This document provides an overview of Hadoop and compares it to Spark. It discusses the key components of Hadoop including HDFS for storage, MapReduce for processing, and YARN for resource management. HDFS stores large datasets across clusters in a fault-tolerant manner. MapReduce allows parallel processing of large datasets using a map and reduce model. YARN was later added to improve resource management. The document also summarizes Spark, which can perform both batch and stream processing more efficiently than Hadoop for many workloads. A comparison of Hadoop and Spark highlights their different processing models.
Asserting that Big Data is vital to business is an understatement. Organizations have generated more and more data for years, but struggle to use it effectively. Clearly Big Data has more important uses than ensuring compliance with regulatory requirements. In addition, data is being generated with greater velocity, due to the advent of new pervasive devices (e.g., smartphones, tablets, etc.), social Web sites (e.g., Facebook, Twitter, LinkedIn, etc.) and other sources like GPS, Google Maps, heat/pressure sensors, etc.
Today’s era is generally treated as the era of data on each and every field of computing application huge amount of data is generated. The society is gradually more dependent on computers so large amount of data is generated in each and every second which is either in structured format, unstructured format or semi structured format. These huge amount of data are generally treated as big data. To analyze big data is a biggest challenge in current world. 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 and it generally follows horizontal processing. Map Reduce programming is generally run over Hadoop Framework and process the large amount of structured and unstructured data. This Paper describes about different joining strategies used in Map reduce programming to combine the data of two files in Hadoop Framework and also discusses the skewness problem associate to it.
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.
Big Data with Hadoop – For Data Management, Processing and StoringIRJET Journal
This document discusses big data and Hadoop. It begins with defining big data and explaining its characteristics of volume, variety, velocity, and veracity. It then provides an overview of Hadoop, describing its core components of HDFS for storage and MapReduce for processing. Key technologies in Hadoop's ecosystem are also summarized like Hive, Pig, and HBase. The document concludes by outlining some challenges of big data like issues of heterogeneity and incompleteness of data.
Introduction to Big Data and Hadoop using Local Standalone Modeinventionjournals
Big Data is a term defined for data sets that are extreme and complex where traditional data processing applications are inadequate to deal with them. The term Big Data often refers simply to the use of predictive investigation on analytic methods that extract value from data. Big data is generalized as a large data which is a collection of big datasets that cannot be processed using traditional computing techniques. Big data is not purely a data, rather than it is a complete subject involves various tools, techniques and frameworks. Big data can be any structured collection which results incapability of conventional data management methods. Hadoop is a distributed example used to change the large amount of data. This manipulation contains not only storage as well as processing on the data. Hadoop is an open- source software framework for dispersed storage and processing of big data sets on computer clusters built from commodity hardware. HDFS was built to support high throughput, streaming reads and writes of extremely large files. Hadoop Map Reduce is a software framework for easily writing applications which process vast amounts of data. Wordcount example reads text files and counts how often words occur. The input is text files and the result is wordcount file, each line of which contains a word and the count of how often it occurred separated by a tab.
Big Data Analysis and Its Scheduling Policy – HadoopIOSR Journals
This document discusses scheduling policies in Hadoop for big data analysis. It describes the default FIFO scheduler in Hadoop as well as alternative schedulers like the Fair Scheduler and Capacity Scheduler. The Fair Scheduler was developed by Facebook to allocate resources fairly between jobs by assigning them to pools with minimum guaranteed capacities. The Capacity Scheduler allows multiple tenants to securely share a large cluster while giving each organization capacity guarantees. It also supports hierarchical queues to prioritize sharing unused resources within an organization.
Large amount of data are produced daily from various fields such as science, economics,
engineering and health. The main challenge of pervasive computing is to store and analyze large amount of
data.This has led to the need for usable and scalable data applications and storage clusters. In this article, we
examine the hadoop architecture developed to deal with these problems. The Hadoop architecture consists of
the Hadoop Distributed File System (HDFS) and Mapreduce programming model, which enables storage and
computation on a set of commodity computers. In this study, a Hadoop cluster consisting of four nodes was
created.Regarding the data size and cluster size, Pi and Grep MapReduce applications, which show the effect of
different data sizes and number of nodes in the cluster, have been made and their results examined.
Financial Data Infrastructure with HDF5
HDF5 is a file format and library that can address the growing need for data storage and management in financial institutions. It allows for [1] limitless data storage through efficient compression and configurable storage granularity, as well as [2] parallel data access and delivery. HDF5 is also infrastructure agnostic and can scale across heterogeneous environments. When used for financial market data and high frequency trading applications, HDF5 has the potential to store massive datasets needed for predictive analytics and algorithmic trading strategies.
Asserting that Big Data is vital to business is an understatement. Organizations have generated more and more data for years, but struggle to use it effectively. Clearly Big Data has more important uses than ensuring compliance with regulatory requirements. In addition, data is being generated with greater velocity, due to the advent of new pervasive devices (e.g., smartphones, tablets, etc.), social Web sites (e.g., Facebook, Twitter, LinkedIn, etc.) and other sources like GPS, Google Maps, heat/pressure sensors, etc.
