Big data is used for structured, unstructured and semi-structured large volume of data which is difficult to
manage and costly to store. Using explanatory analysis techniques to understand such raw data, carefully
balance the benefits in terms of storage and retrieval techniques is an essential part of the Big Data. The
research discusses the Map Reduce issues, framework for Map Reduce programming model and
implementation. The paper includes the analysis of Big Data using Map Reduce techniques and identifying
a required document from a stream of documents. Identifying a required document is part of the security in
a stream of documents in the cyber world. The document may be significant in business, medical, social, or
terrorism.
Big data is the term for any gathering of information sets, so expensive and complex, that it gets to be hard to process for utilizing customary information handling applications. The difficulties incorporate investigation, catch, duration, inquiry, sharing, stockpiling, Exchange, perception, and protection infringement. To reduce spot business patterns, anticipate diseases, conflict etc., we require bigger data sets when compared with the smaller data sets. Enormous information is hard to work with utilizing most social database administration frameworks and desktop measurements and perception bundles, needing rather enormously parallel programming running on tens, hundreds, or even a large number of servers. In this paper there was an observation on Hadoop architecture, different tools used for big data and its security issues.
The document discusses Big Data architectures and Oracle's solutions for Big Data. It provides an overview of key components of Big Data architectures, including data ingestion, distributed file systems, data management capabilities, and Oracle's unified reference architecture. It describes techniques for operational intelligence, exploration and discovery, and performance management in Big Data solutions.
This document outlines the top 10 big data security and privacy challenges as identified by the Cloud Security Alliance. It discusses each challenge in terms of use cases. The challenges are: 1) secure computations in distributed programming frameworks, 2) security best practices for non-relational data stores, 3) secure data storage and transaction logs, 4) end-point input validation/filtering, 5) real-time security/compliance monitoring, 6) scalable and composable privacy-preserving data mining and analytics, 7) cryptographically enforced access control and secure communication, 8) granular access control, 9) granular audits, and 10) data provenance. Each challenge is described briefly and accompanied by example use cases.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable and distributed processing of large datasets. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing vast amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. HDFS stores data reliably across machines in a Hadoop cluster and MapReduce processes data in parallel by breaking the job into smaller fragments of work executed across cluster nodes.
A Review Paper on Big Data and Hadoop for Data Scienceijtsrd
Â
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 Processing with Hadoop : A ReviewIRJET Journal
Â
1. This document provides an overview of big data processing with Hadoop. It defines big data and describes the challenges of volume, velocity, variety and variability.
2. Traditional data processing approaches are inadequate for big data due to its scale. Hadoop provides a distributed file system called HDFS and a MapReduce framework to address this.
3. HDFS uses a master-slave architecture with a NameNode and DataNodes to store and retrieve file blocks. MapReduce allows distributed processing of large datasets across clusters through mapping and reducing functions.
This document discusses big data and Hadoop. It begins with an introduction to big data, noting the volume, variety and velocity of data. It then provides an overview of Hadoop, including its core components HDFS for storage and MapReduce for processing. The document also outlines some of the key challenges of big data including heterogeneity, scale, timeliness, privacy and the need for human collaboration in analysis. It concludes by discussing how Hadoop provides a solution for big data processing through its distributed architecture and use of HDFS and MapReduce.
This document provides an overview of big data concepts including definitions of big data, characteristics of big data using the 5Vs model, common big data technologies like Hadoop and MapReduce, and use cases. It discusses how big data has evolved over time through increased data volumes and varieties. Key frameworks like HDFS and MapReduce that enable distributed storage and processing of large datasets are explained. Examples of big data applications in areas such as banking are also provided.
Big data is the term for any gathering of information sets, so expensive and complex, that it gets to be hard to process for utilizing customary information handling applications. The difficulties incorporate investigation, catch, duration, inquiry, sharing, stockpiling, Exchange, perception, and protection infringement. To reduce spot business patterns, anticipate diseases, conflict etc., we require bigger data sets when compared with the smaller data sets. Enormous information is hard to work with utilizing most social database administration frameworks and desktop measurements and perception bundles, needing rather enormously parallel programming running on tens, hundreds, or even a large number of servers. In this paper there was an observation on Hadoop architecture, different tools used for big data and its security issues.
The document discusses Big Data architectures and Oracle's solutions for Big Data. It provides an overview of key components of Big Data architectures, including data ingestion, distributed file systems, data management capabilities, and Oracle's unified reference architecture. It describes techniques for operational intelligence, exploration and discovery, and performance management in Big Data solutions.
This document outlines the top 10 big data security and privacy challenges as identified by the Cloud Security Alliance. It discusses each challenge in terms of use cases. The challenges are: 1) secure computations in distributed programming frameworks, 2) security best practices for non-relational data stores, 3) secure data storage and transaction logs, 4) end-point input validation/filtering, 5) real-time security/compliance monitoring, 6) scalable and composable privacy-preserving data mining and analytics, 7) cryptographically enforced access control and secure communication, 8) granular access control, 9) granular audits, and 10) data provenance. Each challenge is described briefly and accompanied by example use cases.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable and distributed processing of large datasets. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing vast amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. HDFS stores data reliably across machines in a Hadoop cluster and MapReduce processes data in parallel by breaking the job into smaller fragments of work executed across cluster nodes.
A Review Paper on Big Data and Hadoop for Data Scienceijtsrd
Â
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 Processing with Hadoop : A ReviewIRJET Journal
Â
1. This document provides an overview of big data processing with Hadoop. It defines big data and describes the challenges of volume, velocity, variety and variability.
2. Traditional data processing approaches are inadequate for big data due to its scale. Hadoop provides a distributed file system called HDFS and a MapReduce framework to address this.
3. HDFS uses a master-slave architecture with a NameNode and DataNodes to store and retrieve file blocks. MapReduce allows distributed processing of large datasets across clusters through mapping and reducing functions.
This document discusses big data and Hadoop. It begins with an introduction to big data, noting the volume, variety and velocity of data. It then provides an overview of Hadoop, including its core components HDFS for storage and MapReduce for processing. The document also outlines some of the key challenges of big data including heterogeneity, scale, timeliness, privacy and the need for human collaboration in analysis. It concludes by discussing how Hadoop provides a solution for big data processing through its distributed architecture and use of HDFS and MapReduce.
This document provides an overview of big data concepts including definitions of big data, characteristics of big data using the 5Vs model, common big data technologies like Hadoop and MapReduce, and use cases. It discusses how big data has evolved over time through increased data volumes and varieties. Key frameworks like HDFS and MapReduce that enable distributed storage and processing of large datasets are explained. Examples of big data applications in areas such as banking are also provided.
This document provides an overview of big data, including its components of variety, volume, and velocity. It discusses frameworks for managing big data like Hadoop and HPCC, describing how Hadoop uses HDFS for storage and MapReduce for processing, while HPCC uses its own data refinery and delivery engine. Examples are given of big data sources and applications. Privacy and security issues are also addressed.
