This document summarizes a research paper that studied techniques for mitigating data skew and partition skew in MapReduce applications. It describes how skew can occur from unevenly distributed data or straggler nodes. It then summarizes a technique called LIBRA that uses sample map tasks to estimate data distribution, partitions the data accordingly, and allows reduce tasks to start earlier.
LARGE-SCALE DATA PROCESSING USING MAPREDUCE IN CLOUD COMPUTING ENVIRONMENTijwscjournal
The computer industry is being challenged to develop methods and techniques for affordable data processing on large datasets at optimum response times. The technical challenges in dealing with the increasing demand to handle vast quantities of data is daunting and on the rise. One of the recent processing models with a more efficient and intuitive solution to rapidly process large amount of data in parallel is called MapReduce. It is a framework defining a template approach of programming to perform large-scale data computation on clusters of machines in a cloud computing environment. MapReduce provides automatic parallelization and distribution of computation based on several processors. It hides the complexity of writing parallel and distributed programming code. This paper provides a comprehensive systematic review and analysis of large-scale dataset processing and dataset handling challenges and
requirements in a cloud computing environment by using the MapReduce framework and its open-source implementation Hadoop. We defined requirements for MapReduce systems to perform large-scale data processing. We also proposed the MapReduce framework and one implementation of this framework on Amazon Web Services. At the end of the paper, we presented an experimentation of running MapReduce
system in a cloud environment. This paper outlines one of the best techniques to process large datasets is MapReduce; it also can help developers to do parallel and distributed computation in a cloud environment.
Equalizing the amount of processing time for each reducer instead of equalizing the amount of data each process in heterogeneous environment. A lightweight strategy to address the data skew problem among the reductions of MapReduce applications. MapReduce has been widely used in various applications, including web indexing, log analysis, data mining, scientific simulations and machine translations. The data skew refers to the imbalance in the amount of data assigned to each task.Using an innovative sampling method which can achieve a highly accurate approximation to the distribution of the intermediate data by sampling only a small fraction during the map processing and to reduce the data in reducer side. Prioritizing the sampling tasks for partitioning decision and splitting of large keys is supported when application semantics permit.Thus providing a reduced data of total ordered output as a result by range partitioner. In the proposed system, the data reduction is by predicting the reduction orders in parallel data processing using feature and instance selection. The accuracy of the data scale and data skew is effectively improved by CHI-ICF data reduction technique. In the existing system normal data distribution is calculated instead here still efficient distribution of data using the feature selection by χ 2 statistics (CHI) and instance selection by Iterative case filter (ICF) is processed.
The objective of this paper is to present the hybrid approach for edge detection. Under this technique, edge
detection is performed in two phase. In first phase, Canny Algorithm is applied for image smoothing and in
second phase neural network is to detecting actual edges. Neural network is a wonderful tool for edge
detection. As it is a non-linear network with built-in thresholding capability. Neural Network can be trained
with back propagation technique using few training patterns but the most important and difficult part is to
identify the correct and proper training set.
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.
Enhancement of Map Function Image Processing System Using DHRF Algorithm on B...AM Publications
Cloud computing is the concept of distributing a work and also processing the same work over the internet. Cloud
computing is called as service on demand. It is always available on the internet in Pay and Use mode. Processing of the Big
Data takes more time to compute MRI and DICOM data. The processing of hard tasks like this can be solved by using the
concept of MapReduce. MapReduce function is a concept of Map and Reduce functions. Map is the process of splitting or
dividing data. Reduce function is the process of integrating the output of the Map’s input to produce the result. The Map
function does two various image processing techniques to process the input data. Java Advanced Imaging (JAI) is introduced
in the map function in this proposed work. The processed intermediate data of the Map function is sent to the Reduce function
for the further process. The Dynamic Handover Reduce Function (DHRF) algorithm is introduced in the reduce function in
this work. This algorithm is implemented in the Reduce function to reduce the waiting time while processing the intermediate
data. The DHRF algorithm gives the final output by processing the Reduce function. The enhanced MapReduce concept and
proposed optimized algorithm is made to work on Euca2ool (a Cloud tool) to produce an effective and better output when
compared with the previous work in the field of Cloud Computing and Big Data.
Distributed Feature Selection for Efficient Economic Big Data AnalysisIRJET Journal
The document proposes a new framework for efficiently analyzing large and high-dimensional economic big data. The framework combines methods for economic feature selection and econometric model construction to identify patterns in economic development from vast amounts of economic indicator data. It relies on three key aspects: 1) novel data pre-processing techniques to prepare high-quality economic data, 2) an innovative distributed feature identification solution to locate important economic indicators from multidimensional datasets, and 3) new econometric models to capture patterns of economic development. The framework is demonstrated on economic data collected over 30 years from over 300 towns in Dalian, China.
The document discusses Hadoop MapReduce. It describes Hadoop as a framework for distributed processing of large datasets across computer clusters. MapReduce is the programming model used in Hadoop for processing and generating large datasets in parallel. The two main components of Hadoop are HDFS for storage and MapReduce for processing. MapReduce involves two main phases - the map phase where input data is converted into intermediate outputs, and the reduce phase where the outputs are aggregated to form the final results.
MAP-REDUCE IMPLEMENTATIONS: SURVEY AND PERFORMANCE COMPARISONijcsit
Map Reduce has gained remarkable significance as a rominent parallel data processing tool in the research community, academia and industry with the spurt in volume of data that is to be analyzed. Map Reduce is used in different applications such as data mining, data analytic where massive data analysis is required, but still it is constantly being explored on different parameters such as performance and efficiency. This survey intends to explore large scale data processing using Map Reduce and its various implementations to facilitate the database, researchers and other communities in developing the technical understanding of the Map Reduce framework. In this survey, different Map Reduce implementations are explored and their inherent features are compared on different parameters. It also addresses the open issues and challenges raised on fully functional DBMS/Data Warehouse on Map Reduce. The comparison of various Map Reduce implementations is done with the most popular implementation Hadoop and other similar implementations using other platforms.
LARGE-SCALE DATA PROCESSING USING MAPREDUCE IN CLOUD COMPUTING ENVIRONMENTijwscjournal
The computer industry is being challenged to develop methods and techniques for affordable data processing on large datasets at optimum response times. The technical challenges in dealing with the increasing demand to handle vast quantities of data is daunting and on the rise. One of the recent processing models with a more efficient and intuitive solution to rapidly process large amount of data in parallel is called MapReduce. It is a framework defining a template approach of programming to perform large-scale data computation on clusters of machines in a cloud computing environment. MapReduce provides automatic parallelization and distribution of computation based on several processors. It hides the complexity of writing parallel and distributed programming code. This paper provides a comprehensive systematic review and analysis of large-scale dataset processing and dataset handling challenges and
requirements in a cloud computing environment by using the MapReduce framework and its open-source implementation Hadoop. We defined requirements for MapReduce systems to perform large-scale data processing. We also proposed the MapReduce framework and one implementation of this framework on Amazon Web Services. At the end of the paper, we presented an experimentation of running MapReduce
system in a cloud environment. This paper outlines one of the best techniques to process large datasets is MapReduce; it also can help developers to do parallel and distributed computation in a cloud environment.
Equalizing the amount of processing time for each reducer instead of equalizing the amount of data each process in heterogeneous environment. A lightweight strategy to address the data skew problem among the reductions of MapReduce applications. MapReduce has been widely used in various applications, including web indexing, log analysis, data mining, scientific simulations and machine translations. The data skew refers to the imbalance in the amount of data assigned to each task.Using an innovative sampling method which can achieve a highly accurate approximation to the distribution of the intermediate data by sampling only a small fraction during the map processing and to reduce the data in reducer side. Prioritizing the sampling tasks for partitioning decision and splitting of large keys is supported when application semantics permit.Thus providing a reduced data of total ordered output as a result by range partitioner. In the proposed system, the data reduction is by predicting the reduction orders in parallel data processing using feature and instance selection. The accuracy of the data scale and data skew is effectively improved by CHI-ICF data reduction technique. In the existing system normal data distribution is calculated instead here still efficient distribution of data using the feature selection by χ 2 statistics (CHI) and instance selection by Iterative case filter (ICF) is processed.