Today’s era is generally treated as the era of data on each and every field of computing application huge amount of data is generated. The society is gradually more dependent on computers so large amount of data is generated in each and every second which is either in structured format, unstructured format or semi structured format. These huge amount of data are generally treated as big data. To analyze big data is a biggest challenge in current world. 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 and it generally follows horizontal processing. Map Reduce programming is generally run over Hadoop Framework and process the large amount of structured and unstructured data. This Paper describes about different joining strategies used in Map reduce programming to combine the data of two files in Hadoop Framework and also discusses the skewness problem associate to it.
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.
Big Data with Hadoop – For Data Management, Processing and StoringIRJET Journal
This document discusses big data and Hadoop. It begins with defining big data and explaining its characteristics of volume, variety, velocity, and veracity. It then provides an overview of Hadoop, describing its core components of HDFS for storage and MapReduce for processing. Key technologies in Hadoop's ecosystem are also summarized like Hive, Pig, and HBase. The document concludes by outlining some challenges of big data like issues of heterogeneity and incompleteness of data.
Introduction to Big Data and Hadoop using Local Standalone Modeinventionjournals
Big Data is a term defined for data sets that are extreme and complex where traditional data processing applications are inadequate to deal with them. The term Big Data often refers simply to the use of predictive investigation on analytic methods that extract value from data. Big data is generalized as a large data which is a collection of big datasets that cannot be processed using traditional computing techniques. Big data is not purely a data, rather than it is a complete subject involves various tools, techniques and frameworks. Big data can be any structured collection which results incapability of conventional data management methods. Hadoop is a distributed example used to change the large amount of data. This manipulation contains not only storage as well as processing on the data. Hadoop is an open- source software framework for dispersed storage and processing of big data sets on computer clusters built from commodity hardware. HDFS was built to support high throughput, streaming reads and writes of extremely large files. Hadoop Map Reduce is a software framework for easily writing applications which process vast amounts of data. Wordcount example reads text files and counts how often words occur. The input is text files and the result is wordcount file, each line of which contains a word and the count of how often it occurred separated by a tab.
Big Data Analysis and Its Scheduling Policy – HadoopIOSR Journals
This document discusses scheduling policies in Hadoop for big data analysis. It describes the default FIFO scheduler in Hadoop as well as alternative schedulers like the Fair Scheduler and Capacity Scheduler. The Fair Scheduler was developed by Facebook to allocate resources fairly between jobs by assigning them to pools with minimum guaranteed capacities. The Capacity Scheduler allows multiple tenants to securely share a large cluster while giving each organization capacity guarantees. It also supports hierarchical queues to prioritize sharing unused resources within an organization.
Large amount of data are produced daily from various fields such as science, economics,
engineering and health. The main challenge of pervasive computing is to store and analyze large amount of
data.This has led to the need for usable and scalable data applications and storage clusters. In this article, we
examine the hadoop architecture developed to deal with these problems. The Hadoop architecture consists of
the Hadoop Distributed File System (HDFS) and Mapreduce programming model, which enables storage and
computation on a set of commodity computers. In this study, a Hadoop cluster consisting of four nodes was
created.Regarding the data size and cluster size, Pi and Grep MapReduce applications, which show the effect of
different data sizes and number of nodes in the cluster, have been made and their results examined.
Financial Data Infrastructure with HDF5
HDF5 is a file format and library that can address the growing need for data storage and management in financial institutions. It allows for [1] limitless data storage through efficient compression and configurable storage granularity, as well as [2] parallel data access and delivery. HDF5 is also infrastructure agnostic and can scale across heterogeneous environments. When used for financial market data and high frequency trading applications, HDF5 has the potential to store massive datasets needed for predictive analytics and algorithmic trading strategies.
A Big-Data Process Consigned Geographically by Employing Mapreduce Frame WorkIRJET Journal
This document discusses frameworks for processing big data that is distributed across geographic locations. It begins by introducing the challenges of geo-distributed big data processing and then describes several MapReduce-based frameworks like G-Hadoop and G-MR that can process pre-located geo-distributed data. It also covers Spark-based systems like Iridium and frameworks that partition data across geographic locations, such as KOALA grid-based systems. The document analyzes key aspects of geo-distributed big data processing systems like data distribution, task scheduling, and fault tolerance.
Survey of Parallel Data Processing in Context with MapReduce cscpconf
MapReduce is a parallel programming model and an associated implementation introduced by
Google. In the programming model, a user specifies the computation by two functions, Map and Reduce. The underlying MapReduce library automatically parallelizes the computation, and handles complicated issues like data distribution, load balancing and fault tolerance. The original MapReduce implementation by Google, as well as its open-source counterpart,Hadoop, is aimed for parallelizing computing in large clusters of commodity machines.This paper gives an overview of MapReduce programming model and its applications. The author has described here the workflow of MapReduce process. Some important issues, like fault tolerance, are studied in more detail. Even the illustration of working of Map Reduce is given. The data locality issue in heterogeneous environments can noticeably reduce the Map Reduce performance. In this paper, the author has addressed the illustration of data across nodes in a way that each node has a balanced data processing load stored in a parallel manner. Given a data intensive application running on a Hadoop Map Reduce cluster, the auhor has exemplified how data placement is done in Hadoop architecture and the role of Map Reduce in the Hadoop Architecture. The amount of data stored in each node to achieve improved data-processing performance is explained here.