How Data Virtualization Puts Enterprise Machine Learning Programs into Produc...Denodo
Â
Watch full webinar here: https://bit.ly/3offv7G
Presented at AI Live APAC
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Watch this on-demand session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc.
This document provides an agenda for an event on managing dark data and information governance. The agenda includes presentations on topics such as defining dark data, using HP ControlPoint software to find and analyze unstructured enterprise data, using AI to analyze database and application content, securing and managing data with HP Records Manager, and hosting data in the HP Cloud. It also includes a demonstration of ControlPoint and Records Manager, as well as a Q&A session.
Big data offers opportunities but also security and privacy issues due to its large volume, velocity, and variety. Some key security issues include insecure computation, lack of input validation and filtering, and privacy concerns in data mining and analytics. Recommendations to enhance big data security include securing computation code, implementing comprehensive input validation and filtering, granular access controls, and securing data storage and computation. Case studies on security issues include vulnerability to fake data generation, challenges with Amazon's data lakes, possibility of sensitive information mining, and the rapid evolution of NoSQL databases lacking security focus.
Data sciences is the topnotch in our world now as it enables us to predict the future and behaviors of people and systems alike.
Hence, this course focuses on introducing the processing involved in data sciences.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
Â
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
The Big Data Importance â Tools and their UsageIRJET Journal
Â
This document discusses big data, tools for analyzing big data, and opportunities that big data analytics provides. It begins by defining big data and its key characteristics of volume, variety and velocity. It then discusses tools for storing, managing and processing big data like Hadoop, MapReduce and HDFS. Finally, it outlines how big data analytics can be applied across different domains to enable new insights and informed decision making through analyzing large datasets.
Big data analytics - Introduction to Big Data and HadoopSamiraChandan
Â
This document provides an introduction to big data and Hadoop. It defines big data as large and complex data sets that are difficult to process using traditional methods. It describes the characteristics of big data using the 5 V's model and discusses the importance of big data analytics. The document also outlines the differences between traditional and big data, and describes the types and components of Hadoop, including HDFS, MapReduce, YARN and Hadoop common. It provides examples of the Hadoop ecosystem and discusses the stages of big data processing.
This presentation briefly discusses the following topics:
Classification of Data
What is Structured Data?
What is Unstructured Data?
What is Semistructured Data?
Structured vs Unstructured Data: 5 Key Differences
1. The document discusses data warehousing and data mining. Data warehousing involves collecting and integrating data from multiple sources to support analysis and decision making. Data mining involves analyzing large datasets to discover patterns.
2. Web mining is discussed as a type of data mining that analyzes web data. There are three domains of web mining: web content mining, web structure mining, and web usage mining. Common techniques for web mining include clustering, association rules, path analysis, and sequential patterns.
3. Web mining has benefits like addressing ineffective search engines and monitoring user visit habits to improve website design. Data warehousing and data mining can provide useful business intelligence when the right analysis techniques are applied to large amounts of integrated
This document provides an introduction to big data analytics. It discusses what big data is, key concepts and terminology, the characteristics of big data including the five Vs, different types of data, and case study background. It also covers big data drivers like marketplace dynamics, business architecture, and information and communications technology. The slides include information on data analytics categories, business intelligence, key performance indicators, and how big data relates to business layers and the feedback loop.
This document summarizes a literature review paper on big data analytics. It begins by defining big data as large datasets that are difficult to handle with traditional tools due to their size, variety, and velocity. It then discusses how big data analytics applies advanced analytics techniques to big data to extract valuable insights. The paper reviews literature on big data analytics tools and methods for storage, management, and analysis of big data. It also discusses opportunities that big data analytics provides for decision making in various domains.
The document provides an overview of key concepts in data science including data types, the data value chain, and big data. It defines data science as extracting insights from large, diverse datasets using tools like machine learning. The data value chain involves acquiring, processing, analyzing and using data. Big data is characterized by its volume, velocity and variety. Common techniques for big data analytics include data mining, machine learning and visualization.
This document discusses the evolution from traditional RDBMS to big data analytics. As data volumes grow rapidly, traditional RDBMS struggle to store and process large amounts of data. Hadoop provides a framework to store and process big data across commodity hardware. Key components of Hadoop include HDFS for distributed storage, MapReduce for distributed processing, Hive for SQL-like queries, and Sqoop for transferring data between Hadoop and relational databases. The document also outlines some applications and limitations of Hadoop.
1. The document discusses Big Data analytics using Hadoop. It defines Big Data and explains the characteristics of volume, velocity, and variety.
2. Hadoop is introduced as a framework for distributed storage and processing of large data sets across clusters of commodity hardware. It uses HDFS for reliable storage and streaming of large data sets.
3. Key Hadoop components are the NameNode, which manages file system metadata, and DataNodes, which store and retrieve data blocks. Hadoop provides scalability, fault tolerance, and high performance on large data sets.
This document outlines the phases of the data analytics lifecycle, with a focus on Phase 1: Discovery. The Discovery phase involves understanding the business problem, available resources, and formulating initial hypotheses to test. Key activities in Discovery include interviewing stakeholders, learning the domain, assessing available data and tools, and framing the business and analytics problems. The goal is to have enough information to draft an analytic plan and scope the project before moving to the next phase of data preparation.
Data Mining and Business Intelligence ToolsMotaz Saad
Â
This document provides an outline for a presentation on data mining and business intelligence. It discusses why data mining is important due to the explosive growth of data from various sources like business transactions, scientific research, and social media. It also gives an overview of some popular open source and non-open source data mining tools, including WEKA, Rapid Miner, SPSS, SQL Server Analysis Services, and Oracle Data Miner.
The document discusses key concepts related to big data including what data and big data are, the three structures of big data (volume, velocity, and variety), sources and types of big data, how big data differs from traditional databases, applications of big data across various fields such as healthcare and social media, tools for working with big data like Hadoop and MongoDB, and challenges and solutions related to big data.
6.a survey on big data challenges in the context of predictiveEditorJST
Â
Information is producing from various assets in a quick fashion. In request to know how much information is advancing we require predictive analytics. When the information is semi organized or unstructured the ordinary business insight calculations or instruments are not useful. In this paper, we have attempted to call attention to the difficulties when we utilize business knowledge devices
The document discusses big data testing using the Hadoop platform. It describes how Hadoop, along with technologies like HDFS, MapReduce, YARN, Pig, and Spark, provides tools for efficiently storing, processing, and analyzing large volumes of structured and unstructured data distributed across clusters of machines. These technologies allow organizations to leverage big data to gain valuable insights by enabling parallel computation of massive datasets.
Big Data refers to the bulk amount of data while Hadoop is a framework to process this data.
There are various technologies and fields under Big Data. Big Data finds its applications in various areas like healthcare, military and various other fields.
http://www.techsparks.co.in/thesis-topics-in-big-data-and-hadoop/
This document provides an overview of big data, including its components of variety, volume, and velocity. It discusses frameworks for managing big data like Hadoop and HPCC, describing how Hadoop uses HDFS for storage and MapReduce for processing, while HPCC uses its own data refinery and delivery engine. Examples are given of big data sources and applications. Privacy and security issues are also addressed.