The objective of this paper is to present the hybrid approach for edge detection. Under this technique, edge
detection is performed in two phase. In first phase, Canny Algorithm is applied for image smoothing and in
second phase neural network is to detecting actual edges. Neural network is a wonderful tool for edge
detection. As it is a non-linear network with built-in thresholding capability. Neural Network can be trained
with back propagation technique using few training patterns but the most important and difficult part is to
identify the correct and proper training set.
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.
Enhancement of Map Function Image Processing System Using DHRF Algorithm on B...AM Publications
Cloud computing is the concept of distributing a work and also processing the same work over the internet. Cloud
computing is called as service on demand. It is always available on the internet in Pay and Use mode. Processing of the Big
Data takes more time to compute MRI and DICOM data. The processing of hard tasks like this can be solved by using the
concept of MapReduce. MapReduce function is a concept of Map and Reduce functions. Map is the process of splitting or
dividing data. Reduce function is the process of integrating the output of the Map’s input to produce the result. The Map
function does two various image processing techniques to process the input data. Java Advanced Imaging (JAI) is introduced
in the map function in this proposed work. The processed intermediate data of the Map function is sent to the Reduce function
for the further process. The Dynamic Handover Reduce Function (DHRF) algorithm is introduced in the reduce function in
this work. This algorithm is implemented in the Reduce function to reduce the waiting time while processing the intermediate
data. The DHRF algorithm gives the final output by processing the Reduce function. The enhanced MapReduce concept and
proposed optimized algorithm is made to work on Euca2ool (a Cloud tool) to produce an effective and better output when
compared with the previous work in the field of Cloud Computing and Big Data.
Distributed Feature Selection for Efficient Economic Big Data AnalysisIRJET Journal
The document proposes a new framework for efficiently analyzing large and high-dimensional economic big data. The framework combines methods for economic feature selection and econometric model construction to identify patterns in economic development from vast amounts of economic indicator data. It relies on three key aspects: 1) novel data pre-processing techniques to prepare high-quality economic data, 2) an innovative distributed feature identification solution to locate important economic indicators from multidimensional datasets, and 3) new econometric models to capture patterns of economic development. The framework is demonstrated on economic data collected over 30 years from over 300 towns in Dalian, China.
The document discusses Hadoop MapReduce. It describes Hadoop as a framework for distributed processing of large datasets across computer clusters. MapReduce is the programming model used in Hadoop for processing and generating large datasets in parallel. The two main components of Hadoop are HDFS for storage and MapReduce for processing. MapReduce involves two main phases - the map phase where input data is converted into intermediate outputs, and the reduce phase where the outputs are aggregated to form the final results.
MAP-REDUCE IMPLEMENTATIONS: SURVEY AND PERFORMANCE COMPARISONijcsit
Map Reduce has gained remarkable significance as a rominent parallel data processing tool in the research community, academia and industry with the spurt in volume of data that is to be analyzed. Map Reduce is used in different applications such as data mining, data analytic where massive data analysis is required, but still it is constantly being explored on different parameters such as performance and efficiency. This survey intends to explore large scale data processing using Map Reduce and its various implementations to facilitate the database, researchers and other communities in developing the technical understanding of the Map Reduce framework. In this survey, different Map Reduce implementations are explored and their inherent features are compared on different parameters. It also addresses the open issues and challenges raised on fully functional DBMS/Data Warehouse on Map Reduce. The comparison of various Map Reduce implementations is done with the most popular implementation Hadoop and other similar implementations using other platforms.
Data Distribution Handling on Cloud for Deployment of Big Dataijccsa
Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to process massive amounts of data. Processing large datasets has become crucial in research and business environments. The big challenges associated with processing large datasets is the vast infrastructure required. Cloud computing provides vast infrastructure to store and process Big data. Vms can be provisioned on demand in cloud to process the data by forming cluster of Vms . Map Reduce paradigm can be used to process data wherein the mapper assign part of task to particular Vms in cluster and reducer combines individual output from each Vms to produce final result. we have proposed an algorithm to reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall data processing time in cloud.
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.
Implementation of p pic algorithm in map reduce to handle big dataeSAT Publishing House
This document presents an implementation of the p-PIC clustering algorithm using the MapReduce framework to handle big data. P-PIC is a parallel version of the Power Iteration Clustering (PIC) algorithm that is able to cluster large datasets in a distributed environment. The document first provides background on PIC and challenges with scaling to big data. It then describes how p-PIC addresses these challenges using MPI for parallelization. The design of implementing p-PIC within MapReduce is presented, including the map and reduce functions. Experimental results on synthetic datasets up to 100,000 records show that p-PIC using MapReduce has increased performance and scalability compared to the original p-PIC implementation using MPI.
The document describes the development of a Hydrologic Community Modeling System (HCMS) using a workflow engine called TRIDENT. The HCMS will allow for seamlessly integrated hydrologic models with interchangeable and portable modules. It will include libraries for data access, data processing, hydrologic models, and post-analysis tools. TRIDENT facilitates composing, executing, archiving, and sharing scientific workflows. Its use in hydrologic modeling provides benefits like flexible model setup, interactive or automated execution, high-performance computing, and provenance capture. The document introduces several libraries being developed as part of the HCMS.
Simplified Data Processing On Large ClusterHarsh Kevadia
A computer cluster consists of a set of loosely connected or tightly connected computers that work together so that in many respects they can be viewed as a single system. They are connected through fast local area network and are deployed to improve performance over that of single computer. We know that on the web large amount of data are being stored, processed and retrieved in a few milliseconds. Doing so with help of single computer machine is very difficult task. And so we require cluster of machines which can perform this task.
Although using cluster for processing data is not enough, we need to develop a technique that can perform this task easily and efficiently. MapReduce programming model is used for this type of processing. In this model Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key.
Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system.
Dynamically Partitioning Big Data Using Virtual Machine MappingAM Publications
Big data refers to data that is so large that it exceeds the processing capabilities of traditional systems. Big data
can be awkward to work and the storage, processing and analysis of big data can be problematic. MapReduce is a recent
programming model that can handle big data. MapReduce achieves this by distributing the storage and processing of data
amongst a large number of computers (nodes). However, this means the time required to process a MapReduce job is
dependent on whichever node is last to complete a task. This problem is bad situation by heterogeneous environments. In
this paper a methodologyis properly to improve MapReduce execution in heterogeneous environments. It is carried out
using dynamically partitioning data during the Map phase and by using virtual machine mapping in the Reduce phase in
order to maximize resource utilization.
This document discusses architectural and security management for grid computing. It begins by defining grid computing as an environment that enables sharing of distributed resources across organizations to achieve common goals. It then describes the key components of a grid, including computation resources, storage, communications, software/licenses, and special equipment. The document outlines a four-level grid architecture including a fabric level, core middleware level, user middleware level, and application level. It also discusses important aspects of grid computing such as resource balancing, reliability through distribution, parallel CPU capacity, and management of different projects. Finally, it emphasizes that security is a major concern for grid computing due to the open nature of sharing resources across organizational boundaries.
Efficient Point Cloud Pre-processing using The Point Cloud LibraryCSCJournals
Robotics, video games, environmental mapping and medical are some of the fields that use 3D data processing. In this paper we propose a novel optimization approach for the open source Point Cloud Library (PCL) that is frequently used for processing 3D data. Three main aspects of the PCL are discussed: point cloud creation from disparity of color image pairs; voxel grid downsample filtering to simplify point clouds; and passthrough filtering to adjust the size of the point cloud. Additionally, OpenGL shader based rendering is examined. An optimization technique based on CPU cycle measurement is proposed and applied in order to optimize those parts of the pre-processing chain where measured performance is slowest. Results show that with optimized modules the performance of the pre-processing chain has increased 69 fold.
Cloud computing is a realized wonder. It delights its users by providing applications, platforms and infrastructure without any initial investment. The “pay as you use” strategy comforts the users. The usage can be increased by adding infrastructure, tools or applications to the existing application. The realistic beauty of cloud computing is that there is no need for any sophisticated tool for access, web browser or even smartphone will do. Cloud computing is a windfall for small organizations having less sensitive information. But for large organizations, the risks related to security may be daunting. Necessary steps have to be taken for managing the issues like confidentiality, integrity, privacy, availability and so on. In this paper availability is taken and studied in a multi-dimensional perspective. Availability is taken a key issue and the mechanisms that enable enhancement are analyzed.