The document provides an introduction to the HDF5 format and The HDF Group software. It discusses:
- HDF5 is a data model, library, and file format for managing large amounts of numerical data. It was developed starting in 1996 as an improved successor to the HDF4 format.
- The HDF5 data model defines objects like datasets, groups, and attributes that provide a flexible way to organize and describe stored data. Properties of these objects can be modified to control storage and performance.
- The HDF5 library provides interfaces to work with HDF5 files and objects from C, C++, Fortran, Java and other languages. A set of command line utilities and the HDFView browser can be used to
Big data refers to large amounts of data from various sources that is analyzed to solve problems. It is characterized by volume, velocity, and variety. Hadoop is an open source framework used to store and process big data across clusters of computers. Key components of Hadoop include HDFS for storage, MapReduce for processing, and HIVE for querying. Other tools like Pig and HBase provide additional functionality. Together these tools provide a scalable infrastructure to handle the volume, speed, and complexity of big data.
One of the challenges in storing and processing the data and using the latest internet technologies has resulted in large volumes of data. The technique to manage this massive amount of data and to pull out the value, out of this volume is collectively called Big data. Over the recent years, there has been a rising interest in big data for social media analysis. Online social media have become the important platform across the world to share information. Facebook, one of the largest social media site receives posts in millions every day. One of the efficient technologies that deal with the Big Data is Hadoop. Hadoop, for processing large data volume jobs uses MapReduce programming model. This paper provides a survey on Hadoop and its role in facebook and a brief introduction to HIVE.
Web Oriented FIM for large scale dataset using Hadoopdbpublications
In large scale datasets, mining frequent itemsets using existing parallel mining algorithm is to balance the load by distributing such enormous data between collections of computers. But we identify high performance issue in existing mining algorithms [1]. To handle this problem, we introduce a new approach called data partitioning using Map Reduce programming model.In our proposed system, we have introduced new technique called frequent itemset ultrametric tree rather than conservative FP-trees. An investigational outcome tells us that, eradicating redundant transaction results in improving the performance by reducing computing loads.
There is a growing trend of applications that ought to handle huge information. However, analysing huge information may be a terribly difficult drawback nowadays. For such data many techniques can be considered. The technologies like Grid Computing, Volunteering Computing, and RDBMS can be considered as potential techniques to handle such data. We have a still in growing phase Hadoop Tool to handle such data also. We will do a survey on all this techniques to find a potential technique to manage and work with Big Data.
Mankind has stored more than 295 billion gigabytes (or 295 Exabyte) of data since 1986, as per a report by the University of Southern California. Storing and monitoring this data in widely distributed environments for 24/7 is a huge task for global service organizations. These datasets require high processing power which can’t be offered by traditional databases as they are stored in an unstructured format. Although one can use Map Reduce paradigm to solve this problem using java based Hadoop, it cannot provide us with maximum functionality. Drawbacks can be overcome using Hadoop-streaming techniques that allow users to define non-java executable for processing this datasets. This paper proposes a THESAURUS model which allows a faster and easier version of business analysis.
Survey on Performance of Hadoop Map reduce Optimization Methodspaperpublications3
Abstract: Hadoop is a open source software framework for storage and processing large scale of datasets on clusters of commodity hardware. Hadoop provides a reliable shared storage and analysis system, here storage provided by HDFS and analysis provided by MapReduce. MapReduce frameworks are foraying into the domain of high performance of computing with stringent non-functional requirements namely execution times and throughputs. MapReduce provides simple programming interfaces with two functions: map and reduce. The functions can be automatically executed in parallel on a cluster without requiring any intervention from the programmer. Moreover, MapReduce offers other benefits, including load balancing, high scalability, and fault tolerance. The challenge is that when we consider the data is dynamically and continuously produced, from different geographical locations. For dynamically generated data, an efficient algorithm is desired, for timely guiding the transfer of data into the cloud over time for geo-dispersed data sets, there is need to select the best data center to aggregate all data onto given that a MapReduce like framework is most efficient when data to be processed are all in one place, and not across data centers due to the enormous overhead of inter-data center data moving in the stage of shuffle and reduce. Recently, many researchers tend to implement and deploy data-intensive and/or computation-intensive algorithms on MapReduce parallel computing framework for high processing efficiency.
The document is a seminar report on the Hadoop framework. It provides an introduction to Hadoop and describes its key technologies including MapReduce, HDFS, and programming model. MapReduce allows distributed processing of large datasets across clusters. HDFS is the distributed file system used by Hadoop to reliably store large amounts of data across commodity hardware.
IRJET - A Prognosis Approach for Stock Market Prediction based on Term Streak...IRJET Journal
This document discusses using machine learning algorithms and Hadoop to predict stock market performance based on historical stock data. It proposes a model that would collect stock data from various sources, preprocess the data to clean it, cluster the data using MapReduce, and then use a support vector machine algorithm to analyze the clustered data and generate stock predictions. The model is designed to take advantage of Hadoop's ability to process large datasets in parallel across multiple servers or clusters. The goal is to more accurately predict stock prices and identify market trends based on analyzing huge amounts of historical stock market data.