How Data Virtualization Puts Enterprise Machine Learning Programs into Produc...Denodo
Â
Watch full webinar here: https://bit.ly/3offv7G
Presented at AI Live APAC
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Watch this on-demand session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc.
This document provides an agenda for an event on managing dark data and information governance. The agenda includes presentations on topics such as defining dark data, using HP ControlPoint software to find and analyze unstructured enterprise data, using AI to analyze database and application content, securing and managing data with HP Records Manager, and hosting data in the HP Cloud. It also includes a demonstration of ControlPoint and Records Manager, as well as a Q&A session.
Big data offers opportunities but also security and privacy issues due to its large volume, velocity, and variety. Some key security issues include insecure computation, lack of input validation and filtering, and privacy concerns in data mining and analytics. Recommendations to enhance big data security include securing computation code, implementing comprehensive input validation and filtering, granular access controls, and securing data storage and computation. Case studies on security issues include vulnerability to fake data generation, challenges with Amazon's data lakes, possibility of sensitive information mining, and the rapid evolution of NoSQL databases lacking security focus.
Data sciences is the topnotch in our world now as it enables us to predict the future and behaviors of people and systems alike.
Hence, this course focuses on introducing the processing involved in data sciences.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
Â
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
The Big Data Importance â Tools and their UsageIRJET Journal
Â
This document discusses big data, tools for analyzing big data, and opportunities that big data analytics provides. It begins by defining big data and its key characteristics of volume, variety and velocity. It then discusses tools for storing, managing and processing big data like Hadoop, MapReduce and HDFS. Finally, it outlines how big data analytics can be applied across different domains to enable new insights and informed decision making through analyzing large datasets.
Big data analytics - Introduction to Big Data and HadoopSamiraChandan
Â
This document provides an introduction to big data and Hadoop. It defines big data as large and complex data sets that are difficult to process using traditional methods. It describes the characteristics of big data using the 5 V's model and discusses the importance of big data analytics. The document also outlines the differences between traditional and big data, and describes the types and components of Hadoop, including HDFS, MapReduce, YARN and Hadoop common. It provides examples of the Hadoop ecosystem and discusses the stages of big data processing.
This presentation briefly discusses the following topics:
Classification of Data
What is Structured Data?
What is Unstructured Data?
What is Semistructured Data?
Structured vs Unstructured Data: 5 Key Differences
1. The document discusses data warehousing and data mining. Data warehousing involves collecting and integrating data from multiple sources to support analysis and decision making. Data mining involves analyzing large datasets to discover patterns.
2. Web mining is discussed as a type of data mining that analyzes web data. There are three domains of web mining: web content mining, web structure mining, and web usage mining. Common techniques for web mining include clustering, association rules, path analysis, and sequential patterns.
3. Web mining has benefits like addressing ineffective search engines and monitoring user visit habits to improve website design. Data warehousing and data mining can provide useful business intelligence when the right analysis techniques are applied to large amounts of integrated
This document provides an introduction to big data analytics. It discusses what big data is, key concepts and terminology, the characteristics of big data including the five Vs, different types of data, and case study background. It also covers big data drivers like marketplace dynamics, business architecture, and information and communications technology. The slides include information on data analytics categories, business intelligence, key performance indicators, and how big data relates to business layers and the feedback loop.
This document summarizes a literature review paper on big data analytics. It begins by defining big data as large datasets that are difficult to handle with traditional tools due to their size, variety, and velocity. It then discusses how big data analytics applies advanced analytics techniques to big data to extract valuable insights. The paper reviews literature on big data analytics tools and methods for storage, management, and analysis of big data. It also discusses opportunities that big data analytics provides for decision making in various domains.
The document provides an overview of key concepts in data science including data types, the data value chain, and big data. It defines data science as extracting insights from large, diverse datasets using tools like machine learning. The data value chain involves acquiring, processing, analyzing and using data. Big data is characterized by its volume, velocity and variety. Common techniques for big data analytics include data mining, machine learning and visualization.
This document discusses the evolution from traditional RDBMS to big data analytics. As data volumes grow rapidly, traditional RDBMS struggle to store and process large amounts of data. Hadoop provides a framework to store and process big data across commodity hardware. Key components of Hadoop include HDFS for distributed storage, MapReduce for distributed processing, Hive for SQL-like queries, and Sqoop for transferring data between Hadoop and relational databases. The document also outlines some applications and limitations of Hadoop.
1. The document discusses Big Data analytics using Hadoop. It defines Big Data and explains the characteristics of volume, velocity, and variety.
2. Hadoop is introduced as a framework for distributed storage and processing of large data sets across clusters of commodity hardware. It uses HDFS for reliable storage and streaming of large data sets.
3. Key Hadoop components are the NameNode, which manages file system metadata, and DataNodes, which store and retrieve data blocks. Hadoop provides scalability, fault tolerance, and high performance on large data sets.
This document outlines the phases of the data analytics lifecycle, with a focus on Phase 1: Discovery. The Discovery phase involves understanding the business problem, available resources, and formulating initial hypotheses to test. Key activities in Discovery include interviewing stakeholders, learning the domain, assessing available data and tools, and framing the business and analytics problems. The goal is to have enough information to draft an analytic plan and scope the project before moving to the next phase of data preparation.
Data Mining and Business Intelligence ToolsMotaz Saad
Â
This document provides an outline for a presentation on data mining and business intelligence. It discusses why data mining is important due to the explosive growth of data from various sources like business transactions, scientific research, and social media. It also gives an overview of some popular open source and non-open source data mining tools, including WEKA, Rapid Miner, SPSS, SQL Server Analysis Services, and Oracle Data Miner.
The document discusses key concepts related to big data including what data and big data are, the three structures of big data (volume, velocity, and variety), sources and types of big data, how big data differs from traditional databases, applications of big data across various fields such as healthcare and social media, tools for working with big data like Hadoop and MongoDB, and challenges and solutions related to big data.
6.a survey on big data challenges in the context of predictiveEditorJST
Â
Information is producing from various assets in a quick fashion. In request to know how much information is advancing we require predictive analytics. When the information is semi organized or unstructured the ordinary business insight calculations or instruments are not useful. In this paper, we have attempted to call attention to the difficulties when we utilize business knowledge devices
The document discusses big data testing using the Hadoop platform. It describes how Hadoop, along with technologies like HDFS, MapReduce, YARN, Pig, and Spark, provides tools for efficiently storing, processing, and analyzing large volumes of structured and unstructured data distributed across clusters of machines. These technologies allow organizations to leverage big data to gain valuable insights by enabling parallel computation of massive datasets.
Big Data refers to the bulk amount of data while Hadoop is a framework to process this data.