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.
Using a Cloud to Replenish Parched Groundwater Modeling EffortsJoseph Luchette
This document discusses how cloud computing can be used to improve groundwater modeling by providing unprecedented computing power. Cloud computing allows modelers to access virtual computers over the internet in a cost-effective way. This empowers modelers to perform model calibration and uncertainty analysis using sophisticated approaches that were previously computationally prohibitive. The document specifically focuses on how cloud computing can facilitate parameter estimation, which is well-suited for parallel computing. It describes how BeoPEST software allows a modeler to efficiently distribute model runs across local computers and virtual machines in the cloud.
This document proposes CATCH, a cloud-based system to improve data transfer efficiency for high-performance computing (HPC) workloads. CATCH uses cloud storage to stage input data for HPC jobs and offload output data, in order to reduce storage usage at HPC centers and improve data transfer times. Evaluation of CATCH using a real cloud platform and HPC workload logs showed it could reduce average transfer times by up to 81.1% and decrease wait times and storage usage at HPC centers.
IRJET - Evaluating and Comparing the Two Variation with Current Scheduling Al...IRJET Journal
This document presents two variations of a job-driven scheduling scheme called JOSS for efficiently executing MapReduce jobs on remote outsourced data across multiple data centers. The goal of JOSS is to improve data locality for map and reduce tasks, avoid job starvation, and improve job performance. Extensive experiments show that the two JOSS variations, called JOSS-T and JOSS-J, outperform other scheduling algorithms in terms of data locality and network overhead without significant overhead. JOSS-T performs best for workloads of small jobs, while JOSS-J provides the shortest workload time for jobs of varying sizes distributed across data centers.
Survey on Load Rebalancing for Distributed File System in CloudAM Publications
1. The document discusses load rebalancing algorithms for distributed file systems in cloud computing. It aims to balance the load across storage nodes to improve performance and resource utilization.
2. A large file is divided into chunks which are distributed across multiple storage nodes. If some nodes become overloaded (heavy nodes) while others are underloaded (light nodes), chunks can be migrated from heavy to light nodes using load rebalancing algorithms.
3. The algorithms structure storage nodes in a distributed hash table to allow efficient lookup and migration of chunks between nodes. Nodes independently calculate their load and migrate chunks to balance load without global knowledge of all nodes' loads.
Review: Data Driven Traffic Flow Forecasting using MapReduce in Distributed M...AM Publications
from last decade, the use of communication and transportation technology increases in urban traffic
management system. To predict the correct result forecasting technique is used. Furthermore, as more data are
collected, increase in traffic data. In short, traffic flow forecasting system find out collection of historical observations
for records similar to the current conditions and uses these to estimate the future state of the system. In this paper we
focus on data driven traffic flow forecasting system which is based on MapReduce framework for distributed system
with Bayesian network approach. For probability distribution of data between two adjacent node i.e. data used for
forecasting(Input node) and data which is forecasted (output node) used a Gaussian mixture model (GMM) whose
parameters are updated using Expectation Maximization algorithm. Finally focus on model fusion, main problem in
distributed modelling for data storage and processing in traffic flow forecasting system.
Data Warehouses store integrated and consistent data in a subject-oriented data repository dedicated
especially to support business intelligence processes. However, keeping these repositories updated usually
involves complex and time-consuming processes, commonly denominated as Extract-Transform-Load tasks.
These data intensive tasks normally execute in a limited time window and their computational requirements
tend to grow in time as more data is dealt with. Therefore, we believe that a grid environment could suit
rather well as support for the backbone of the technical infrastructure with the clear financial advantage of
using already acquired desktop computers normally present in the organization. This article proposes a
different approach to deal with the distribution of ETL processes in a grid environment, taking into account
not only the processing performance of its nodes but also the existing bandwidth to estimate the grid
availability in a near future and therefore optimize workflow distribution.
Cloud computing is the one of the emerging techniques to process the big data. Large collection of set or large
volume of data is known as big data. Processing of big data (MRI images and DICOM images) normally takes
more time compare with other data. The main tasks such as handling big data can be solved by using the concepts
of hadoop. Enhancing the hadoop concept it will help the user to process the large set of images or data. The
Advanced Hadoop Distributed File System (AHDF) and MapReduce are the two default main functions which
are used to enhance hadoop. HDF method is a hadoop file storing system, which is used for storing and retrieving
the data. MapReduce is the combinations of two functions namely maps and reduce. Map is the process of
splitting the inputs and reduce is the process of integrating the output of map’s input. Recently, in medical fields
the experienced problems like machine failure and fault tolerance while processing the result for the scanned
data. A unique optimized time scheduling algorithm, called Advanced Dynamic Handover Reduce Function
(ADHRF) algorithm is introduced in the reduce function. Enhancement of hadoop and cloud introduction of
ADHRF helps to overcome the processing risks, to get optimized result with less waiting time and reduction in
error percentage of the output image
VIRTUAL MACHINE SCHEDULING IN CLOUD COMPUTING ENVIRONMENTijmpict
Cloud computing is an upcoming technology in dispersed computing facilitating paying for each model as
for each user demand and need. Cloud incorporates a set of virtual machine which comprises both storage
and computational facility. The fundamental goal of cloud computing is to offer effective access to isolated
and geographically circulated resources. Cloud is growing every day and experiences numerous problems
such as scheduling. Scheduling means a collection of policies to regulate the order of task to be executed
by a computer system. An excellent scheduler derives its scheduling plan in accordance with the type of
work and the varying environment. This research paper demonstrates a generalized precedence algorithm
for effective performance of work and contrast with Round Robin and FCFS Scheduling. Algorithm needs
to be tested within CloudSim toolkit and outcome illustrates that it provide good presentation compared
some customary scheduling algorithm.
A TALE of DATA PATTERN DISCOVERY IN PARALLELJenny Liu
In the era of IoTs and A.I., distributed and parallel computing is embracing big data driven and algorithm focused applications and services. With rapid progress and development on parallel frameworks, algorithms and accelerated computing capacities, it still remains challenging on deliver an efficient and scalable data analysis solution. This talk shares a research experience on data pattern discovery in domain applications. In particular, the research scrutinizes key factors in analysis workflow design and data parallelism improvement on cloud.
TOWARDS REDUCTION OF DATA FLOW IN A DISTRIBUTED NETWORK USING PRINCIPAL COMPO...cscpconf
For performing distributed data mining two approaches are possible: First, data from several sources are copied to a data warehouse and mining algorithms are applied in it. Secondly,
mining can performed at the local sites and the results can be aggregated. When the number of
features is high, a lot of bandwidth is consumed in transferring datasets to a centralized location. For this dimensionality reduction can be done at the local sites. In dimensionality reduction a certain encoding is applied on data so as to obtain its compressed form. The
reduced features thus obtained at the local sites are aggregated and data mining algorithms are applied on them. There are several methods of performing dimensionality reduction. Two most important ones are Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA). Here a detailed study is done on how PCA could be useful in reducing data flow across a distributed network.
A comparative survey based on processing network traffic data using hadoop pi...ijcses
Big data analysis has now become an integral part of many computational and statistical departments.
Analysis of peta-byte scale of data is having an enhanced importance in the present day scenario. Big data
manipulation is now considered as a key area of research in the field of data analytics and novel
techniques are being evolved day by day. Thousands of transaction requests are being processed in every
minute by different websites related to e-commerce, shopping carts and online banking. Here comes the
need of network traffic and weblog analysis for which Hadoop comes as a suggested solution. It can
efficiently process the Netflow data collected from routers, switches or even from website access logs at
fixed intervals.
BIGDATA- Survey on Scheduling Methods in Hadoop MapReduceMahantesh Angadi
The document summarizes a technical seminar presentation on scheduling methods in the Hadoop MapReduce framework. The presentation covers the motivation for Hadoop and MapReduce, provides an introduction to big data and Hadoop, and describes HDFS and the MapReduce programming model. It then discusses challenges in MapReduce scheduling and surveys the literature on existing scheduling methods. The presentation surveys five papers on proposed MapReduce scheduling methods, summarizing the key points of each. It concludes that improving data locality can enhance performance and that future work could consider scheduling algorithms for heterogeneous clusters.
