance of data reuse, statutory requirements, protecting investments, sharing innovation, and building an institutional record. However, digital preservation poses issues like selection, preservation format and longevity, access, trust in integrity and authentication, costs, and intellectual property rights. Preservation requires more than just storage; approaches include technology preservation, emulation, data migration, and using the Open Archival Information System model.
Big Data in Distributed Analytics,Cybersecurity And Digital ForensicsSherinMariamReji05
This document provides an overview of big data and its applications in distributed analytics, cyber security, and digital forensics. It discusses how big data can reduce the processing time of large volumes of data in distributed computing environments using Hadoop. Examples of big data applications include using social media, search engine, and aircraft black box data for analysis. The document also outlines the challenges of traditional systems and how distributed big data architectures help address them by allowing data to be processed across clustered computers.
The document discusses big data issues and challenges. It defines big data as large volumes of structured and unstructured data that is growing exponentially due to increased data generation. Some key challenges discussed include storage and processing limitations of exabytes of data, privacy and security risks, and the need for new skills and training to manage and analyze big data. Examples are given of large data projects in various domains like science, healthcare, and commerce that are driving big data growth.
This document discusses big data, including what it is, common data sources, its volume, velocity and variety characteristics, solutions like Hadoop and its HDFS and MapReduce components, and the impact and future of big data. It explains that big data refers to large and complex datasets that are difficult to process using traditional tools. Hadoop provides a framework to store and process big data across clusters of commodity hardware.
This document defines big data and discusses its key characteristics and applications. It begins by defining big data as large volumes of structured, semi-structured, and unstructured data that is difficult to process using traditional methods. It then outlines the 5 Vs of big data: volume, velocity, variety, veracity, and variability. The document also discusses Hadoop as an open-source framework for distributed storage and processing of big data, and lists several applications of big data across various industries. Finally, it discusses both the risks and benefits of working with big data.
This document compares and contrasts the FAIR and GDPR frameworks for managing research data. It summarizes the key principles of each: FAIR focuses on making data findable, accessible, interoperable and reusable, while GDPR prioritizes privacy by default and design. The document notes challenges for researchers working with human subject data under these differing paradigms and expresses concerns that researchers may not receive adequate support to share such data or take advantage of open science and data science innovations if GDPR restrictions are too strict. It concludes by questioning which framework, FAIR or GDPR, will have more influence over human subject research going forward.
Digital assets include any outputs of business activities that have value, such as intellectual property, knowledge, data collections, and digital information created each year. They should be preserved for several reasons: increasing significance of data reuse, statutory requirements, protecting investments, sharing innovation, and building an institutional record. However, digital preservation poses issues like selection, preservation format and longevity, access, trust in integrity and authentication, costs, and intellectual property rights. Preservation requires more than just storage; approaches include technology preservation, emulation, data migration, and using the Open Archival Information System model.
Big Data in Distributed Analytics,Cybersecurity And Digital ForensicsSherinMariamReji05
This document provides an overview of big data and its applications in distributed analytics, cyber security, and digital forensics. It discusses how big data can reduce the processing time of large volumes of data in distributed computing environments using Hadoop. Examples of big data applications include using social media, search engine, and aircraft black box data for analysis. The document also outlines the challenges of traditional systems and how distributed big data architectures help address them by allowing data to be processed across clustered computers.
The document discusses big data issues and challenges. It defines big data as large volumes of structured and unstructured data that is growing exponentially due to increased data generation. Some key challenges discussed include storage and processing limitations of exabytes of data, privacy and security risks, and the need for new skills and training to manage and analyze big data. Examples are given of large data projects in various domains like science, healthcare, and commerce that are driving big data growth.
This document discusses big data, including what it is, common data sources, its volume, velocity and variety characteristics, solutions like Hadoop and its HDFS and MapReduce components, and the impact and future of big data. It explains that big data refers to large and complex datasets that are difficult to process using traditional tools. Hadoop provides a framework to store and process big data across clusters of commodity hardware.
This document defines big data and discusses its key characteristics and applications. It begins by defining big data as large volumes of structured, semi-structured, and unstructured data that is difficult to process using traditional methods. It then outlines the 5 Vs of big data: volume, velocity, variety, veracity, and variability. The document also discusses Hadoop as an open-source framework for distributed storage and processing of big data, and lists several applications of big data across various industries. Finally, it discusses both the risks and benefits of working with big data.