There are various technologies and fields under Big Data. Big Data finds its applications in various areas like healthcare, military and various other fields.
http://www.techsparks.co.in/thesis-topics-in-big-data-and-hadoop/
The document discusses big data analysis and provides an introduction to key concepts. It is divided into three parts: Part 1 introduces big data and Hadoop, the open-source software framework for storing and processing large datasets. Part 2 provides a very quick introduction to understanding data and analyzing data, intended for those new to the topic. Part 3 discusses concepts and references to use cases for big data analysis in the airline industry, intended for more advanced readers. The document aims to familiarize business and management users with big data analysis terms and thinking processes for formulating analytical questions to address business problems.
Cloud Analytics Ability to Design, Build, Secure, and Maintain Analytics Solu...YogeshIJTSRD
Â
Cloud Analytics is another area in the IT field where different services like Software, Infrastructure, storage etc. are offered as services online. Users of cloud services are under constant fear of data loss, security threats, and availability issues. However, the major challenge in these methods is obtaining real time and unbiased datasets. Many datasets are internal and cannot be shared due to privacy issues or may lack certain statistical characteristics. As a result of this, researchers prefer to generate datasets for training and testing purposes in simulated or closed experimental environments which may lack comprehensiveness. Advances in sensor technology, the Internet of things IoT , social networking, wireless communications, and huge collection of data from years have all contributed to a new field of study Big Data is discussed in this paper. Through this analysis and investigation, we provide recommendations for the research public on future directions on providing data based decisions for cloud supported Big Data computing and analytic solutions. This paper concentrates upon the recent trends in Big Data storage and analysing, in the clouds, and also points out the security limitations. Rajan Ramvilas Saroj "Cloud Analytics: Ability to Design, Build, Secure, and Maintain Analytics Solutions on the Cloud" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd43728.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/43728/cloud-analytics-ability-to-design-build-secure-and-maintain-analytics-solutions-on-the-cloud/rajan-ramvilas-saroj
This document discusses big data and tools for analyzing big data. It defines big data as very large data sets that are difficult to analyze using traditional data management systems due to the volume, variety and velocity of the data. It describes characteristics of big data including the 5 V's: volume, variety, velocity, veracity and value. It also discusses some common tools for analyzing big data, including MapReduce, Apache Hadoop, Apache Mahout and Apache Spark. Finally, it briefly mentions the potential of quantum computing for big data analysis by being able to process exponentially large amounts of data simultaneously.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
Â
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
Big Data is a much talked about technology across businesses today. A vast majority of organizations spanning across industries are convinced of its usefulness, but the implementation focus is primarily application oriented than infrastructure oriented.
This document discusses security issues with Hadoop and available solutions. It identifies vulnerabilities in Hadoop including lack of authentication, unsecured data in transit, and unencrypted data at rest. It describes current solutions like Kerberos for authentication, SASL for encrypting data in motion, and encryption zones for encrypting data at rest. However, it notes limitations of encryption zones for processing encrypted data efficiently with MapReduce. It proposes a novel method for large scale encryption that can securely process encrypted data in Hadoop.
This document discusses big data and Hadoop. It defines big data as high volume data that cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework that can store and process large data sets across clusters of commodity hardware. It has two main components - HDFS for storage and MapReduce for distributed processing. HDFS stores data across clusters and replicates it for fault tolerance, while MapReduce allows data to be mapped and reduced for analysis.
Big data is a broad term for data sets so large or complex that tr.docxhartrobert670
Â
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set.
Analysis of data sets can find new correlations, to "spot business trends, prevent diseases, combat crime and so on."[1] Scientists, practitioners of media and advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[2]connectomics, complex physics simulations,[3] and biological and environmental research.[4]
Data sets grow in size in part because they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers, and wireless sensor networks.[5]
HYPERLINK "http://en.wikipedia.org/wiki/Big_data" \l "cite_note-6" [6]
HYPERLINK "http://en.wikipedia.org/wiki/Big_data" \l "cite_note-7" [7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5Ă1018) of data were created;[9] The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.[10]
Work with big data is necessarily uncommon; most analysis is of "PC size" data, on a desktop PC or notebook[11] that can handle the available data set.
Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires "massively parallel software running on tens, hundreds, or even thousands of servers".[12] What is considered "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make Big Data a moving target. Thus, what is considered to be "Big" in one year will become ordinary in later years. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."[13]
Contents
¡ 1 Definition
¡ 2 Characteristics
¡ 3 Architecture
¡ 4 Technologies
¡ 5 Applications
¡ 5.1 Government
¡ 5.1.1 United States of America
¡ 5.1.2 India
¡ 5.1.3 United Kingdom
¡ 5.2 International development
¡ 5.3 Manufacturing
¡ 5.3.1 Cyber-Physical Models
¡ 5.4 Media
¡ 5.4.1 Internet of Things (IoT)
¡ 5.4.2 Technology
¡ 5.5 Private sector
¡ 5.5.1 Retail
¡ 5.5.2 Retail Banking
¡ 5.5.3 Real Estate
¡ 5.6 Science
¡ 5.6.1 Science and Resear ...
using big-data methods analyse the Cross platform aviationranjit banshpal
Â
This document discusses using big data analytics methods to address issues in the aviation industry. It defines big data and explains why it is needed due to the large and diverse datasets in aviation. Traditional data mining techniques are ineffective on heterogeneous aviation data. The document proposes using cloud-based big data analytics platforms like masFlight to integrate diverse aviation data sources in real-time and perform fast data mining to help with operations planning and research. This can help address key issues in aviation around data standardization, normalization and scalability.
This document discusses scheduling algorithms for processing big data using Hadoop. It provides background on big data and Hadoop, including that big data is characterized by volume, velocity, and variety. Hadoop uses MapReduce and HDFS to process and store large datasets across clusters. The default scheduling algorithm in Hadoop is FIFO, but performance can be improved using alternative scheduling algorithms. The objective is to study and analyze various scheduling algorithms that could increase performance for big data processing in Hadoop.
Big data refers to massive amounts of structured and unstructured data that is difficult to process using traditional databases. It is characterized by volume, variety, velocity, and veracity. Major sources of big data include social media posts, videos uploaded, app downloads, searches, and tweets. Trends in big data include increased use of sensors, tools for non-data scientists, in-memory databases, NoSQL databases, Hadoop, cloud storage, machine learning, and self-service analytics. Big data has applications in banking, media, healthcare, energy, manufacturing, education, and transportation for tasks like fraud detection, personalized experiences, reducing costs, predictive maintenance, measuring teacher effectiveness, and traffic control.
Fundamentals of data mining and its applicationsSubrat Swain
Â
Data mining involves applying intelligent methods to extract patterns from large data sets. It is used to discover useful knowledge from a variety of data sources. The overall goal is to extract human-understandable knowledge that can be used for decision-making.
The document discusses the data mining process, which typically involves problem definition, data exploration, data preparation, modeling, evaluation, and deployment. It also covers data mining software tools and techniques for ensuring privacy, such as randomization and k-anonymity. Finally, it outlines several applications of data mining in fields like industry, science, music, and more.
This document provides an overview of big data concepts including:
- Mohamed Magdy's background and credentials in big data engineering and data science.