STUDY ON EMERGING APPLICATIONS ON DATA PLANE AND OPTIMIZATION POSSIBILITIESijdpsjournal
By programming both the data plane and the control plane, network operators can adapt their networks to
their needs. Thanks to research over the past decade, this concept has more formulized and more
technologically feasible. However, since control plane programmability came first, it has already been
successfully implemented in the real network and is beginning to pay off. Today, the data plane
programmability is evolving very rapidly to reach this level, attracting the attention of researchers and
developers: Designing data plane languages, application development on it, formulizing software switches
and architecture that can run data plane codes and the applications, increasing performance of software
switch, and so on. As the control plane and data plane become more open, many new innovations and
technologies are emerging, but some experts warn that consumers may be confused as to which of the many
technologies to choose. This is a testament to how much innovation is emerging in the network. This paper
outlines some emerging applications on the data plane and offers opportunities for further improvement
and optimization. Our observations show that most of the implementations are done in a test environment
and have not been tested well enough in terms of performance, but there are many interesting works, for
example, previous control plane solutions are being implemented in the data plane.
This document discusses a proposed data-aware caching framework called Dache that could be used with big data applications built on MapReduce. Dache aims to cache intermediate data generated during MapReduce jobs to avoid duplicate computations. When tasks run, they would first check the cache for existing results before running the actual computations. The goal is to improve efficiency by reducing redundant work. The document outlines the objectives and scope of extending MapReduce with Dache, provides background on MapReduce and Hadoop, and concludes that initial experiments show Dache can eliminate duplicate tasks in incremental jobs.
PERFORMANCE EVALUATION OF BIG DATA PROCESSING OF CLOAK-REDUCEijdpsjournal
Big Data has introduced the challenge of storing and processing large volumes of data (text, images, and
videos). The success of centralised exploitation of massive data on a node is outdated, leading to the
emergence of distributed storage, parallel processing and hybrid distributed storage and parallel
processing frameworks.
The main objective of this paper is to evaluate the load balancing and task allocation strategy of our
hybrid distributed storage and parallel processing framework CLOAK-Reduce. To achieve this goal, we
first performed a theoretical approach of the architecture and operation of some DHT-MapReduce. Then,
we compared the data collected from their load balancing and task allocation strategy by simulation.
Finally, the simulation results show that CLOAK-Reduce C5R5 replication provides better load balancing
efficiency, MapReduce job submission with 10% churn or no churn.
IRJET - Survey Paper on Map Reduce Processing using HADOOPIRJET Journal
This document summarizes a survey paper on MapReduce processing using Hadoop. It discusses how big data is growing rapidly due to factors like the internet and social media. Traditional databases cannot handle big data. Hadoop uses MapReduce and HDFS to store and process extremely large datasets across commodity servers in a distributed manner. HDFS stores data in a distributed file system, while MapReduce allows parallel processing of that data. The paper describes the MapReduce process and its core functions like map, shuffle, reduce. It explains how Hadoop provides advantages like scalability, cost effectiveness, flexibility and parallel processing for big data.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This document describes Dremel, an interactive query system for analyzing large nested datasets. Dremel uses a multi-level execution tree to parallelize queries across thousands of CPUs. It stores nested data in a novel columnar format that improves performance by only reading relevant columns from storage. Dremel has been in production at Google since 2006 and is used by thousands of users to interactively analyze datasets containing trillions of records.
Big Data Processing with Hadoop : A ReviewIRJET Journal
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 defines big data as large datasets that are difficult to process using traditional methods due to their volume, variety, and velocity. Hadoop is presented as an open-source software framework for distributed storage and processing of large datasets across clusters of commodity servers. The key components of Hadoop are the Hadoop Distributed File System (HDFS) for storage and MapReduce as a programming model for distributed processing. A number of other technologies in Hadoop's ecosystem are also described such as HBase, Avro, Pig, Hive, Sqoop, Zookeeper and Mahout. The document concludes that Hadoop provides solutions for efficiently processing and analyzing big data.
IRJET- Generate Distributed Metadata using Blockchain Technology within HDFS ...IRJET Journal
This document proposes a new HDFS architecture that eliminates the single point of failure of the NameNode by distributing metadata storage using blockchain technology. In the traditional HDFS, the NameNode stores all metadata, but in the new architecture this is replaced by blockchain miners that securely store encrypted metadata across data nodes. Blockchain links data blocks in a serial manner with cryptographic hashes to ensure integrity. The key components are HDFS clients, data nodes for storage, and specially designated miner nodes that help create and store metadata blocks in an encrypted and distributed fashion similar to how transactions are recorded in a blockchain. This architecture aims to provide reliable, secure and faster metadata access without a single point of failure.
This document discusses scheduling policies in Hadoop for big data analysis. It describes the default FIFO scheduler in Hadoop as well as alternative schedulers like the Fair Scheduler and Capacity Scheduler. The Fair Scheduler was developed by Facebook to allocate resources fairly between jobs by assigning them to pools with minimum guaranteed capacities. The Capacity Scheduler allows multiple tenants to securely share a large cluster while giving each organization capacity guarantees. It also supports hierarchical queues to prioritize sharing unused resources within an organization.