This document compares and contrasts the FAIR and GDPR frameworks for managing research data. It summarizes the key principles of each: FAIR focuses on making data findable, accessible, interoperable and reusable, while GDPR prioritizes privacy by default and design. The document notes challenges for researchers working with human subject data under these differing paradigms and expresses concerns that researchers may not receive adequate support to share such data or take advantage of open science and data science innovations if GDPR restrictions are too strict. It concludes by questioning which framework, FAIR or GDPR, will have more influence over human subject research going forward.
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. There are four key characteristics of big data: volume, velocity, variety, and veracity. Various sources generate big data, including social media, scientific instruments, mobile devices, sensors, and more. Analyzing big data can provide benefits like cost reductions, time reductions, new product development, and smarter business decisions. Hadoop Distributed File System (HDFS) and Hadoop software platform provide scalable and cost-effective infrastructure for storing and processing big data across commodity servers in a cluster.
This presentation will provide an overview of issues in digital preservation. Presentation was delivered during the joint DPE/Planets/CAPAR/nestor training event, ‘The Preservation challenge: basic concepts and practical applications’ (Barcelona, March 2009)
International Journal on Cloud Computing: Services and Architecture (IJCCSA)ijccsa
Cloud computing helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability, and data protection. To discuss and address these issues, we invite researches who focus on cloud computing to shed more light on this emerging field. This peer-reviewed open access Journal aims to bring together researchers and practitioners in all security aspects of cloud-centric and outsourced computing, including (but not limited to):
Big data refers to extremely large and complex datasets that cannot be processed using traditional data processing software. It is characterized by high volume, variety, velocity, veracity, and value. Key concepts for working with big data include clustered, parallel, and distributed computing which involve pooling resources across multiple machines to analyze large datasets simultaneously. Common frameworks and tools are used to break jobs into smaller pieces to run in parallel across distributed systems for batch and real-time processing. Cloud computing provides an effective solution for big data processing by renting servers as needed from leading providers.
11th International conference on Database Management Systems (DMS 2020) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Database Management Systems. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this field and establishing new collaborations in these areas.
Two-Phase TDS Approach for Data Anonymization To Preserving Bigdata Privacydbpublications
While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. There have been a various different privacy preserving mechanisms developed for protecting privacy at different stages (e.g. data generation, data storage, data processing) of big data life cycle. The goal of this paper is to provide a complete overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms and also we illustrate the infrastructure of big data and state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. This paper focus on the anonymization process, which significantly improve the scalability and efficiency of TDS (top-down-specialization) for data anonymization over existing approaches. Also, we discuss the challenges and future research directions related to preserving privacy in big data.
11th International conference on Database Management Systems (DMS 2020)dannyijwest
11th International conference on Database Management Systems (DMS 2020) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Database Management Systems. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this field and establishing new collaborations in these areas.
Digital data preservation involves planning and allocating resources to ensure digital information remains accessible and usable over time. It requires policies, strategies and preservation methods to ensure future access. The document outlines 13 ways to approach digital preservation, including as an ongoing activity that avoids crises through continuous effort rather than periodic bursts; as a cooperative effort across organizations to enhance funding; and as a selection process to determine what data is worth preserving given limits on storage. The conclusion notes that while the article outlines preservation approaches, it does not explain how to achieve preservation or select essential data to retain.
Big Data is the new technology or science to make the well informed decision in
business or any other science discipline with huge volume of data from new sources of
heterogeneous data. . Such new sources include blogs, online media, social network, sensor network,
image data and other forms of data which vary in volume, structure, format and other factors. Big
Data applications are increasingly adopted in all science and engineering domains, including space
science, biomedical sciences and astronomic and deep space studies. The major challenges of big
data mining are in data accessing and processing, data privacy and mining algorithms. This paper
includes the information about what is big data, data mining with big data, the challenges in big data
mining and what are the currently available solutions to meet those challenges.
A presentation on Digital Preservation by Rupesh Kumar A, Assistant Professor, Department of Studies and Research in Library and Information Science, Tumkur University, Tumakuru, Karnataka, India.
Sirris innovate2011 - Smart Products with smart data - introduction, Dr. Elen...Sirris
This lecture highlights current trends, challenges and opportunities related to the emergence of large amounts of data. It also presents Sirris’s recent research activities in this domain.