Data Distribution Handling on Cloud for Deployment of Big Dataijccsa
Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to process massive amounts of data. Processing large datasets has become crucial in research and business environments. The big challenges associated with processing large datasets is the vast infrastructure required. Cloud computing provides vast infrastructure to store and process Big data. Vms can be provisioned on demand in cloud to process the data by forming cluster of Vms . Map Reduce paradigm can be used to process data wherein the mapper assign part of task to particular Vms in cluster and reducer combines individual output from each Vms to produce final result. we have proposed an algorithm to reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall data processing time in cloud.
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.
Implementation of p pic algorithm in map reduce to handle big dataeSAT Publishing House
This document presents an implementation of the p-PIC clustering algorithm using the MapReduce framework to handle big data. P-PIC is a parallel version of the Power Iteration Clustering (PIC) algorithm that is able to cluster large datasets in a distributed environment. The document first provides background on PIC and challenges with scaling to big data. It then describes how p-PIC addresses these challenges using MPI for parallelization. The design of implementing p-PIC within MapReduce is presented, including the map and reduce functions. Experimental results on synthetic datasets up to 100,000 records show that p-PIC using MapReduce has increased performance and scalability compared to the original p-PIC implementation using MPI.
The document describes the development of a Hydrologic Community Modeling System (HCMS) using a workflow engine called TRIDENT. The HCMS will allow for seamlessly integrated hydrologic models with interchangeable and portable modules. It will include libraries for data access, data processing, hydrologic models, and post-analysis tools. TRIDENT facilitates composing, executing, archiving, and sharing scientific workflows. Its use in hydrologic modeling provides benefits like flexible model setup, interactive or automated execution, high-performance computing, and provenance capture. The document introduces several libraries being developed as part of the HCMS.
Simplified Data Processing On Large ClusterHarsh Kevadia
A computer cluster consists of a set of loosely connected or tightly connected computers that work together so that in many respects they can be viewed as a single system. They are connected through fast local area network and are deployed to improve performance over that of single computer. We know that on the web large amount of data are being stored, processed and retrieved in a few milliseconds. Doing so with help of single computer machine is very difficult task. And so we require cluster of machines which can perform this task.
Although using cluster for processing data is not enough, we need to develop a technique that can perform this task easily and efficiently. MapReduce programming model is used for this type of processing. In this model Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key.
Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system.
Dynamically Partitioning Big Data Using Virtual Machine MappingAM Publications
Big data refers to data that is so large that it exceeds the processing capabilities of traditional systems. Big data
can be awkward to work and the storage, processing and analysis of big data can be problematic. MapReduce is a recent
programming model that can handle big data. MapReduce achieves this by distributing the storage and processing of data
amongst a large number of computers (nodes). However, this means the time required to process a MapReduce job is
dependent on whichever node is last to complete a task. This problem is bad situation by heterogeneous environments. In
this paper a methodologyis properly to improve MapReduce execution in heterogeneous environments. It is carried out
using dynamically partitioning data during the Map phase and by using virtual machine mapping in the Reduce phase in
order to maximize resource utilization.
This document discusses architectural and security management for grid computing. It begins by defining grid computing as an environment that enables sharing of distributed resources across organizations to achieve common goals. It then describes the key components of a grid, including computation resources, storage, communications, software/licenses, and special equipment. The document outlines a four-level grid architecture including a fabric level, core middleware level, user middleware level, and application level. It also discusses important aspects of grid computing such as resource balancing, reliability through distribution, parallel CPU capacity, and management of different projects. Finally, it emphasizes that security is a major concern for grid computing due to the open nature of sharing resources across organizational boundaries.
Efficient Point Cloud Pre-processing using The Point Cloud LibraryCSCJournals
Robotics, video games, environmental mapping and medical are some of the fields that use 3D data processing. In this paper we propose a novel optimization approach for the open source Point Cloud Library (PCL) that is frequently used for processing 3D data. Three main aspects of the PCL are discussed: point cloud creation from disparity of color image pairs; voxel grid downsample filtering to simplify point clouds; and passthrough filtering to adjust the size of the point cloud. Additionally, OpenGL shader based rendering is examined. An optimization technique based on CPU cycle measurement is proposed and applied in order to optimize those parts of the pre-processing chain where measured performance is slowest. Results show that with optimized modules the performance of the pre-processing chain has increased 69 fold.
Cloud computing is a realized wonder. It delights its users by providing applications, platforms and infrastructure without any initial investment. The “pay as you use” strategy comforts the users. The usage can be increased by adding infrastructure, tools or applications to the existing application. The realistic beauty of cloud computing is that there is no need for any sophisticated tool for access, web browser or even smartphone will do. Cloud computing is a windfall for small organizations having less sensitive information. But for large organizations, the risks related to security may be daunting. Necessary steps have to be taken for managing the issues like confidentiality, integrity, privacy, availability and so on. In this paper availability is taken and studied in a multi-dimensional perspective. Availability is taken a key issue and the mechanisms that enable enhancement are analyzed.
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.
Using a Cloud to Replenish Parched Groundwater Modeling EffortsJoseph Luchette
This document discusses how cloud computing can be used to improve groundwater modeling by providing unprecedented computing power. Cloud computing allows modelers to access virtual computers over the internet in a cost-effective way. This empowers modelers to perform model calibration and uncertainty analysis using sophisticated approaches that were previously computationally prohibitive. The document specifically focuses on how cloud computing can facilitate parameter estimation, which is well-suited for parallel computing. It describes how BeoPEST software allows a modeler to efficiently distribute model runs across local computers and virtual machines in the cloud.
This document proposes CATCH, a cloud-based system to improve data transfer efficiency for high-performance computing (HPC) workloads. CATCH uses cloud storage to stage input data for HPC jobs and offload output data, in order to reduce storage usage at HPC centers and improve data transfer times. Evaluation of CATCH using a real cloud platform and HPC workload logs showed it could reduce average transfer times by up to 81.1% and decrease wait times and storage usage at HPC centers.
IRJET - Evaluating and Comparing the Two Variation with Current Scheduling Al...IRJET Journal
This document presents two variations of a job-driven scheduling scheme called JOSS for efficiently executing MapReduce jobs on remote outsourced data across multiple data centers. The goal of JOSS is to improve data locality for map and reduce tasks, avoid job starvation, and improve job performance. Extensive experiments show that the two JOSS variations, called JOSS-T and JOSS-J, outperform other scheduling algorithms in terms of data locality and network overhead without significant overhead. JOSS-T performs best for workloads of small jobs, while JOSS-J provides the shortest workload time for jobs of varying sizes distributed across data centers.
Survey on Load Rebalancing for Distributed File System in CloudAM Publications
1. The document discusses load rebalancing algorithms for distributed file systems in cloud computing. It aims to balance the load across storage nodes to improve performance and resource utilization.
2. A large file is divided into chunks which are distributed across multiple storage nodes. If some nodes become overloaded (heavy nodes) while others are underloaded (light nodes), chunks can be migrated from heavy to light nodes using load rebalancing algorithms.
3. The algorithms structure storage nodes in a distributed hash table to allow efficient lookup and migration of chunks between nodes. Nodes independently calculate their load and migrate chunks to balance load without global knowledge of all nodes' loads.
Review: Data Driven Traffic Flow Forecasting using MapReduce in Distributed M...AM Publications
from last decade, the use of communication and transportation technology increases in urban traffic
management system. To predict the correct result forecasting technique is used. Furthermore, as more data are
collected, increase in traffic data. In short, traffic flow forecasting system find out collection of historical observations
for records similar to the current conditions and uses these to estimate the future state of the system. In this paper we
focus on data driven traffic flow forecasting system which is based on MapReduce framework for distributed system
with Bayesian network approach. For probability distribution of data between two adjacent node i.e. data used for
forecasting(Input node) and data which is forecasted (output node) used a Gaussian mixture model (GMM) whose
parameters are updated using Expectation Maximization algorithm. Finally focus on model fusion, main problem in
distributed modelling for data storage and processing in traffic flow forecasting system.