Big data refers to large amounts of data from various sources that is analyzed to solve problems. It is characterized by volume, velocity, and variety. Hadoop is an open source framework used to store and process big data across clusters of computers. Key components of Hadoop include HDFS for storage, MapReduce for processing, and HIVE for querying. Other tools like Pig and HBase provide additional functionality. Together these tools provide a scalable infrastructure to handle the volume, speed, and complexity of big data.
A Big-Data Process Consigned Geographically by Employing Mapreduce Frame WorkIRJET Journal
This document discusses frameworks for processing big data that is distributed across geographic locations. It begins by introducing the challenges of geo-distributed big data processing and then describes several MapReduce-based frameworks like G-Hadoop and G-MR that can process pre-located geo-distributed data. It also covers Spark-based systems like Iridium and frameworks that partition data across geographic locations, such as KOALA grid-based systems. The document analyzes key aspects of geo-distributed big data processing systems like data distribution, task scheduling, and fault tolerance.
Survey of Parallel Data Processing in Context with MapReduce cscpconf
MapReduce is a parallel programming model and an associated implementation introduced by
Google. In the programming model, a user specifies the computation by two functions, Map and Reduce. The underlying MapReduce library automatically parallelizes the computation, and handles complicated issues like data distribution, load balancing and fault tolerance. The original MapReduce implementation by Google, as well as its open-source counterpart,Hadoop, is aimed for parallelizing computing in large clusters of commodity machines.This paper gives an overview of MapReduce programming model and its applications. The author has described here the workflow of MapReduce process. Some important issues, like fault tolerance, are studied in more detail. Even the illustration of working of Map Reduce is given. The data locality issue in heterogeneous environments can noticeably reduce the Map Reduce performance. In this paper, the author has addressed the illustration of data across nodes in a way that each node has a balanced data processing load stored in a parallel manner. Given a data intensive application running on a Hadoop Map Reduce cluster, the auhor has exemplified how data placement is done in Hadoop architecture and the role of Map Reduce in the Hadoop Architecture. The amount of data stored in each node to achieve improved data-processing performance is explained here.
The document provides an introduction to the HDF5 format and The HDF Group software. It discusses:
- HDF5 is a data model, library, and file format for managing large amounts of numerical data. It was developed starting in 1996 as an improved successor to the HDF4 format.
- The HDF5 data model defines objects like datasets, groups, and attributes that provide a flexible way to organize and describe stored data. Properties of these objects can be modified to control storage and performance.
- The HDF5 library provides interfaces to work with HDF5 files and objects from C, C++, Fortran, Java and other languages. A set of command line utilities and the HDFView browser can be used to
Big data refers to large amounts of data from various sources that is analyzed to solve problems. It is characterized by volume, velocity, and variety. Hadoop is an open source framework used to store and process big data across clusters of computers. Key components of Hadoop include HDFS for storage, MapReduce for processing, and HIVE for querying. Other tools like Pig and HBase provide additional functionality. Together these tools provide a scalable infrastructure to handle the volume, speed, and complexity of big data.
One of the challenges in storing and processing the data and using the latest internet technologies has resulted in large volumes of data. The technique to manage this massive amount of data and to pull out the value, out of this volume is collectively called Big data. Over the recent years, there has been a rising interest in big data for social media analysis. Online social media have become the important platform across the world to share information. Facebook, one of the largest social media site receives posts in millions every day. One of the efficient technologies that deal with the Big Data is Hadoop. Hadoop, for processing large data volume jobs uses MapReduce programming model. This paper provides a survey on Hadoop and its role in facebook and a brief introduction to HIVE.
Web Oriented FIM for large scale dataset using Hadoopdbpublications
In large scale datasets, mining frequent itemsets using existing parallel mining algorithm is to balance the load by distributing such enormous data between collections of computers. But we identify high performance issue in existing mining algorithms [1]. To handle this problem, we introduce a new approach called data partitioning using Map Reduce programming model.In our proposed system, we have introduced new technique called frequent itemset ultrametric tree rather than conservative FP-trees. An investigational outcome tells us that, eradicating redundant transaction results in improving the performance by reducing computing loads.
There is a growing trend of applications that ought to handle huge information. However, analysing huge information may be a terribly difficult drawback nowadays. For such data many techniques can be considered. The technologies like Grid Computing, Volunteering Computing, and RDBMS can be considered as potential techniques to handle such data. We have a still in growing phase Hadoop Tool to handle such data also. We will do a survey on all this techniques to find a potential technique to manage and work with Big Data.
Mankind has stored more than 295 billion gigabytes (or 295 Exabyte) of data since 1986, as per a report by the University of Southern California. Storing and monitoring this data in widely distributed environments for 24/7 is a huge task for global service organizations. These datasets require high processing power which can’t be offered by traditional databases as they are stored in an unstructured format. Although one can use Map Reduce paradigm to solve this problem using java based Hadoop, it cannot provide us with maximum functionality. Drawbacks can be overcome using Hadoop-streaming techniques that allow users to define non-java executable for processing this datasets. This paper proposes a THESAURUS model which allows a faster and easier version of business analysis.