Big data refers to large amounts of data that are beyond the processing capabilities of typical database software. It is characterized by its volume, velocity, and variety. Hadoop is an open-source software framework that can distribute data and processing across clusters of computers to solve big data problems. Hadoop uses HDFS for storage and MapReduce as a programming model to process large datasets in parallel across clusters.
Big data is characterized by 3 V's - volume, velocity, and variety. It refers to large and complex datasets that are difficult to process using traditional database management tools. Key technologies to handle big data include distributed file systems, Apache Hadoop, data-intensive computing, and tools like MapReduce. Common tools used are infrastructure management tools like Chef and Puppet, monitoring tools like Nagios and Ganglia, and analytics platforms like Netezza and Greenplum.
Hypermedia is a computer information retrieval system that allows users to access and interact with audio, visual, text, graphics and photo files through hyperlinks. It extends hypertext by incorporating multimedia. A hypermedia database provides links to additional information on a subject. Unlike traditional databases designed to store relational records, hypermedia databases allow non-linear access to documents through organized links. Examples of early hypermedia include Hypercard, a hypertext programming tool for Macintosh, and the World Wide Web, which combines all Internet resources accessible through HTTP.
The document discusses strategies for planning and resourcing digital archives and recordkeeping over the long term. It emphasizes understanding information needs, designing systems to support records, using open formats, applying metadata, managing migration, educating staff, and securing funding for projects through building support and linking to popular ideas. Free tools and resources are also mentioned.
Course in Big Data Analytics in association with IBM
Everyday huge amount of data is created. This data comes from everywhere : sensors used to gather climate information, post to social media sites, digital pictures and videos, purchase transaction records and Cell phone GPS signals to name a few. This data is Big Data.
Big data is a blanket term for any collection of data set so large and complex that it becomes difficult to process using on hand data management tools or traditional data processing applications. The challenges include capture, storage, search, sharing, transfer, analysis and visualization. Anyone who has knowledge on Java, basic UNIX and basic SQL can opt for Big Data training course.
cloud computing is a growing field in computer science. This ppt can help the beginners understand it. contains information about PaaS, Iaas, SaaS and other concepts of Cloud Computing.It also contains a video on cloud computing.
The rise of “Big Data” on cloud computing: Review and open research issues
Paper Link: https://www.researchgate.net/publication/264624667_The_rise_of_Big_Data_on_cloud_computing_Review_and_open_research_issues
The document discusses the course objectives and topics for CCS334 - Big Data Analytics. The course aims to teach students about big data, NoSQL databases, Hadoop, and related tools for big data management and analytics. It covers understanding big data and its characteristics, unstructured data, industry examples of big data applications, web analytics, and key tools used for big data including Hadoop, Spark, and NoSQL databases.
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. There are four key characteristics of big data: volume, velocity, variety, and veracity. Various sources generate big data, including social media, scientific instruments, mobile devices, sensors, and more. Analyzing big data can provide benefits like cost reductions, time reductions, new product development, and smarter business decisions. Hadoop Distributed File System (HDFS) and Hadoop software platform provide scalable and cost-effective infrastructure for storing and processing big data across commodity servers in a cluster.
This presentation will provide an overview of issues in digital preservation. Presentation was delivered during the joint DPE/Planets/CAPAR/nestor training event, ‘The Preservation challenge: basic concepts and practical applications’ (Barcelona, March 2009)
International Journal on Cloud Computing: Services and Architecture (IJCCSA)ijccsa
Cloud computing helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability, and data protection. To discuss and address these issues, we invite researches who focus on cloud computing to shed more light on this emerging field. This peer-reviewed open access Journal aims to bring together researchers and practitioners in all security aspects of cloud-centric and outsourced computing, including (but not limited to):
Big data refers to extremely large and complex datasets that cannot be processed using traditional data processing software. It is characterized by high volume, variety, velocity, veracity, and value. Key concepts for working with big data include clustered, parallel, and distributed computing which involve pooling resources across multiple machines to analyze large datasets simultaneously. Common frameworks and tools are used to break jobs into smaller pieces to run in parallel across distributed systems for batch and real-time processing. Cloud computing provides an effective solution for big data processing by renting servers as needed from leading providers.
11th International conference on Database Management Systems (DMS 2020) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Database Management Systems. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this field and establishing new collaborations in these areas.