Data Warehouses store integrated and consistent data in a subject-oriented data repository dedicated
especially to support business intelligence processes. However, keeping these repositories updated usually
involves complex and time-consuming processes, commonly denominated as Extract-Transform-Load tasks.
These data intensive tasks normally execute in a limited time window and their computational requirements
tend to grow in time as more data is dealt with. Therefore, we believe that a grid environment could suit
rather well as support for the backbone of the technical infrastructure with the clear financial advantage of
using already acquired desktop computers normally present in the organization. This article proposes a
different approach to deal with the distribution of ETL processes in a grid environment, taking into account
not only the processing performance of its nodes but also the existing bandwidth to estimate the grid
availability in a near future and therefore optimize workflow distribution.
Cloud computing is the one of the emerging techniques to process the big data. Large collection of set or large
volume of data is known as big data. Processing of big data (MRI images and DICOM images) normally takes
more time compare with other data. The main tasks such as handling big data can be solved by using the concepts
of hadoop. Enhancing the hadoop concept it will help the user to process the large set of images or data. The
Advanced Hadoop Distributed File System (AHDF) and MapReduce are the two default main functions which
are used to enhance hadoop. HDF method is a hadoop file storing system, which is used for storing and retrieving
the data. MapReduce is the combinations of two functions namely maps and reduce. Map is the process of
splitting the inputs and reduce is the process of integrating the output of map’s input. Recently, in medical fields
the experienced problems like machine failure and fault tolerance while processing the result for the scanned
data. A unique optimized time scheduling algorithm, called Advanced Dynamic Handover Reduce Function
(ADHRF) algorithm is introduced in the reduce function. Enhancement of hadoop and cloud introduction of
ADHRF helps to overcome the processing risks, to get optimized result with less waiting time and reduction in
error percentage of the output image
VIRTUAL MACHINE SCHEDULING IN CLOUD COMPUTING ENVIRONMENTijmpict
Cloud computing is an upcoming technology in dispersed computing facilitating paying for each model as
for each user demand and need. Cloud incorporates a set of virtual machine which comprises both storage
and computational facility. The fundamental goal of cloud computing is to offer effective access to isolated
and geographically circulated resources. Cloud is growing every day and experiences numerous problems
such as scheduling. Scheduling means a collection of policies to regulate the order of task to be executed
by a computer system. An excellent scheduler derives its scheduling plan in accordance with the type of
work and the varying environment. This research paper demonstrates a generalized precedence algorithm
for effective performance of work and contrast with Round Robin and FCFS Scheduling. Algorithm needs
to be tested within CloudSim toolkit and outcome illustrates that it provide good presentation compared
some customary scheduling algorithm.
A TALE of DATA PATTERN DISCOVERY IN PARALLELJenny Liu
In the era of IoTs and A.I., distributed and parallel computing is embracing big data driven and algorithm focused applications and services. With rapid progress and development on parallel frameworks, algorithms and accelerated computing capacities, it still remains challenging on deliver an efficient and scalable data analysis solution. This talk shares a research experience on data pattern discovery in domain applications. In particular, the research scrutinizes key factors in analysis workflow design and data parallelism improvement on cloud.
TOWARDS REDUCTION OF DATA FLOW IN A DISTRIBUTED NETWORK USING PRINCIPAL COMPO...cscpconf
For performing distributed data mining two approaches are possible: First, data from several sources are copied to a data warehouse and mining algorithms are applied in it. Secondly,
mining can performed at the local sites and the results can be aggregated. When the number of
features is high, a lot of bandwidth is consumed in transferring datasets to a centralized location. For this dimensionality reduction can be done at the local sites. In dimensionality reduction a certain encoding is applied on data so as to obtain its compressed form. The
reduced features thus obtained at the local sites are aggregated and data mining algorithms are applied on them. There are several methods of performing dimensionality reduction. Two most important ones are Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA). Here a detailed study is done on how PCA could be useful in reducing data flow across a distributed network.
A comparative survey based on processing network traffic data using hadoop pi...ijcses
Big data analysis has now become an integral part of many computational and statistical departments.
Analysis of peta-byte scale of data is having an enhanced importance in the present day scenario. Big data
manipulation is now considered as a key area of research in the field of data analytics and novel
techniques are being evolved day by day. Thousands of transaction requests are being processed in every
minute by different websites related to e-commerce, shopping carts and online banking. Here comes the
need of network traffic and weblog analysis for which Hadoop comes as a suggested solution. It can
efficiently process the Netflow data collected from routers, switches or even from website access logs at
fixed intervals.
BIGDATA- Survey on Scheduling Methods in Hadoop MapReduceMahantesh Angadi
The document summarizes a technical seminar presentation on scheduling methods in the Hadoop MapReduce framework. The presentation covers the motivation for Hadoop and MapReduce, provides an introduction to big data and Hadoop, and describes HDFS and the MapReduce programming model. It then discusses challenges in MapReduce scheduling and surveys the literature on existing scheduling methods. The presentation surveys five papers on proposed MapReduce scheduling methods, summarizing the key points of each. It concludes that improving data locality can enhance performance and that future work could consider scheduling algorithms for heterogeneous clusters.
This document discusses using MapReduce and Apache Hadoop for large-scale data mining and analytics. It describes several Apache Hadoop projects like HDFS, MapReduce, HBase and Mahout. It discusses using Mahout for tasks like clustering, classification and recommendation. The document reviews literature on parallel K-means clustering with MapReduce and using clouds for scalable big data analytics. It outlines a plan to study parallel K-means clustering and implement a solution to handle large datasets.
The document summarizes the CURE clustering algorithm, which uses a hierarchical approach that selects a constant number of representative points from each cluster to address limitations of centroid-based and all-points clustering methods. It employs random sampling and partitioning to speed up processing of large datasets. Experimental results show CURE detects non-spherical and variably-sized clusters better than compared methods, and it has faster execution times on large databases due to its sampling approach.
Big data Clustering Algorithms And StrategiesFarzad Nozarian
The document discusses various algorithms for big data clustering. It begins by covering preprocessing techniques such as data reduction. It then covers hierarchical, prototype-based, density-based, grid-based, and scalability clustering algorithms. Specific algorithms discussed include K-means, K-medoids, PAM, CLARA/CLARANS, DBSCAN, OPTICS, MR-DBSCAN, DBCURE, and hierarchical algorithms like PINK and l-SL. The document emphasizes techniques for scaling these algorithms to large datasets, including partitioning, sampling, approximation strategies, and MapReduce implementations.
This document provides an overview of clustering techniques. It defines clustering as grouping a set of similar objects into classes, with objects within a cluster being similar to each other and dissimilar to objects in other clusters. The document then discusses partitioning, hierarchical, and density-based clustering methods. It also covers mathematical elements of clustering like partitions, distances, and data types. The goal of clustering is to minimize a similarity function to create high similarity within clusters and low similarity between clusters.
This document discusses distributed deep learning on Hadoop clusters using CaffeOnSpark. CaffeOnSpark is an open source project that allows deep learning models defined in Caffe to be trained and run on large datasets distributed across a Spark cluster. It provides a scalable architecture that can reduce training time by up to 19x compared to single node training. CaffeOnSpark provides APIs in Scala and Python and can be easily deployed on both public and private clouds. It has been used in production at Yahoo since 2015 to power applications like Flickr and Yahoo Weather.
This document provides an overview of the Hadoop MapReduce Fundamentals course. It discusses what Hadoop is, why it is used, common business problems it can address, and companies that use Hadoop. It also outlines the core parts of Hadoop distributions and the Hadoop ecosystem. Additionally, it covers common MapReduce concepts like HDFS, the MapReduce programming model, and Hadoop distributions. The document includes several code examples and screenshots related to Hadoop and MapReduce.
Hadoop Mapreduce Performance Enhancement Using In-Node Combinersijcsit
This document summarizes a research paper that proposes using in-node combiners to improve the performance of Hadoop MapReduce jobs. It discusses how MapReduce jobs are I/O intensive and describes two common bottlenecks: during the map phase when data is loaded from disks, and during the shuffle phase when intermediate results are transferred over the network. The paper introduces an in-node combiner approach to optimize I/O by locally aggregating intermediate results within nodes to reduce network traffic between mappers and reducers. It evaluates this approach through an experiment counting word occurrences in Twitter messages.