- Definitions of big data, the three V's of big data (volume, velocity, variety), and why big data analytics is important.
- Descriptions of Hadoop, HDFS, MapReduce, and YARN - the core components of Hadoop architecture for distributed storage and processing of big data.
- Explanations of HDFS architecture, data blocks, high availability in HDFS 2/3, and erasure coding in HDFS 3.
This document provides an overview of big data. It begins with an introduction that defines big data as massive, complex data sets from various sources that are growing rapidly in volume and variety. It then discusses the brief history of big data and provides definitions, describing big data as data that is too large and complex for traditional data management tools. The document outlines key aspects of big data including the sources, types, applications, and characteristics. It discusses how big data is used in business intelligence to help companies make better decisions. Finally, it describes the key aspects a big data platform must address such as handling different data types, large volumes, and analytics.
REAL-TIME INTRUSION DETECTION SYSTEM FOR BIG DATAijp2p
Â
The objective of the proposed system is to integrate the high volume of data along with the important
considerations like monitoring a wide array of heterogeneous security. When a real time cyber attack
occurred, the Intrusion Detection System automatically store the log in distributed environment and
monitor the log with existing intrusion dictionary. At the same time the system will check and categorize the
severity of the log to high, medium, and low respectively. After the categorization, the system will
automatically take necessary action against the user-unit with respect to the severity of the log. The
advantage of the system is that it utilize anomaly detection, evaluates data and issue alert message or
reports based on abnormal behaviour.
Companies are increasingly using big data technologies like Hadoop to store and analyze large amounts of customer data to gain insights. This raises security issues as more data is collected and needs to be properly classified and owned. Big data is also being used for fraud detection and security event management to replace traditional SIEM systems that are difficult for IT departments to manage. While big data can process structured and unstructured data at large scales, specialized skills are required like expertise in Hadoop, data mining, and analyzing various data types.
Big Data Tools: A Deep Dive into Essential ToolsFredReynolds2
Â
Today, practically every firm uses big data to gain a competitive advantage in the market. With this in mind, freely available big data tools for analysis and processing are a cost-effective and beneficial choice for enterprises. Hadoop is the sectorâs leading open-source initiative and big data tidal roller. Moreover, this is not the final chapter! Numerous other businesses pursue Hadoopâs free and open-source path.
This document discusses big data workflows. It begins by defining big data and workflows, noting that workflows are task-oriented processes for decision making. Big data workflows require many servers to run one application, unlike traditional IT workflows which run on one server. The document then covers the 5Vs and 1C characteristics of big data: volume, velocity, variety, variability, veracity, and complexity. It lists software tools for big data platforms, business analytics, databases, data mining, and programming. Challenges of big data are also discussed: dealing with size and variety of data, scalability, analysis, and management issues. Major application areas are listed in private sector domains like retail, banking, manufacturing, and government.
Similar to DOCUMENT SELECTION USING MAPREDUCE (20)
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Â
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
QA or the Highway - Component Testing: Bridging the gap between frontend appl...zjhamm304
Â
These are the slides for the presentation, "Component Testing: Bridging the gap between frontend applications" that was presented at QA or the Highway 2024 in Columbus, OH by Zachary Hamm.
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
đ Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
đť Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
"NATO Hackathon Winner: AI-Powered Drug Search", Taras KlobaFwdays
Â
This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
High performance Serverless Java on AWS- GoTo Amsterdam 2024Vadym Kazulkin
Â
Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM âisâ and âisnâtâ
- Understand the value of KM and the benefits of engaging
- Define and reflect on your âwhatâs in it for me?â
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
Â
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...DanBrown980551
Â
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energyâs Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
-Customer data, centering around customer tariffs, bills, energy usage, and full consumption disclosure
-Power systems data, focusing on grid data, inclusive of transmission and distribution networks, generation, intergrid power flows, and market settlement data
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Â
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Â
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
Â
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
Â
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Introduction of Cybersecurity with OSS at Code Europe 2024
Â
DOCUMENT SELECTION USING MAPREDUCE
1. International Journal of Security, Privacy and Trust Management (IJSPTM) Vol 4, No 3/4, November 2015
DOI : 10.5121/ijsptm.2015.4401 1
DOCUMENT SELECTION USING MAPREDUCE
Yenumula B Reddy and Desmond Hill
Department of Computer Science, Grambling State University, Grambling, LA 71245,
USA
ABSTRACT
Big data is used for structured, unstructured and semi-structured large volume of data which is difficult to
manage and costly to store. Using explanatory analysis techniques to understand such raw data, carefully
balance the benefits in terms of storage and retrieval techniques is an essential part of the Big Data. The
research discusses the MapReduce issues, framework for MapReduce programming model and
implementation. The paper includes the analysis of Big Data using MapReduce techniques and identifying
a required document from a stream of documents. Identifying a required document is part of the security in
a stream of documents in the cyber world. The document may be significant in business, medical, social, or
terrorism.
KEYWORDS
HDFS, MapReduce, Big Data, Document identification, client node, data node, name node;
1. INTRODUCTION
Big data is a general term used for a large volume of data that are structured, unstructured and
semi-structured data created or generated by a company [1]. This data cannot be loaded using any
database models and is not possible to get results with available query languages. That means the
big data cannot be processed using traditional tools. The data is not unique quantity in terms of
many bytes (terra-bytes or petabytes or exabytes). It is continuously growing every minute. It is
real big and grows in exabytes. The origins are from a variety of platforms. Volume changes
quickly and cannot be predicted its growth. It is expensive to analyze, organize, and to prepare as
useful data. Therefore, we need a special effort to prepare as meaningful by using new algorithms,
special hardware, and software. There may not be a single tool to analyze and create big data as
meaningful data. The tools may vary to analyze one type of data to another. The big data
management requires generating high quality of data to answer the business queries.
The primary goal is to discover the repeatable business patterns, uncover the hidden patterns, and
understand the unknown correlations and other useful information. The business giants have
access to the information, but they do not know to get the value out of it. The traditional tools are
not helpful due to semi-structured storage. The big data analytics may commonly use software
tools such as predictive analytics and data mining. The technology associated with big data
analytics includes NoSQL databases, Hadoop, and MapReduce [2]. There are open source
frameworks that support processing of big data. The analysis requires MapReduce to distribute
the work among a large number of computers and processes in real-time. Hadoop structure lowers
the risk of catastrophic system failure, even the significant number of nodes become inoperative.
2. International Journal of Security, Privacy and Trust Management (IJSPTM) Vol 4, No 3/4, November 2015
2
The Government and information technology (IT) managers are highly motivated to turn big data
into an asset so that the data can be used to meet their business requirements. Today Hadoop
framework and MapReduce offer new technology to process and transform big data into
meaningful data. It is required to deploy the infrastructure differently to support the distributed
processing and meet the real-time demands. The IT managers found that security and privacy are
the major problems particularly while using a third party cloud service providers.