Two-Phase TDS Approach for Data Anonymization To Preserving Bigdata Privacydbpublications
While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. There have been a various different privacy preserving mechanisms developed for protecting privacy at different stages (e.g. data generation, data storage, data processing) of big data life cycle. The goal of this paper is to provide a complete overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms and also we illustrate the infrastructure of big data and state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. This paper focus on the anonymization process, which significantly improve the scalability and efficiency of TDS (top-down-specialization) for data anonymization over existing approaches. Also, we discuss the challenges and future research directions related to preserving privacy in big data.
11th International conference on Database Management Systems (DMS 2020)dannyijwest
11th International conference on Database Management Systems (DMS 2020) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Database Management Systems. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this field and establishing new collaborations in these areas.
Digital data preservation involves planning and allocating resources to ensure digital information remains accessible and usable over time. It requires policies, strategies and preservation methods to ensure future access. The document outlines 13 ways to approach digital preservation, including as an ongoing activity that avoids crises through continuous effort rather than periodic bursts; as a cooperative effort across organizations to enhance funding; and as a selection process to determine what data is worth preserving given limits on storage. The conclusion notes that while the article outlines preservation approaches, it does not explain how to achieve preservation or select essential data to retain.
Big Data is the new technology or science to make the well informed decision in
business or any other science discipline with huge volume of data from new sources of
heterogeneous data. . Such new sources include blogs, online media, social network, sensor network,
image data and other forms of data which vary in volume, structure, format and other factors. Big
Data applications are increasingly adopted in all science and engineering domains, including space
science, biomedical sciences and astronomic and deep space studies. The major challenges of big
data mining are in data accessing and processing, data privacy and mining algorithms. This paper
includes the information about what is big data, data mining with big data, the challenges in big data
mining and what are the currently available solutions to meet those challenges.
A presentation on Digital Preservation by Rupesh Kumar A, Assistant Professor, Department of Studies and Research in Library and Information Science, Tumkur University, Tumakuru, Karnataka, India.
Sirris innovate2011 - Smart Products with smart data - introduction, Dr. Elen...Sirris
This lecture highlights current trends, challenges and opportunities related to the emergence of large amounts of data. It also presents Sirris’s recent research activities in this domain.
Big data refers to large amounts of data that are beyond the processing capabilities of typical database software. It is characterized by its volume, velocity, and variety. Hadoop is an open-source software framework that can distribute data and processing across clusters of computers to solve big data problems. Hadoop uses HDFS for storage and MapReduce as a programming model to process large datasets in parallel across clusters.
Big data is characterized by 3 V's - volume, velocity, and variety. It refers to large and complex datasets that are difficult to process using traditional database management tools. Key technologies to handle big data include distributed file systems, Apache Hadoop, data-intensive computing, and tools like MapReduce. Common tools used are infrastructure management tools like Chef and Puppet, monitoring tools like Nagios and Ganglia, and analytics platforms like Netezza and Greenplum.
Hypermedia is a computer information retrieval system that allows users to access and interact with audio, visual, text, graphics and photo files through hyperlinks. It extends hypertext by incorporating multimedia. A hypermedia database provides links to additional information on a subject. Unlike traditional databases designed to store relational records, hypermedia databases allow non-linear access to documents through organized links. Examples of early hypermedia include Hypercard, a hypertext programming tool for Macintosh, and the World Wide Web, which combines all Internet resources accessible through HTTP.
The document discusses strategies for planning and resourcing digital archives and recordkeeping over the long term. It emphasizes understanding information needs, designing systems to support records, using open formats, applying metadata, managing migration, educating staff, and securing funding for projects through building support and linking to popular ideas. Free tools and resources are also mentioned.
Course in Big Data Analytics in association with IBM
Everyday huge amount of data is created. This data comes from everywhere : sensors used to gather climate information, post to social media sites, digital pictures and videos, purchase transaction records and Cell phone GPS signals to name a few. This data is Big Data.
Big data is a blanket term for any collection of data set so large and complex that it becomes difficult to process using on hand data management tools or traditional data processing applications. The challenges include capture, storage, search, sharing, transfer, analysis and visualization. Anyone who has knowledge on Java, basic UNIX and basic SQL can opt for Big Data training course.
cloud computing is a growing field in computer science. This ppt can help the beginners understand it. contains information about PaaS, Iaas, SaaS and other concepts of Cloud Computing.It also contains a video on cloud computing.