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.
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.
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.
Managing Big data using Hadoop Map Reduce in Telecom DomainAM Publications
Map reduce is a programming model for analysing and processing large massive data sets. Apache Hadoop is an efficient frame work and the most popular implementation of the map reduce model. Hadoop’s success has motivated research interest and has led to different modifications as well as extensions to framework. In this paper, the challenges faced in different domains like data storage, analytics, online processing and privacy/ security issues while handling big data are explored. Also, the various possible solutions with respect to Telecom domain with Hadoop Map reduce implementation is discussed in this paper.
A Comprehensive Study on Big Data Applications and Challengesijcisjournal
Big Data has gained much interest from the academia and the IT industry. In the digital and computing
world, information is generated and collected at a rate that quickly exceeds the boundary range. As
information is transferred and shared at light speed on optic fiber and wireless networks, the volume of
data and the speed of market growth increase. Conversely, the fast growth rate of such large data
generates copious challenges, such as the rapid growth of data, transfer speed, diverse data, and security.
Even so, Big Data is still in its early stage, and the domain has not been reviewed in general. Hence, this
study expansively surveys and classifies an assortment of attributes of Big Data, including its nature,
definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a
data life cycle that uses the technologies and terminologies of Big Data. Map/Reduce is a programming
model for efficient distributed computing. It works well with semi-structured and unstructured data. A
simple model but good for a lot of applications like Log processing and Web index building.
The document discusses big data and distributed computing. It explains that big data refers to large, unstructured datasets that are too large for traditional databases. Distributed computing uses multiple computers connected via a network to process large datasets in parallel. Hadoop is an open-source framework for distributed computing that uses MapReduce and HDFS for parallel processing and storage across clusters. HDFS stores data redundantly across nodes for fault tolerance.
This document discusses leveraging MapReduce with Hadoop to analyze weather data. It proposes building a data analytical engine using MapReduce on Hadoop to process massive amounts of temperature data from sensors. The document describes implementing MapReduce jobs to analyze National Climatic Data Center temperature data, with mappers filtering and assigning data to key-value pairs and reducers calculating averages, maximums, and minimums on the data. Overall, the document examines using Hadoop and MapReduce to scalably process large volumes of sensor weather data.
Leveraging Map Reduce With Hadoop for Weather Data Analytics iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Big Data Storage System Based on a Distributed Hash Tables Systemijdms
The Big Data is unavoidable considering the place of the digital is the predominant form of communication in the daily life of the consumer. The control of its stakes and the quality its data must be a priority in order not to distort the strategies arising from their treatment in the aim to derive profit. In order to achieve this, a lot of research work has been carried out companies and several platforms created. MapReduce, is one of the enabling technologies, has proven to be applicable to a wide range of fields. However, despite its importance recent work has shown its limitations. And to remedy this, the Distributed Hash Tables (DHT) has been used. Thus, this document not only analyses the and MapReduce implementations and Top-Level Domain (TLD)s in general, but it also provides a description of a model of DHT as well as some guidelines for the planification of the future research
1. The document proposes a Twiche framework to cache intermediate results from MapReduce jobs processing large amounts of Twitter data to improve efficiency.
2. Twiche requires minimal changes to the original MapReduce model and allows tasks to submit intermediate results to a cache manager to avoid duplicate computations.
3. An evaluation showed Twiche can eliminate all duplicate tasks in incremental MapReduce jobs without substantial changes to application code.
Big data refers to large volumes of unstructured or semi-structured data that is difficult to process using traditional databases and analysis tools. The amount of data generated daily is growing exponentially due to factors like increased internet usage and data collection by organizations. Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses HDFS for reliable storage and MapReduce as a programming model to process data in parallel across nodes.
This document provides an introduction and overview of Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop uses MapReduce and HDFS to parallelize workloads and store data redundantly across nodes to solve issues around hardware failure and combining results. Key aspects covered include how HDFS distributes and replicates data, how MapReduce isolates processing into mapping and reducing functions to abstract communication, and how Hadoop moves computation to the data to improve performance.
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.
Enhancing Performance and Fault Tolerance of Hadoop ClusterIRJET Journal
This document discusses enhancing performance and fault tolerance in Hadoop clusters. It proposes a method to identify faulty nodes in the cluster that are decreasing job execution efficiency. The method monitors node performance and categorizes nodes as active or blacklisted based on the number of task failures. If a node is frequently blacklisted, it is considered faulty. The experiment shows that removing faulty nodes identified by this method improves overall cluster efficiency by reducing job execution time.
A popular programming model for running data intensive applications on the cloud is map reduce. In
the Hadoop usually, jobs are scheduled in FIFO order by default. There are many map reduce
applications which require strict deadline. In Hadoop framework, scheduler wi t h deadline
con s t ra in t s has not been implemented. Existing schedulers d o not guarantee that the job will be
completed by a specific deadline. Some schedulers address the issue of deadlines but focus more on
improving s y s t em utilization. We have proposed an algorithm which facilitates the user to
specify a jobs deadline and evaluates whether the job can be finished before the deadline.
Scheduler with deadlines for Hadoop, which ensures that only jobs, whose deadlines can be met are
scheduled for execution. If the job submitted does not satisfy the specified deadline, physical or
virtual nodes can be added dynamically to complete the job within deadline[8].
A Survey on Data Mapping Strategy for data stored in the storage cloud 111NavNeet KuMar
This document describes a method for processing large amounts of data stored in cloud storage using Hadoop clusters. Data is uploaded to cloud storage by users and then processed using MapReduce on Hadoop clusters. The method involves storing data in the cloud for processing and then running MapReduce algorithms on Hadoop clusters to analyze the data in parallel. The results are then stored back in the cloud for users to download. An architecture is proposed involving a controller that directs requests to Hadoop masters which coordinate nodes to perform mapping and reducing of data according to the algorithm implemented.
The document discusses analyzing the MovieLens dataset using a big data approach with Pig Hadoop. It introduces the dataset and discusses how big data is changing businesses by uncovering hidden insights. The main functionalities of the project are outlined, including analyzing aspects like movie ratings by year, gender, and age. The requirements, modules, and system design are then described. The modules involve loading the data into HDFS, analyzing it with MapReduce, storing results in HDFS, and reading results. The system design shows the data flowing from HDFS to MapReduce processing to end users. References are provided to learn more about related big data and Hadoop topics.
TASK-DECOMPOSITION BASED ANOMALY DETECTION OF MASSIVE AND HIGH-VOLATILITY SES...ijdpsjournal
This document summarizes a research paper that presents a task-decomposition based anomaly detection system for analyzing massive and highly volatile session data from the Science Information Network (SINET), Japan's academic backbone network. The system uses a master-worker design with dynamic task scheduling to process over 1 billion sessions per day. It discriminates incoming and outgoing traffic using GPU parallelization and generates histograms of traffic volumes over time. Long short-term memory (LSTM) neural networks detect anomalies like spikes in incoming traffic volumes. The experiment analyzed SINET data from February 27 to March 8, 2021, detecting some anomalies while processing 500-650 gigabytes of daily session 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.
Similar to Survey on load balancing and data skew mitigation in mapreduce applications (20)
Submission Deadline: 30th September 2022
Acceptance Notification: Within Three Days’ time period
Online Publication: Within 24 Hrs. time Period
Expected Date of Dispatch of Printed Journal: 5th October 2022
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...IAEME Publication
White layer thickness (WLT) formed and surface roughness in wire electric discharge turning (WEDT) of tungsten carbide composite has been made to model through response surface methodology (RSM). A Taguchi’s standard Design of experiments involving five input variables with three levels has been employed to establish a mathematical model between input parameters and responses. Percentage of cobalt content, spindle speed, Pulse on-time, wire feed and pulse off-time were changed during the experimental tests based on the Taguchi’s orthogonal array L27 (3^13). Analysis of variance (ANOVA) revealed that the mathematical models obtained can adequately describe performance within the parameters of the factors considered. There was a good agreement between the experimental and predicted values in this study.