Survey on Performance of Hadoop Map reduce Optimization Methodspaperpublications3
Abstract: Hadoop is a open source software framework for storage and processing large scale of datasets on clusters of commodity hardware. Hadoop provides a reliable shared storage and analysis system, here storage provided by HDFS and analysis provided by MapReduce. MapReduce frameworks are foraying into the domain of high performance of computing with stringent non-functional requirements namely execution times and throughputs. MapReduce provides simple programming interfaces with two functions: map and reduce. The functions can be automatically executed in parallel on a cluster without requiring any intervention from the programmer. Moreover, MapReduce offers other benefits, including load balancing, high scalability, and fault tolerance. The challenge is that when we consider the data is dynamically and continuously produced, from different geographical locations. For dynamically generated data, an efficient algorithm is desired, for timely guiding the transfer of data into the cloud over time for geo-dispersed data sets, there is need to select the best data center to aggregate all data onto given that a MapReduce like framework is most efficient when data to be processed are all in one place, and not across data centers due to the enormous overhead of inter-data center data moving in the stage of shuffle and reduce. Recently, many researchers tend to implement and deploy data-intensive and/or computation-intensive algorithms on MapReduce parallel computing framework for high processing efficiency.
The document is a seminar report on the Hadoop framework. It provides an introduction to Hadoop and describes its key technologies including MapReduce, HDFS, and programming model. MapReduce allows distributed processing of large datasets across clusters. HDFS is the distributed file system used by Hadoop to reliably store large amounts of data across commodity hardware.
IRJET - A Prognosis Approach for Stock Market Prediction based on Term Streak...IRJET Journal
This document discusses using machine learning algorithms and Hadoop to predict stock market performance based on historical stock data. It proposes a model that would collect stock data from various sources, preprocess the data to clean it, cluster the data using MapReduce, and then use a support vector machine algorithm to analyze the clustered data and generate stock predictions. The model is designed to take advantage of Hadoop's ability to process large datasets in parallel across multiple servers or clusters. The goal is to more accurately predict stock prices and identify market trends based on analyzing huge amounts of historical stock market data.
STUDY ON EMERGING APPLICATIONS ON DATA PLANE AND OPTIMIZATION POSSIBILITIESijdpsjournal
By programming both the data plane and the control plane, network operators can adapt their networks to
their needs. Thanks to research over the past decade, this concept has more formulized and more
technologically feasible. However, since control plane programmability came first, it has already been
successfully implemented in the real network and is beginning to pay off. Today, the data plane
programmability is evolving very rapidly to reach this level, attracting the attention of researchers and
developers: Designing data plane languages, application development on it, formulizing software switches
and architecture that can run data plane codes and the applications, increasing performance of software
switch, and so on. As the control plane and data plane become more open, many new innovations and
technologies are emerging, but some experts warn that consumers may be confused as to which of the many
technologies to choose. This is a testament to how much innovation is emerging in the network. This paper
outlines some emerging applications on the data plane and offers opportunities for further improvement
and optimization. Our observations show that most of the implementations are done in a test environment
and have not been tested well enough in terms of performance, but there are many interesting works, for
example, previous control plane solutions are being implemented in the data plane.
This document discusses a proposed data-aware caching framework called Dache that could be used with big data applications built on MapReduce. Dache aims to cache intermediate data generated during MapReduce jobs to avoid duplicate computations. When tasks run, they would first check the cache for existing results before running the actual computations. The goal is to improve efficiency by reducing redundant work. The document outlines the objectives and scope of extending MapReduce with Dache, provides background on MapReduce and Hadoop, and concludes that initial experiments show Dache can eliminate duplicate tasks in incremental jobs.
PERFORMANCE EVALUATION OF BIG DATA PROCESSING OF CLOAK-REDUCEijdpsjournal
Big Data has introduced the challenge of storing and processing large volumes of data (text, images, and
videos). The success of centralised exploitation of massive data on a node is outdated, leading to the
emergence of distributed storage, parallel processing and hybrid distributed storage and parallel
processing frameworks.
The main objective of this paper is to evaluate the load balancing and task allocation strategy of our
hybrid distributed storage and parallel processing framework CLOAK-Reduce. To achieve this goal, we
first performed a theoretical approach of the architecture and operation of some DHT-MapReduce. Then,
we compared the data collected from their load balancing and task allocation strategy by simulation.
Finally, the simulation results show that CLOAK-Reduce C5R5 replication provides better load balancing
efficiency, MapReduce job submission with 10% churn or no churn.
IRJET - Survey Paper on Map Reduce Processing using HADOOPIRJET Journal
This document summarizes a survey paper on MapReduce processing using Hadoop. It discusses how big data is growing rapidly due to factors like the internet and social media. Traditional databases cannot handle big data. Hadoop uses MapReduce and HDFS to store and process extremely large datasets across commodity servers in a distributed manner. HDFS stores data in a distributed file system, while MapReduce allows parallel processing of that data. The paper describes the MapReduce process and its core functions like map, shuffle, reduce. It explains how Hadoop provides advantages like scalability, cost effectiveness, flexibility and parallel processing for big data.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This document describes Dremel, an interactive query system for analyzing large nested datasets. Dremel uses a multi-level execution tree to parallelize queries across thousands of CPUs. It stores nested data in a novel columnar format that improves performance by only reading relevant columns from storage. Dremel has been in production at Google since 2006 and is used by thousands of users to interactively analyze datasets containing trillions of records.