The rise of “Big Data” on cloud computing: Review and open research issues
Paper Link: https://www.researchgate.net/publication/264624667_The_rise_of_Big_Data_on_cloud_computing_Review_and_open_research_issues
The document discusses the course objectives and topics for CCS334 - Big Data Analytics. The course aims to teach students about big data, NoSQL databases, Hadoop, and related tools for big data management and analytics. It covers understanding big data and its characteristics, unstructured data, industry examples of big data applications, web analytics, and key tools used for big data including Hadoop, Spark, and NoSQL databases.
Data Mining With Big Data presents an overview of data mining techniques for large and complex datasets. It discusses how big data is produced and its characteristics including volume, velocity, variety, and variability. The document outlines challenges of big data mining such as platform and algorithm design, and solutions like distributed computing and privacy controls. Hadoop is presented as a framework for managing big data using its distributed file system and processing capabilities. The presentation concludes that big data technologies can provide more relevant insights by analyzing large and dynamic data sources.
Big data refers to large, complex datasets that come in a variety of formats from multiple sources. It is characterized by high volume, velocity, and variety of data, as well as additional Vs like veracity, value, and variability. Managing big data presents numerous challenges around storage, processing speed and volume, data integration and quality, extracting useful insights, privacy and security, scalability, and costs. Skills and tools are required to govern, manage, and analyze big data effectively.
Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbour algorithm is discreetly chosen among them and described along with an example.
At Softroniics we provide job oriented training for freshers in IT sector. We are Pioneers in all leading technologies like Android, Java, .NET, PHP, Python, Embedded Systems, Matlab, NS2, VLSI etc. We are specializiling in technologies like Big Data, Cloud Computing, Internet Of Things (iOT), Data Mining, Networking, Information Security, Image Processing, Mechanical, Automobile automation and many other. We are providing long term and short term internship also.
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A Review Paper on Big Data and Hadoop for Data Scienceijtsrd
Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, technqiues and frameworks. Hadoop is an open source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Mr. Ketan Bagade | Mrs. Anjali Gharat | Mrs. Helina Tandel "A Review Paper on Big Data and Hadoop for Data Science" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29816.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/29816/a-review-paper-on-big-data-and-hadoop-for-data-science/mr-ketan-bagade
Big data refers to extremely large data sets that are difficult to process using traditional data processing tools. It is characterized by volume, velocity, variety, veracity and variability. Big data comes in both structured and unstructured formats from a variety of sources. To effectively analyze big data, platforms must be able to handle different data types, large volumes, streaming data, and provide analytics capabilities. The five key aspects of big data are volume, velocity, variety, veracity and variability.
to effectively analyze this kind of information is now seen as a key competitive advantage to better inform decisions. In order to do so, organizations employ Sentiment Analysis (SA) techniques on these data. However, the usage of social media around the world is ever-increasing, which considerably accelerates massive data generation and makes traditional SA systems unable to deliver useful insights. Such volume of data can be efficiently analyzed using the combination of SA techniques and Big Data technologies. In fact, big data is not a luxury but an essential necessary to make valuable predictions. However, there are some challenges associated with big data such as quality that could highly affect the SA systems’ accuracy that use huge volume of data. Thus, the quality aspect should be addressed in order to build reliable and credible systems. For this, the goal of our research work is to consider Big Data Quality Metrics (BDQM) in SA that rely of big data. In this paper, we first highlight the most eloquent BDQM that should be considered throughout the Big Data Value Chain (BDVC) in any big data project. Then, we measure the impact of BDQM on a novel SA method accuracy in a real case study by giving simulation results.
Big data refers to extremely large data sets that are difficult to process using traditional data processing tools. It is characterized by volume, velocity, variety, veracity and variability. Big data can be structured, unstructured or semi-structured. It comes from a variety of sources and must be analyzed in real-time. A big data platform must be able to handle different data types and volumes at large scale from diverse sources, perform analytics and enable discovery. The five characteristics that define big data are volume, velocity, variety, veracity and variability.
The document discusses the Internet of Things (IoT) and some of the key challenges. It notes that IoT data is multi-modal, distributed, heterogeneous, noisy and incomplete. It raises issues around data management, actuation and feedback, service descriptions, real-time analysis, and privacy and security. The document outlines research challenges around transforming raw data to actionable information, machine learning for large datasets, making data accessible and discoverable, and energy efficient data collection and communication. It emphasizes that IoT data integration requires solutions across physical, cyber and social domains.