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURSIAEME Publication
The study explores the reasons for a transgender to become entrepreneurs. In this study transgender entrepreneur was taken as independent variable and reasons to become as dependent variable. Data were collected through a structured questionnaire containing a five point Likert Scale. The study examined the data of 30 transgender entrepreneurs in Salem Municipal Corporation of Tamil Nadu State, India. Simple Random sampling technique was used. Garrett Ranking Technique (Percentile Position, Mean Scores) was used as the analysis for the present study to identify the top 13 stimulus factors for establishment of trans entrepreneurial venture. Economic advancement of a nation is governed upon the upshot of a resolute entrepreneurial doings. The conception of entrepreneurship has stretched and materialized to the socially deflated uncharted sections of transgender community. Presently transgenders have smashed their stereotypes and are making recent headlines of achievements in various fields of our Indian society. The trans-community is gradually being observed in a new light and has been trying to achieve prospective growth in entrepreneurship. The findings of the research revealed that the optimistic changes are taking place to change affirmative societal outlook of the transgender for entrepreneurial ventureship. It also laid emphasis on other transgenders to renovate their traditional living. The paper also highlights that legislators, supervisory body should endorse an impartial canons and reforms in Tamil Nadu Transgender Welfare Board Association.
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURSIAEME Publication
Since ages gender difference is always a debatable theme whether caused by nature, evolution or environment. The birth of a transgender is dreadful not only for the child but also for their parents. The pain of living in the wrong physique and treated as second class victimized citizen is outrageous and fully harboured with vicious baseless negative scruples. For so long, social exclusion had perpetuated inequality and deprivation experiencing ingrained malign stigma and besieged victims of crime or violence across their life spans. They are pushed into the murky way of life with a source of eternal disgust, bereft sexual potency and perennial fear. Although they are highly visible but very little is known about them. The common public needs to comprehend the ravaged arrogance on these insensitive souls and assist in integrating them into the mainstream by offering equal opportunity, treat with humanity and respect their dignity. Entrepreneurship in the current age is endorsing the gender fairness movement. Unstable careers and economic inadequacy had inclined one of the gender variant people called Transgender to become entrepreneurs. These tiny budding entrepreneurs resulted in economic transition by means of employment, free from the clutches of stereotype jobs, raised standard of living and handful of financial empowerment. Besides all these inhibitions, they were able to witness a platform for skill set development that ignited them to enter into entrepreneurial domain. This paper epitomizes skill sets involved in trans-entrepreneurs of Thoothukudi Municipal Corporation of Tamil Nadu State and is a groundbreaking determination to sightsee various skills incorporated and the impact on entrepreneurship.
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONSIAEME Publication
The banking and financial services industries are experiencing increased technology penetration. Among them, the banking industry has made technological advancements to better serve the general populace. The economy focused on transforming the banking sector's system into a cashless, paperless, and faceless one. The researcher wants to evaluate the user's intention for utilising a mobile banking application. The study also examines the variables affecting the user's behaviour intention when selecting specific applications for financial transactions. The researcher employed a well-structured questionnaire and a descriptive study methodology to gather the respondents' primary data utilising the snowball sampling technique. The study includes variables like performance expectations, effort expectations, social impact, enabling circumstances, and perceived risk. Each of the aforementioned variables has a major impact on how users utilise mobile banking applications. The outcome will assist the service provider in comprehending the user's history with mobile banking applications.
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONSIAEME Publication
Technology upgradation in banking sector took the economy to view that payment mode towards online transactions using mobile applications. This system enabled connectivity between banks, Merchant and user in a convenient mode. there are various applications used for online transactions such as Google pay, Paytm, freecharge, mobikiwi, oxygen, phonepe and so on and it also includes mobile banking applications. The study aimed at evaluating the predilection of the user in adopting digital transaction. The study is descriptive in nature. The researcher used random sample techniques to collect the data. The findings reveal that mobile applications differ with the quality of service rendered by Gpay and Phonepe. The researcher suggest the Phonepe application should focus on implementing the application should be user friendly interface and Gpay on motivating the users to feel the importance of request for money and modes of payments in the application.
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINOIAEME Publication
The prototype of a voice-based ATM for visually impaired using Arduino is to help people who are blind. This uses RFID cards which contain users fingerprint encrypted on it and interacts with the users through voice commands. ATM operates when sensor detects the presence of one person in the cabin. After scanning the RFID card, it will ask to select the mode like –normal or blind. User can select the respective mode through voice input, if blind mode is selected the balance check or cash withdraw can be done through voice input. Normal mode procedure is same as the existing ATM.
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...IAEME Publication
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends for conflict management as an integral part of effective human resource management.
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMYIAEME Publication
Our life journey, in general, is closely defined by the way we understand the meaning of why we coexist and deal with its challenges. As we develop the "inspiration economy", we could say that nearly all of the challenges we have faced are opportunities that help us to discover the rest of our journey. In this note paper, we explore how being faced with the opportunity of being a close carer for an aging parent with dementia brought intangible discoveries that changed our insight of the meaning of the rest of our life journey.
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...IAEME Publication
The main objective of this study is to analyze the impact of aspects of Organizational Culture on the Effectiveness of the Performance Management System (PMS) in the Health Care Organization at Thanjavur. Organizational Culture and PMS play a crucial role in present-day organizations in achieving their objectives. PMS needs employees’ cooperation to achieve its intended objectives. Employees' cooperation depends upon the organization’s culture. The present study uses exploratory research to examine the relationship between the Organization's culture and the Effectiveness of the Performance Management System. The study uses a Structured Questionnaire to collect the primary data. For this study, Thirty-six non-clinical employees were selected from twelve randomly selected Health Care organizations at Thanjavur. Thirty-two fully completed questionnaires were received.
Living in 21st century in itself reminds all of us the necessity of police and its administration. As more and more we are entering into the modern society and culture, the more we require the services of the so called ‘Khaki Worthy’ men i.e., the police personnel. Whether we talk of Indian police or the other nation’s police, they all have the same recognition as they have in India. But as already mentioned, their services and requirements are different after the like 26th November, 2008 incidents, where they without saving their own lives has sacrificed themselves without any hitch and without caring about their respective family members and wards. In other words, they are like our heroes and mentors who can guide us from the darkness of fear, militancy, corruption and other dark sides of life and so on. Now the question arises, if Gandhi would have been alive today, what would have been his reaction/opinion to the police and its functioning? Would he have some thing different in his mind now what he had been in his mind before the partition or would he be going to start some Satyagraha in the form of some improvement in the functioning of the police administration? Really these questions or rather night mares can come to any one’s mind, when there is too much confusion is prevailing in our minds, when there is too much corruption in the society and when the polices working is also in the questioning because of one or the other case throughout the India. It is matter of great concern that we have to thing over our administration and our practical approach because the police personals are also like us, they are part and parcel of our society and among one of us, so why we all are pin pointing towards them.
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...IAEME Publication
The goal of this study was to see how talent management affected employee retention in the selected IT organizations in Chennai. The fundamental issue was the difficulty to attract, hire, and retain talented personnel who perform well and the gap between supply and demand of talent acquisition and retaining them within the firms. The study's main goals were to determine the impact of talent management on employee retention in IT companies in Chennai, investigate talent management strategies that IT companies could use to improve talent acquisition, performance management, career planning and formulate retention strategies that the IT firms could use. The respondents were given a structured close-ended questionnaire with the 5 Point Likert Scale as part of the study's quantitative research design. The target population consisted of 289 IT professionals. The questionnaires were distributed and collected by the researcher directly. The Statistical Package for Social Sciences (SPSS) was used to collect and analyse the questionnaire responses. Hypotheses that were formulated for the various areas of the study were tested using a variety of statistical tests. The key findings of the study suggested that talent management had an impact on employee retention. The studies also found that there is a clear link between the implementation of talent management and retention measures. Management should provide enough training and development for employees, clarify job responsibilities, provide adequate remuneration packages, and recognise employees for exceptional performance.