Big Data Processing with Hadoop : A ReviewIRJET Journal
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 defines big data as large datasets that are difficult to process using traditional methods due to their volume, variety, and velocity. Hadoop is presented as an open-source software framework for distributed storage and processing of large datasets across clusters of commodity servers. The key components of Hadoop are the Hadoop Distributed File System (HDFS) for storage and MapReduce as a programming model for distributed processing. A number of other technologies in Hadoop's ecosystem are also described such as HBase, Avro, Pig, Hive, Sqoop, Zookeeper and Mahout. The document concludes that Hadoop provides solutions for efficiently processing and analyzing big data.
IRJET- Generate Distributed Metadata using Blockchain Technology within HDFS ...IRJET Journal
This document proposes a new HDFS architecture that eliminates the single point of failure of the NameNode by distributing metadata storage using blockchain technology. In the traditional HDFS, the NameNode stores all metadata, but in the new architecture this is replaced by blockchain miners that securely store encrypted metadata across data nodes. Blockchain links data blocks in a serial manner with cryptographic hashes to ensure integrity. The key components are HDFS clients, data nodes for storage, and specially designated miner nodes that help create and store metadata blocks in an encrypted and distributed fashion similar to how transactions are recorded in a blockchain. This architecture aims to provide reliable, secure and faster metadata access without a single point of failure.
This document discusses scheduling policies in Hadoop for big data analysis. It describes the default FIFO scheduler in Hadoop as well as alternative schedulers like the Fair Scheduler and Capacity Scheduler. The Fair Scheduler was developed by Facebook to allocate resources fairly between jobs by assigning them to pools with minimum guaranteed capacities. The Capacity Scheduler allows multiple tenants to securely share a large cluster while giving each organization capacity guarantees. It also supports hierarchical queues to prioritize sharing unused resources within an organization.
Big data refers to large amounts of data from various sources that is analyzed to solve problems. It is characterized by volume, velocity, and variety. Hadoop is an open source framework used to store and process big data across clusters of computers. Key components of Hadoop include HDFS for storage, MapReduce for processing, and HIVE for querying. Other tools like Pig and HBase provide additional functionality. Together these tools provide a scalable infrastructure to handle the volume, speed, and complexity of big data.
AN OVERVIEW OF BIGDATA AND HADOOP . THE ARCHITECHTURE IT USES AND THE WAY IT WORKS ON THE DATA SETS. THE SIDES ALSO SHOW THE VARIOUS FIELDS WHERE THEY ARE MOSTLY USED AND IMPLIMENTED
Performance Improvement of Heterogeneous Hadoop Cluster using Ranking AlgorithmIRJET Journal
This document proposes using a ranking algorithm and sampling algorithm to improve the performance of a heterogeneous Hadoop cluster. The ranking algorithm prioritizes data distribution based on node frequency, so that higher frequency nodes are processed first. The sampling algorithm randomly selects nodes for data distribution instead of evenly distributing across all nodes. The proposed approach reduces computation time and improves overall cluster performance compared to the existing approach of evenly distributing data across nodes of varying sizes. Results show the proposed approach reduces execution time for various file sizes compared to the existing approach.
IRJET- Performing Load Balancing between Namenodes in HDFSIRJET Journal
This document proposes a new architecture for HDFS to address the single point of failure issue of the NameNode. The current HDFS architecture uses a single NameNode that manages file system metadata. If it fails, the entire system fails. The proposed architecture uses multiple interconnected NameNodes that maintain mirrors of each other's metadata using the Chord system. This allows load balancing between NameNodes and prevents failure if one NameNode goes down, as other NameNodes can handle the load and client requests/responses. The goal is to improve scalability, availability and reduce downtime of the NameNode in HDFS through this new distributed architecture.
IRJET- A Study of Comparatively Analysis for HDFS and Google File System ...IRJET Journal
This document compares and contrasts the Hadoop Distributed File System (HDFS) and the Google File System (GFS), which are both frameworks for handling large-scale, distributed data storage and processing. HDFS is an open-source system implemented by Apache and used by companies like Yahoo, Facebook, and IBM. GFS was originally developed by Google as a proprietary system. Both systems use a master-slave architecture with a centralized metadata manager and distributed data nodes, but HDFS uses a NameNode and DataNodes while GFS uses a MasterNode and ChunkServers. The document outlines several key similarities and differences between the two systems in their objectives, implementations, hardware usage, file management, operations, and other technical aspects.
Performance Enhancement using Appropriate File Formats in Big Data Hadoop Eco...IRJET Journal
This document compares the performance of different file formats (Avro, Parquet, ORC, textfile) for storing large datasets in Hadoop. It conducts experiments loading datasets into tables using each format and measuring file size compression and query execution times. The results show that Parquet provides the most compact storage size, compressing data to about 1/3 the size of the original textfile. ORC and Parquet have similar performance for query execution, with ORC having a slightly smaller file size. In general, the binary formats (Avro, Parquet, ORC) outperform textfile for both storage efficiency and query performance.
Harnessing Hadoop: Understanding the Big Data Processing Options for Optimizi...Cognizant
This document discusses big data processing options for optimizing analytical workloads using Hadoop. It provides an overview of Hadoop and its core components HDFS and MapReduce. It also discusses the Hadoop ecosystem including tools like Pig, Hive, HBase, and ecosystem projects. The document compares building Hadoop clusters to using appliances or Hadoop-as-a-Service offerings. It also briefly mentions some Hadoop competitors for real-time processing use cases.