This document provides a review of Hadoop storage and clustering algorithms. It begins with an introduction to big data and the challenges of storing and processing large, diverse datasets. It then discusses related technologies like cloud computing and Hadoop, including the Hadoop Distributed File System (HDFS) and MapReduce processing model. The document analyzes and compares various clustering techniques like K-means, fuzzy C-means, hierarchical clustering, and Self-Organizing Maps based on parameters such as number of clusters, size of clusters, dataset type, and noise.
A proposed Solution: Data Availability and Error Correction in Cloud ComputingCSCJournals
The document proposes a solution to data availability and error correction in cloud computing using a RAID 51 architecture. RAID 51 combines RAID 1 and RAID 5 architectures. RAID 1 creates a mirror copy of data across disks for high availability. RAID 5 uses block-level striping with parity data distributed across disks for error correction. The proposed RAID 51 model uses a RAID 5 configuration under each RAID 1 mirror, allowing the system to sustain failure of disks in either RAID 5 array as well as one additional disk, avoiding data loss. This provides both data availability through mirroring and error correction capabilities.
BIG DATA SECURITY AND PRIVACY ISSUES IN THE CLOUD IJNSA Journal
Many organizations demand efficient solutions to store and analyze huge amount of information. Cloud computing as an enabler provides scalable resources and significant economic benefits in the form of reduced operational costs. This paradigm raises a broad range of security and privacy issues that must be taken into consideration. Multi-tenancy, loss of control, and trust are key challenges in cloud computing environments. This paper reviews the existing technologies and a wide array of both earlier and state-ofthe-art projects on cloud security and privacy. We categorize the existing research according to the cloud reference architecture orchestration, resource control, physical resource, and cloud service management layers, in addition to reviewing the recent developments for enhancing the Apache Hadoop security as one of the most deployed big data infrastructures. We also outline the frontier research on privacy-preserving data-intensive applications in cloud computing such as privacy threat modeling and privacy enhancing solutions.
Big data security and privacy issues in theIJNSA Journal
Many organizations demand efficient solutions to store and analyze huge amount of information. Cloud computing as an enabler provides scalable resources and significant economic benefits in the form of reduced operational costs. This paradigm raises a broad range of security and privacy issues that must be taken into consideration. Multi-tenancy, loss of control, and trust are key challenges in cloud computing environments. This paper reviews the existing technologies and a wide array of both earlier and state-ofthe-art projects on cloud security and privacy. We categorize the existing research according to the cloud reference architecture orchestration, resource control, physical resource, and cloud service management layers, in addition to reviewing the recent developments for enhancing the Apache Hadoop security as one of the most deployed big data infrastructures. We also outline the frontier research on privacy-preserving data-intensive applications in cloud computing such as privacy threat modeling and privacy enhancing solutions.
Delivering Faster Insights with a Logical Data FabricDenodo
Watch full webinar here: https://bit.ly/38B5yOW
We will learn from our speakers today how a logical data fabric helps organisations realise faster insights. They will touch on the recent Forrester total economic impact report, as well as discuss real life customer use cases where a demonstrably faster time to insights helped achieve better decision making, supporting improved business goals.
BIG DATA IN CLOUD COMPUTING REVIEW AND OPPORTUNITIESijcsit
Big Data is used in decision making process to gain useful insights hidden in the data for business and engineering. At the same time it presents challenges in processing, cloud computing has helped in advancement of big data by providing computational, networking and storage capacity. This paper presents the review, opportunities and challenges of transforming big data using cloud computing resources.
Big Data is used in decision making process to gain useful insights hidden in the data for business and engineering. At the same time it presents challenges in processing, cloud computing has helped in advancement of big data by providing computational, networking and storage capacity. This paper presents the review, opportunities and challenges of transforming big data using cloud computing resources.
The COI will establish standards and processes to enable the
sharing of geospatial intelligence information among the National Geospatial-
Intelligence Agency, the Defense Intelligence Agency, and the National
Reconnaissance Office to improve situational awareness and support to
military operations.