The structured and unstructured data comes from a variety of sources. Adoption of big data tools
to process the big data is increasing. High priority is given to improve the big data formalizing
and processing. The top data for transactions include business data documents, email, sensor data,
image data, Weblogs, Internet search indexing, and attached files. The IT group found that
interest in learning about technology, deploy the packages to process the data and adopt the
infrastructure for better performance and affordable cost. They are in the process of implementing
Apache Hadoop frameworks, commercial distributions of the Hadoop framework and other
NoSQL databases.
Currently, priority is given to processing and analyzing unstructured data resources including
Weblogs, social media, e-mail, photos, and videos. Unstructured emails are given priority to
analyze and process. As a first step, the IT staff is working on batch processing and move to real-
time process. The industries and Government are concerned about the scalability, low latency,
and performance in storing and analyzing the data. Further, they are worried about the protection
of data for third-party cloud providers. The data privacy, security, and interoperability standards
are necessary for data and systems management.
The big data is useful if analyzed, stored in a meaningful way and accessed quickly. The main
goal is to store the data to ensure that the information is accessible to business intelligence and
analysis. It is required to design a tool to analyze the data and provide the answers with minimum
efforts and time. The challenges include the size, structure, origin, variety, and continuous change
in data. The data is real big in size (terra-bytes or eta-bytes) and unstructured. It contains text,
figures, videos, tweets, Facebook posts, website clicks, and various types of data from a variety of
websites. The origins are varied and come from a variety of platforms and multiple touch points.
Data changes quickly in terms of format, size, and types of Websites. The software required to
pull, sort, and make the data meaningful. There is no universal solution to make such data
meaningful. The data produced is in universally available languages. Processing such data may
need separate algorithms (depending upon the type of data). There are multiple predictions in
years to come about the data coming from an unknown source and unstructured in nature. The
following predictions include the data origin, type, size, and management.
⢠Massive data collection across multiple points
⢠Firmer Grip on the data collected by various groups
⢠Generalization of format for Internet data and data generated by business media
⢠Entering of social media as part of Big Data generation
The main purpose of big data management is to make sense of the collected data by analyzing and
processing that collected data from various data collection points. Making sense of data means
that the end point of processing of data must be able to answer the business queries. The queries
include data mining related predictions, business queries, and management assistance.
3. International Journal of Security, Privacy and Trust Management (IJSPTM) Vol 4, No 3/4, November 2015
3
The Hadoop project was adopted more in the DoD (Department of Defense) than in other
agencies. The problem in Hadoop system design is the lack of reusability of existing applications
since the Apache Hadoop defines the new Java Application Programming Interfaces (APIâs) for
data access. Research is in progress in understanding the Hadoop system design, usage, and
security status. Protocol level modifications help to improve the security at the level.
Federated systems enable collaboration of multiple systems, networks, and organizations with
different trust levels. Authentication and authorization procedures of a client must be kept
separate from services. Traditional security is about protecting the resources within the boundary
of the organization. The federated systems require the security specification for each function.
Further, the federated systems change over time with new participants joining may not
trustworthy. Therefore, individual protection domains are necessary for each entity. Boundary
constraints are required depending upon the system. Therefore, monolithic security domain does
not work in federated systems.
Existing federated security system separates the client access, associated authentication, and
authorization procedures. The distribution of federated systems needs collaboration between
networking, organizations and associated systems. Maintaining security among these is not an
easy task since multiple entities are involved. The security threat may be unavoidable from a
variety of sources. Therefore, a high-level coarse-grained security goals need to be specified in
the requirements.
2. RECENT DEVELOPMENTS AND MAPREDUCE
Agarwal discussed the data backup in Hadoop file systems using snapshots [1]. The authors
designed an algorithm for a selective copy on appends and low memory overheads. In this work,
the snapshot tree and garbage collection was managed by Name-node. Shvachko et al. [2]
discussed the architecture of Hadoop Distributed File Systems (HDFS) and the experiences to
manage 25 petabytes of Yahoo data. The fault tolerant Google file system running on inexpensive
commodity hardware with aggregate performance to a large number of clients was discussed in
[3]. The paper discusses many aspects of design and provides report measurements from both
micro-benchmarks and real world use. The MapReduce programming model was explained, and
it is easy to use functions were discussed in [4]. The document discussed the experiences and
lessons learned in implanting the model. Further, it discusses the impact of slow machines in
redundant execution, locality of optimization and writing single copy of the immediate data to
local disc, and saving the network bandwidth. Chang et al. [5] discussed the dynamic data control
over data layout and format using Bitable as a flexible solution. They claimed that many projects
were successfully using this model by adding more machines for process over the time.
The deployment of federated security architecture was discussed in Windows Communication
Foundation (WCF) [6]. WCF provides support for building and deploying distributed systems that
employ federated security. Domain/realm, Federation, and security token service consist of the
primary security architecture of federated systems. The current mobile devices were implemented
with the imitation of 60K tasks, but the next generation of Apache MapReduce supports 100K
concurrent tasks [7]. The users in MapReduce specify the computation in terms of the map and a
reduce function. The underlying scheme in MapReduce package automatically paralyzes the
computation and schedules the parallel operations so that the task uses the network and
computational facilities to perform the operations faster. An average of hundred thousand
MapReduce jobs every day are executed on Google clusters.
4. International Journal of Security, Privacy and Trust Management (IJSPTM) Vol 4, No 3/4, November 2015
4
Halevy et al. [8] suggested that representing the data of large data sources with a nonparametric
model is needed compared to summarizing parametric model because the large data sources hold
many details. The authors felt that choosing unsupervised learning on unlabeled data is more
powerful than on labeled data. They pointed out that creating required data sets by automatically
combining data from multiple tables is an active research area. Combining data from multiple
tables with data from other sources, such as unstructured Web pages or Web search queries, is an
important research problem. Various types of security policies such as local, component, generic,
export and federate was discussed in [9]. The security policy generation enforcing also included
in this study.
Hadoop security was discussed in the reports [10 - 15]. Reddy proposed the security model for
Hadoop systems at access control and security level changes depending upon the sensitivity of the
data. Authentication and encryption are the two Security levels for Big Data in Hadoop [11]. The
author in [11] describes that Kerberos file system has better protection to keep the intruders away
from accessing the file system. Chary et al. [12] discussed the security in distributed systems and
current level of security in Hadoop systems that include the client access to Hadoop cluster using
Kerberos Protocol and authorization to access.
OâMalley et al. [13-14] discussed the security threats from different user groups in Kerberos
authentication system, the role of delegation token in Kerberos and MapReduce implementation
details. The research emphasizes the need for internal and external security for Hadoop systems.
The work describes the need for encryption, limiting access to specific users by application,
isolation between customers, and information protection.
The prototype system called âAiravatâ a MapReduce-based system that provides strong security
and privacy guarantees for distributed computations of sensitive data was presented in [15]. The
model provides a method to estimate the different parameters that should be used and tested on
several different problems. This system has weak use cases, a complicated process in specifying
the parameters and is not efficient in the general composition of MapReduce computations (the
MapR function followed by another mapper). Some organizations raised critical questions about
privacy and trust in the system.