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...IAEME Publication
Globally, Millions of dollars were spent by the organizations for employing skilled Information Technology (IT) professionals. It is costly to replace unskilled employees with IT professionals possessing technical skills and competencies that aid in interconnecting the business processes. The organization’s employment tactics were forced to alter by globalization along with technological innovations as they consistently diminish to remain lean, outsource to concentrate on core competencies along with restructuring/reallocate personnel to gather efficiency. As other jobs, organizations or professions have become reasonably more appropriate in a shifting employment landscape, the above alterations trigger both involuntary as well as voluntary turnover. The employee view on jobs is also afflicted by the COVID-19 pandemic along with the employee-driven labour market. So, having effective strategies is necessary to tackle the withdrawal rate of employees. By associating Emotional Intelligence (EI) along with Talent Management (TM) in the IT industry, the rise in attrition rate was analyzed in this study. Only 303 respondents were collected out of 350 participants to whom questionnaires were distributed. From the employees of IT organizations located in Bangalore (India), the data were congregated. A simple random sampling methodology was employed to congregate data as of the respondents. Generating the hypothesis along with testing is eventuated. The effect of EI and TM along with regression analysis between TM and EI was analyzed. The outcomes indicated that employee and Organizational Performance (OP) were elevated by effective EI along with TM.
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...IAEME Publication
By implementing talent management strategy, organizations would have the option to retain their skilled professionals while additionally working on their overall performance. It is the course of appropriately utilizing the ideal individuals, setting them up for future top positions, exploring and dealing with their performance, and holding them back from leaving the organization. It is employee performance that determines the success of every organization. The firm quickly obtains an upper hand over its rivals in the event that its employees having particular skills that cannot be duplicated by the competitors. Thus, firms are centred on creating successful talent management practices and processes to deal with the unique human resources. Firms are additionally endeavouring to keep their top/key staff since on the off chance that they leave; the whole store of information leaves the firm's hands. The study's objective was to determine the impact of talent management on organizational performance among the selected IT organizations in Chennai. The study recommends that talent management limitedly affects performance. On the off chance that this talent is appropriately management and implemented properly, organizations might benefit as much as possible from their maintained assets to support development and productivity, both monetarily and non-monetarily.
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...IAEME Publication
Banking regulations act of India, 1949 defines banking as “acceptance of deposits for the purpose of lending or investment from the public, repayment on demand or otherwise and withdrawable through cheques, drafts order or otherwise”, the major participants of the Indian financial system are commercial banks, the financial institution encompassing term lending institutions. Investments institutions, specialized financial institution and the state level development banks, non banking financial companies (NBFC) and other market intermediaries such has the stock brokers and money lenders are among the oldest of the certain variants of NBFC and the oldest market participants. The asset quality of banks is one of the most important indicators of their financial health. The Indian banking sector has been facing severe problems of increasing Non- Performing Assets (NPAs). The NPAs growth directly and indirectly affects the quality of assets and profitability of banks. It also shows the efficiency of banks credit risk management and the recovery effectiveness. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets that why is a double edge weapon. This paper outlines the concept of quality of bank loans of different types like Housing, Agriculture and MSME loans in state Haryana of selected public and private sector banks. This study is highlighting problems associated with the role of commercial bank in financing Small and Medium Scale Enterprises (SME). The overall objective of the research was to assess the effect of the financing provisions existing for the setting up and operations of MSMEs in the country and to generate recommendations for more robust financing mechanisms for successful operation of the MSMEs, in turn understanding the impact of MSME loans on financial institutions due to NPA. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of selected public sector and private sector banks and attempts to compare the NPA of Housing, Agriculture and MSME loans in state Haryana of public and private sector banks. The tools used in the study are average and Anova test and variance. The findings reveal that NPA is common problem for both public and private sector banks and is associated with all types of loans either that is housing loans, agriculture loans and loans to SMES. NPAs of both public and private sector banks show the increasing trend. In 2010-11 GNPA of public and private sector were at same level it was 2% but after 2010-11 it increased in many fold and at present there is GNPA in some more than 15%. It shows the dark area of Indian banking sector.
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...IAEME Publication
An experiment conducted in this study found that BaSO4 changed Nylon 6's mechanical properties. By changing the weight ratios, BaSO4 was used to make Nylon 6. This Researcher looked into how hard Nylon-6/BaSO4 composites are and how well they wear. Experiments were done based on Taguchi design L9. Nylon-6/BaSO4 composites can be tested for their hardness number using a Rockwell hardness testing apparatus. On Nylon/BaSO4, the wear behavior was measured by a wear monitor, pinon-disc friction by varying reinforcement, sliding speed, and sliding distance, and the microstructure of the crack surfaces was observed by SEM. This study provides significant contributions to ultimate strength by increasing BaSO4 content up to 16% in the composites, and sliding speed contributes 72.45% to the wear rate
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...IAEME Publication
The majority of the population in India lives in villages. The village is the back bone of the country. Village or rural industries play an important role in the national economy, particularly in the rural development. Developing the rural economy is one of the key indicators towards a country’s success. Whether it be the need to look after the welfare of the farmers or invest in rural infrastructure, Governments have to ensure that rural development isn’t compromised. The economic development of our country largely depends on the progress of rural areas and the standard of living of rural masses. Village or rural industries play an important role in the national economy, particularly in the rural development. Rural entrepreneurship is based on stimulating local entrepreneurial talent and the subsequent growth of indigenous enterprises. It recognizes opportunity in the rural areas and accelerates a unique blend of resources either inside or outside of agriculture. Rural entrepreneurship brings an economic value to the rural sector by creating new methods of production, new markets, new products and generate employment opportunities thereby ensuring continuous rural development. Social Entrepreneurship has the direct and primary objective of serving the society along with the earning profits. So, social entrepreneurship is different from the economic entrepreneurship as its basic objective is not to earn profits but for providing innovative solutions to meet the society needs which are not taken care by majority of the entrepreneurs as they are in the business for profit making as a sole objective. So, the Social Entrepreneurs have the huge growth potential particularly in the developing countries like India where we have huge societal disparities in terms of the financial positions of the population. Still 22 percent of the Indian population is below the poverty line and also there is disparity among the rural & urban population in terms of families living under BPL. 25.7 percent of the rural population & 13.7 percent of the urban population is under BPL which clearly shows the disparity of the poor people in the rural and urban areas. The need to develop social entrepreneurship in agriculture is dictated by a large number of social problems. Such problems include low living standards, unemployment, and social tension. The reasons that led to the emergence of the practice of social entrepreneurship are the above factors. The research problem lays upon disclosing the importance of role of social entrepreneurship in rural development of India. The paper the tendencies of social entrepreneurship in India, to present successful examples of such business for providing recommendations how to improve situation in rural areas in terms of social entrepreneurship development. Indian government has made some steps towards development of social enterprises, social entrepreneurship, and social in- novation, but a lot remains to be improved.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...IAEME Publication
Manufacturing industries have witnessed an outburst in productivity. For productivity improvement manufacturing industries are taking various initiatives by using lean tools and techniques. However, in different manufacturing industries, frugal approach is applied in product design and services as a tool for improvement. Frugal approach contributed to prove less is more and seems indirectly contributing to improve productivity. Hence, there is need to understand status of frugal approach application in manufacturing industries. All manufacturing industries are trying hard and putting continuous efforts for competitive existence. For productivity improvements, manufacturing industries are coming up with different effective and efficient solutions in manufacturing processes and operations. To overcome current challenges, manufacturing industries have started using frugal approach in product design and services. For this study, methodology adopted with both primary and secondary sources of data. For primary source interview and observation technique is used and for secondary source review has done based on available literatures in website, printed magazines, manual etc. An attempt has made for understanding application of frugal approach with the study of manufacturing industry project. Manufacturing industry selected for this project study is Mahindra and Mahindra Ltd. This paper will help researcher to find the connections between the two concepts productivity improvement and frugal approach. This paper will help to understand significance of frugal approach for productivity improvement in manufacturing industry. This will also help to understand current scenario of frugal approach in manufacturing industry. In manufacturing industries various process are involved to deliver the final product. In the process of converting input in to output through manufacturing process productivity plays very critical role. Hence this study will help to evolve status of frugal approach in productivity improvement programme. The notion of frugal can be viewed as an approach towards productivity improvement in manufacturing industries.
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENTIAEME Publication
In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away