Similar to The rise of big data on cloud computing (20)
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
1. The rise of “big data”
on cloud computing
Presenter
Muhammad Maaz Irfan
Reg No: 201824100008
School of Information Science and Engineering
University of Jinan, Jinan, Shandong, China
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2. Contents
I. Introduction
II. Definition & characteristics of big data
III. Cloud Computing
IV. Relationship between cloud & big data
V. Case studies
VI. Big data storage system
VII. Hadoop background
VIII. Research Challenges
I. Scalability
II. Availability
III. Data integrity
IV. Transformation
V. Data quality
VI. Heterogeneity
VII. Privacy
VIII. Legal issues
IX. Governance
IX. Open Research issues
X. Conclusion
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3. Introduction
• The rise of social media, Internet of Things (IoT),
and multimedia, has produced an overwhelming
flow of data in either structured
• Big data are characterized by three aspects:
– Data are numerous
– Data cannot be categorized into regular relational
databases
– Data are generated, captured, and processed rapidly.
Moreover, big data is transforming healthcare,
science, engineering, finance, business, and
eventually, the society.
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4. Definition & characteristics of big data
• Big data is a set of techniques and
technologies that require new forms of
integration to uncover large hidden values
from large datasets that are
– Diverse
– Complex
– & In massive scale
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5. Four Vs of big data
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Big
data
Volume
Variety
Velocity
Value
9. Cloud Computing
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• Cloud computing is a fast-growing technology that has
established itself in the next generation of IT industry
and business.
• Cloud computing promises reliable software, hardware,
and IaaS(Infrastructure as a Service) delivered over the
Internet and remote data centers .
• Cloud services have become a powerful architecture
to perform complex large-scale computing tasks and
span a range of IT functions from storage and
computation to database and application services.
11. Relationship between cloud
& big data
• Cloud computing and big data are conjoined.
• Big data provides users the ability to use commodity
computing to process distributed queries across
multiple datasets and return resultant sets in a timely
manner.
• Cloud computing provides the underlying through the
use of Hadoop, a class of distributed data-processing
platforms.
• Big data utilizes distributed storage technology based
on cloud computing rather than local storage attached
to a computer or electronic device.
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15. Big data storage system
• A storage architecture that can be accessed in a highly efficient
manner while achieving availabilityand reliability is required to store
and manage large datasets.
• Existing technologies can be classified as direct attached storage
(DAS), network attached storage (NAS), and storage area network
(SAN).
• The organizational systems of data storages (DAS, NAS, and SAN)
can be divided into three parts:
– (i) disc array, where the foundation of a storage system provides the
fundamental guarantee
– (ii) connection and network subsystems, which connect one or more
disc arrays and servers
– (iii) storage management software, which oversees data sharing,
storage management, and disaster recovery tasks for multiple servers.
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16. background
• Hadoop is an open-source software framework
for storing data and running applications on
clusters of commodity hardware. It provides
massive storage for any kind of data, enormous
processing power and the ability to handle
virtually limitless concurrent tasks or jobs
• The software framework that supports HDFS,
MapReduce and other related entities is called
the project Hadoop or simply Hadoop.
• This is open source and distributed by Apache
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17. Features of Hadoop
I. Cost Effective System
II. Large Cluster of Notes
III. Parallel Processing
IV. Distributive Data
V. Automatic failover management
VI. Data Locality optimization
VII.Heterogeneous Cluster
VIII.Scalability
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18. Research Challenges
I. Scalability = Scalability is the ability of the storage to handle increasing
amounts of data in an appropriate manner.
II. Availability = Availability refers to the resources of the system accessible on
demand by an authorized individual
III. Data integrity = A key aspect of big data security is integrity. Integrity means
that data can be modified only by authorized parties or the data owner to
prevent misuse.
IV. Transformation = Transforming data into a form suitable for analysis is an
obstacle in the adoption of big data.
V. Data quality = In the past, data processing was typically performed on clean
datasets from well-known and limited sources.
VI. Heterogeneity = Variety, one of the major aspects of big data characterization,
is the result of the growth of virtually unlimited different sources of data.
VII.Privacy = Privacy concerns continue to hamper users who outsource their
private data into the cloud storage.
VIII.Legal issues = Specific laws and regulations must be established to preserve
the personal and sensitive information of users.
IX. Governance = Data governance embodies the exercise of control and
authority over data-related rules of law, transparency. 18
19. Conclusion
• Two IT initiatives are currently top of mind for
organizations across the globe i.e.
– Big Data Analytics
– Cloud Computing
As a delivery model for IT services , cloud computing has the
potential to enhance business agility and productivity while
enabling greater efficiencies and reducing costs.
• In the current scenario , Big Data is a big challenge for the
organizations . To store and process such large volume of
data , variety of data and velocity of data Hadoop came into
existence.
• Our presentation is all about Cloud Computing , Big Data &
Big Data Analytics.
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