Preserving the privacy of Big Data was discussed by McSherry [16]. McSherryâs Privacy
integrated queries (PINQ) presents an opportunity to establish a more formal and transparent
basis for privacy technology. The algorithms designed help the users in increasing the privacy-
preserving and increases the portability. Partha et al. [17] presented a system that learns for data-
integrated queries which uses sequences of associations (e.g. foreign keys, links, schema,
mappings, synonyms, and taxonomies). The associations create multiple ranked queries linking
the matches to keywords. The queries are related to Web forms, and users have only to fill the
keywords to get answers.
3. MAPREDUCE PROGRAMMING MODEL
One of the programming models in MapReduce is breaking the large problem into several smaller
problems. The solutions to the problems are then combined to give a solution to the original
problem. The process is similar to software engineering top down model. There are many
questions arise in MapReduce application due to the following reasons.
5. International Journal of Security, Privacy and Trust Management (IJSPTM) Vol 4, No 3/4, November 2015
5
⢠dynamic input
⢠nodes may fail
⢠number of smaller problems may exceed the number of nodes
⢠dependable sub-problems
⢠distribution of input to the smaller jobs
⢠coordination of nodes
⢠synchronization of the completed work
Figure 1: Map, Shuffle, and Reduce Phase
To solve these problems we can use MapReduce programming model and an associated
implementation for processing and generating large data sets. MapReduce application executes in
three steps. They are Map, Shuffle & Sort, and Reduce. A MapReduce job divides the input data-
set into independent chunks that are processed by the map tasks in a completely parallel manner.
A given input pair can have zero or more output pairs. The map outputs are merged and
constructed with respect to the key values. These pairs are propagated to reduce function. The
reduce function then merges these values to form a possibly smaller set of values. That is, the
reduce function filters the map output and generates the results with respect to the key. The total
functionality includes scheduling the tasks, monitoring them and re-executes the failed tasks.
Figure 1 shows the Map, Shuffle, and Reduce phase.
4. IMPLEMENTATION
HDFS is a distributed file system on top of existing operating system. It runs on a cluster of
commodity hardware and has the capability of handling node failures. The files are fragmented in
blocks typically of size 64/128 MB. HDFS is divided into data nodes and name nodes. Name
node is HDFS master node and data node is slave node. The name node controls the distribution
of files into blocks and responsible for balancing. Data node reads and writes HDFS blocks, direct
communication with the client and report awareness to the name node. Figure 2 shows the
relation between Hadoop client, name node, and data node.
6. International Journal of Security, Privacy and Trust Management (IJSPTM) Vol 4, No 3/4, November 2015
6
Command line interface, Java API, Web interfaces are known interfaces for HDFS. The
following instructions are used for the HDFS command line.
Create a directory
$ hadoop fs -mkdir /user/idcuser/data
Copy a file from the local filesystem to HDFS
$ hadoop fs -copyFromLocal cit-Patents.txt /user/idcuser/data/.
List all files in the HDFS file system
$ hadoop fs -ls data/*
Show the end of the specified HDFS file
$ hadoop fs -tail /user/idcuser/data/cit-patents-copy.txt
Append multiple files and move them to HDFS (via stdin/pipes)
$ cat /data/ita13-tutorial/pg*.txt | hadoop fs -put - data/all_gutenberg.txt
File/Directory Commands:
copyFromLocal, copyToLocal, cp, getmerge, ls, lsr (recursive ls), moveFromLocal,
moveToLocal, mv, rm, rmr (recursive rm), touchz, mkdir
Status/List/Show Commands:
stat, tail, cat, test (checks for existence of path, file, zero length files), du, dus
Misc Commands:
setrep, chgrp, chmod, chown, expunge (empties trashfolder)
The single node implementation was done on the Dell Precision T5500 with following
configuration.
Hardware:
CPU: Intel (R) Xeon (R) 1.60 GHz
System Memory: 12 GB
GPU Chip: Two Quadro 4000
Software:
Ubuntu 14.04 (64-bit)
CUDA Version: 6.5
CUDA C, Python, FORTRAN
The computers have duel Quadro 4000 Graphics Processing Cards (GPU). The GPU facility was
not used in the experiment. The research was conducted using Hadoop 2.6.0, JDK 7, and Python
3.4 on Dell Precision T5500 with Ubuntu 14.04 in Department of Computer Science Research
Lab at Grambling State University during spring and summer 2015. The process includes the
following steps:
⢠Two computers were used to setup the single node Hadoop cluster
⢠Install Ubuntu 14.04, create Hadoop account. The Hadoop account helps to separate the
Hadoop installation from other software applications and any other account running on the
same machine.
⢠Configure the Secure Shell (SSH), generate key for the Hadoop account and enable SSH
access to the local machine. Test the SSH setup by connecting the local machine with Hadoop
7. International Journal of Security, Privacy and Trust Management (IJSPTM) Vol 4, No 3/4, November 2015
7
account. Disable the IPv6 using text editor in /etc/sysctl.conf by modifying the code to keep
running Hadoop package. Reboot the system.
⢠Update the required Hadoop core files as directed in document so that the single node
clusters. Use JPS command to check the Hadoop single node cluster.
Figure 2: Relation between Hadoop client, Name Node, and Data Node
The code was developed to utilize the Hadoopâs node distribution capabilities to analyze the text
files. The analysis includes many times of each word repeats in the text file. Further, the program
calculates the number of times the keyword repeats. Appendix A provides the Mapper and
Reducer (Mapper.py and Reducer.py) written in Python. We provide the weight of each keyword
generated by Mapper and Reducer file. The product of keyword with weight factor provides the
importance of the keyword in the document. The sum of these products of all keywords with
weight factors helps to select the document. For each document selection, we provide the
threshold value. The programmer sets the threshold value. The threshold value determines the
selection during the document screening process using MapReduce method. If the sum of the
products with keywords exceeds the threshold, the document is required; otherwise the program
rejects the document. The search program in Appendix B helps to select the required document.
5. DISCUSSION OF RESULTS
To select a required document, we provide the keywords and their importance that varies between
0 and 1. We then take the important factor multiplied by the number of times keyword and sum
the result of all keyword importance. If the sum is above the threshold value, we conclude that the
document is required. The algorithm was coded in two stages. During the first stage, the
reputation of the words and in the second stage the importance factor and selection of the
document were coded in Python. We processed six text files to compare the results of the current
experiment. The keywords and impact factor provided are: medicine (0.02), profession (0.025),
8. International Journal of Security, Privacy and Trust Management (IJSPTM) Vol 4, No 3/4, November 2015
8
disease (0.02), surgery (0.02), mythology (0.02), and cure (0.05). The size of each file in words,
times taken in seconds to process and impact factors respectively are: (128,729; 0.1539; 5.39),
(128,805; 0.1496; 0.62), (266,017; 0.13887, 0), (277,478; 0.1692; 6.02), (330,582; 0.1725; 7.93),
and (409,113; 0.2032; 18.87). The threshold set was 10.0. Therefore, the file with impact factor
18.87 is selected as required file. If we lower the threshold to 5.0 another two files with impact
factors 6.02 and 7.93 would become our required files.