We are in the age of big data which involves collection of large datasets.Managing and processing large data sets is difficult with existing traditional database systems.Hadoop and Map Reduce has become one of the most powerful and popular tools for big data processing . Hadoop Map Reduce a powerful programming model is used for analyzing large set of data with parallelization, fault tolerance and load balancing and other features are it is elastic,scalable,efficient.MapReduce with cloud is combined to form a framework for storage, processing and analysis of massive machine maintenance data in a cloud computing environment.
The document discusses analyzing temperature data using Hadoop MapReduce. It describes importing a weather dataset from the National Climatic Data Center into Eclipse to create a MapReduce program. The program will classify days in the Austin, Texas data from 2015 as either hot or cold based on the recorded temperature. The steps outlined are: importing the project, exporting it as a JAR file, checking that the Hadoop cluster is running, uploading the input file to HDFS, and running the JAR file with the input and output paths specified. The goal is to analyze temperature variation and find the hottest/coldest days of the month/year from the large climate dataset.
An Analytical Study on Research Challenges and Issues in Big Data Analysis.pdfApril Knyff
This document discusses research challenges and issues in big data analysis. It begins with an introduction to big data and its key characteristics of volume, velocity, variety, and veracity. It then discusses challenges related to big data storage, privacy and security of data, and data processing. Specifically, it explores issues around data accessibility, application domains, privacy and security, and analytics such as heterogeneity, incompleteness, and real-time analysis of streaming data.
This document provides an introduction to Hadoop, including its programming model, distributed file system (HDFS), and MapReduce framework. It discusses how Hadoop uses a distributed storage model across clusters, racks, and data nodes to store large datasets in a fault-tolerant manner. It also summarizes the core components of Hadoop, including HDFS for storage, MapReduce for processing, and YARN for resource management. Hadoop provides a scalable platform for distributed processing and analysis of big data.
Infrastructure Considerations for Analytical WorkloadsCognizant
Using Apache Hadoop clusters and Mahout for analyzing big data workloads yields extraordinary performance; we offer a detailed comparison of running Hadoop in a physical vs. virtual infrastructure environment.
Applications of machine learning are widely used in the real world with either supervised or unsupervised
learning process. Recently emerged domain in the information technologies is Big Data which refers to
data with characteristics such as volume, velocity and variety. The existing machine learning approaches
cannot cope with Big Data. The processing of big data has to be done in an environment where distributed
programming is supported. In such environment like Hadoop, a distributed file system like Hadoop
Distributed File System (HDFS) is required to support scalable and efficient access to data. Distributed
environments are often associated with cloud computing and data centres. Naturally such environments are
equipped with GPUs (Graphical Processing Units) that support parallel processing. Thus the environment
is suitable for processing huge amount of data in short span of time. In this paper we propose a framework
that can have generic operations that support processing of big data. Our framework provides building
blocks to support clustering of unstructured data which is in the form of documents. We proposed an
algorithm that works in scheduling jobs of multiple users. We built a prototype application to demonstrate
the proof of concept. The empirical results revealed that the proposed framework shows 95% accuracy
when the results are compared with the ground truth.
The document provides an overview of Hadoop and its core components. It discusses:
- Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers.
- The two core components of Hadoop are HDFS for distributed storage, and MapReduce for distributed processing. HDFS stores data reliably across machines, while MapReduce processes large amounts of data in parallel.
- Hadoop can operate in three modes - standalone, pseudo-distributed and fully distributed. The document focuses on setting up Hadoop in standalone mode for development and testing purposes on a single machine.
Presentation regarding big data. The presentation also contains basics regarding Hadoop and Hadoop components along with their architecture. Contents of the PPT are
1. Understanding Big Data
2. Understanding Hadoop & It’s Components
3. Components of Hadoop Ecosystem
4. Data Storage Component of Hadoop
5. Data Processing Component of Hadoop
6. Data Access Component of Hadoop
7. Data Management Component of Hadoop
8.Hadoop Security Management Tool: Knox ,Ranger
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Determination of Equivalent Circuit parameters and performance characteristic...pvpriya2
Includes the testing of induction motor to draw the circle diagram of induction motor with step wise procedure and calculation for the same. Also explains the working and application of Induction generator
Sri Guru Hargobind Ji - Bandi Chor Guru.pdfBalvir Singh
Sri Guru Hargobind Ji (19 June 1595 - 3 March 1644) is revered as the Sixth Nanak.
• On 25 May 1606 Guru Arjan nominated his son Sri Hargobind Ji as his successor. Shortly
afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
• Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
• As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
people’s freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
• He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
Levelised Cost of Hydrogen (LCOH) Calculator ManualMassimo Talia
The aim of this manual is to explain the
methodology behind the Levelized Cost of
Hydrogen (LCOH) calculator. Moreover, this
manual also demonstrates how the calculator
can be used for estimating the expenses associated with hydrogen production in Europe
using low-temperature electrolysis considering different sources of electricity
This study Examines the Effectiveness of Talent Procurement through the Imple...DharmaBanothu
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.