6. CONCLUSIONS AND FUTURE RESEARCH
The current research discusses the implementation of Hadoop Distributed File system and
selection of required document among the stream of documents (unstructured data). Setting up
Hadoop single node cluster on two machines and selection of required document among the
stream of text documents was completed. The process did not include any data models or SQL. It
is simple Hadoop cluster, Map Reduce algorithm, required keys and their importance factor. The
future research includes the multi-node cluster and High-performance computing using Graphics
Processing Units (GPUs) for real-time response.
ACKNOWLEDGEMENTS
The research work was supported by the AFRL Collaboration Program â Sensors Research, Air
Force Contract FA8650-13-C-5800, through subcontract number GRAM 13-S7700-02-C2.
REFERENCES
1. Sameer Agarwal., Dhruba Borthakur., and Ion Stoica., âSnapshots in Hadoop Distributed File
Systemâ, UC Berkeley Technical Report UCB/EECS, 2011.
2. Konstantin Shvachko, Hairong Kuang, Sanjay Radia, and Robert Chansler., âThe Hadoop Distributed
File Systemâ, 26th IEEE (MSST2010) Symposium on Massive Storage Systems and Technologies,
May, 2010.
3. Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung., âThe Google File Systemâ, SOSPâ03,
October 19â22, 2003.
4. effrey Dean and Sanjay Ghemawat., âMapReduce: Simplified Data Processing on Large Clustersâ,
Proc. 6th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2004, San
Francisco, USA, Dec. 2004.
5. . Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, R. E.
Gruber., âBigtable : A Distributed Storage System For Structured Dataâ, ACM Transactions on
Computer Systems (TOCS), Volume 26 Issue 2, June 2008.
6. thontication, Microsoft Patterns & Practices, http://msdn.microsoft.com/en-us/library/ff649763.aspx
2012 [accessed: April 2013].
7. J. Dean and S. Ghemawat., âMapReduce: simplified data processing on large clustersâ, CACM 50th
anniversary issue, Vol. 51, issue 1, Jan 2008, pp. 107-113.
8. A. Halevy, P. Norvig and F. Pereira., âThe Unreasonable Effectiveness of Dataâ, IEEE Intelligent
Syst., 2009, pp. 8-12.
9. B. Thuraisingham., âSecurity issues for federated database systemsâ, Computers & Security, 13
(1994), pp. 509-525.
10. Yenumula B. Reddy., âAccess Control for Sensitive Data in Hadoop Distributed File Systemsâ, Third
International Conference on Advanced Communications and Computation, INFOCOMP 2013,
November 17 - 22, 2013 - Lisbon, Portugal.
11. P. Ravi, âSecurity for Big Data in Hadoopâ, http://ravistechblog.wordpress.com/tag/Hadoop-security/,
April 15, 2013 [Retrieved - April 2013].
9. International Journal of Security, Privacy and Trust Management (IJSPTM) Vol 4, No 3/4, November 2015
9
12. N. Chary., Siddalinga K M., and Rahman., âSecurity Implementation in Hadoopâ,
http://search.iiit.ac.in/cloud/presentations/28.pdf [accessed: January 2013].
13. O. OâMalle., K. Zhang., S. Radia., R. Marti., and C. Harrell., âHadoop Security Designâ,
http://techcat.org/wp-content/uploads/2013/04/Hadoop-security-design.pdf, 2009, [accessed: March
2013].
14. D. Das., O. OâMalley., S. Radia., and K. Zhang., âAdding Security to Apache Hadoopâ, hortonworks
report, http://www.Hortonworks.com
15. I. Roy Srinath, T.V. Setty, A. Kilzer, V. Shmatikov, and E. Witchel, âAiravat: Security and Privacy
for MapReduceâ, 7th USENIX conference on Networked systems design and implementation
(NSDI'10), 2010, Berkeley, CA.
16. Frank McSherry., âPrivacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data
Analysisâ, Proceedings of SIGMOD, 2009.
17. Partha Pratim Talukdar , Marie Jacob , Muhammad Salman Mehmood , Koby Crammer , Zachary G.
Ives , Fernando Pereira , Sudipto Guha., âLearning to Create Data-Integrating Queriesâ, VLDB, 2008.
Appendix A
#!/usr/bin/python
import sys
import time
search_words = {'cure':0.05,'disease':0.02,'medicicne':0.02,
'mythology':0.02,'surgery': 0.02,'mythology': 0.02}
startTime = time.time()
counter = 0
for line in sys.stdin:
for word in line.strip().split():
for counter in range(len(search_words)):
if word == search_words.keys()[counter]:
print "%st%d" % (word, 1)
Appendix B
#!/usr/bin/python
import sys
import time
current_word = None
current_count = 1
word_names = []
word_amounts = []
x = 0
j = 0
importance = 0
search_importance = 0
totalTime = time.time()
print("n-----------Word Occurrence--------------n")
for line in sys.stdin:
word, count = line.strip().split('t')
10. International Journal of Security, Privacy and Trust Management (IJSPTM) Vol 4, No 3/4, November 2015
10
if current_word:
if word == current_word:
current_count += int(count)
else:
word_names.append(current_word)
print "%st%d" % (current_word, current_count)
word_amounts.append(current_count)
current_count = 1
current_word = word
if current_count > 0:
word_names.append(current_word)
print "%st%d" % (current_word, current_count)
word_amounts.append(current_count)
#adds last searched word
print("n-----------Word Importance---------- n")
from word_mapper import search_words
from word_mapper import startTime
startTime = time.time() - startTime
for i in range(len(word_names)):
importance = search_words[word_names[x]] * word_amounts[x]
search_importance += importance
print "%st%.2f" % (word_names[x], importance)
x = x + 1
print("n-------------Search Stats----------- ")
print "%s%.2f" % ("nSearch importance: ", search_importance)
totalTime = ((time.time() - totalTime) + startTime)
print "%s %.23f %s" % ("Search time: ", totalTime, "seconds.")
print("n-----------END--------------------------------------------")
Authors
Yenumula B. Reddy, Professor, IEEE Senior Member, IARIA Fellow and program
coordinator of Computer Science, Grambling State University. He is editorial board
member of Journal BITM Transactions on EECC, Science Academy Transactions on
Computer and Communications Networks (SATCCN), International Journal of Security,
Privacy and Trust Management (IJSPTM), MASAUM Journal of Engineering and
Applied Sciences (MJEAS), and International Journal of Engineering and Industries
(IJEI). He is Editor-in-Chief of the International Journal of Security, Privacy, and Trust Management
(IJSPTM).
Desmond Hill, is an Undergraduate student at Department of Computer Science,
Grambling State University, Grambling, LA, USA. Currently, he is junior and
majoring in Computer Science. He is working in the Research Project funded by
title Air Force Research Consortium - Sensors. The topic of his current research
is "Hadoop Distributed File Systems".