Search Results - (( data distribution computer algorithm ) OR ( parallel distribution process algorithm ))
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Workflow optimization in distributed computing environment for stream-based data processing model / Saima Gulzar Ahmad
Published 2017“…Eventually, the workflow is executed. The second algorithm is a variation in first algorithm in which data parallelism is introduced in each partition. …”
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Parallel genetic algorithms for shortest path routing in high- performance computing / Mohd Erman Safawie Che Ibrahim
Published 2012“…This project focuses on step-up cluster computing and a parallel Genetic Algorithm. The objectives of this project to set-up Beowulf cluster computer to apply the Travelling Salesman Problem in parallel by using Genetic Algorithms and evaluate sequential algorithms and parallel algorithms by Genetic Algorithms. …”
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Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm
Published 2025“…Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout's distributed machine-learning environment. …”
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Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures
Published 2020“…The first step lies at the modelling of parallel applications running on heterogeneous parallel computers. …”
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Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing
Published 2013“…Researches on data Cloud Computing become the necessary trend in the distributed Cloud Computing system domain since the sources and application of the data are distributed and the scale of the applications enlarges quickly. …”
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Parallel guided image processing model for ficus deltoidea (Jack) moraceae varietal recognition
Published 2018“…Many computer scientists have suggested that the usage of parallel and distributed computing should be strongly considered as mandatory for handling computationally intensive programs. …”
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Book Section -
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MapReduce algorithm for weather dataset
Published 2018“…The purpose of the comparison is to investigate the capability of the proposed model in parallel processing. The comparison results shown that MapReduce Algorithm has produced 37%, 25% and 11% less compared to AWK in term of processing time for 10GB, 5GB and 1GB data, respectively. …”
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Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer
Published 2023“…Consequently, to handle these data, computer algorithms must adapt to their characteristics. …”
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Design and analysis of management platform based on financial big data
Published 2023“…In addition, a financial data management platform based on distributed Hadoop architecture is designed, which combines MapReduce framework with the fuzzy clustering algorithm and the local outlier factor (LOF) algorithm, and uses MapReduce to operate in parallel with the two algorithms, thus improving the performance of the algorithm and the accuracy of the algorithm, and helping to improve the operational efficiency of enterprise financial data processing. …”
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Parallel guided image processing model for ficus deltoidea (Jack) moraceae varietal recognition
Published 2018“…As the number of cores increases, the computational time taken by the parallel algorithm becomes less.…”
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Book Chapter -
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Managing Fragmented Database Replication for Mygrants Using Binary Vote Assignment on Cloud Quorum
Published 2014“…Replication in distributed database is the process of copying and maintaining database objects in multiple databases that make up a distributed database system. …”
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Pembinaan dan pelaksanaan algoritma selari bagi kaedah kelas TTHS dan TTKS dalam menyelesaikan persamaan parabolik pada sistem komputer selari ingatan teragih
Published 2004“…The compatibility of the parallel implementation of AGE on the distributed parallel computer system is also discussed. …”
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Generic DNA encoding design scheme to solve combinatorial problems
Published 2015“…However, DNA computing can solve the problem in linear time since the parallel processing power of DNA computing is able to generate a solution in a single process. …”
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Extreme air pollutant data analysis using classical and Bayesian approaches
Published 2015“…MTM algorithm is an extension of MH algorithm, designed to improve the convergence of MH algorithm by performing parallel computation. …”
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Managing Heterogeneous Database Replication Using Persistence Layer Synchronous Replication (PLSR)
Published 2013“…It achieves faster time execution and cost minimization than that other replication processes. This algorithm also introduces a multi thread based persistence layer, which supports early binding and parallel connection to the servers. …”
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Parallel system for abnormal cell growth prediction based on fast numerical simulation
Published 2010“…The development of the prediction system is the combinations of the parallel algorithms, open source software on Linux environment and distributed multiprocessor system. …”
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Parallelizing web scraping to improve performance and scalability
Published 2023“…It employs a message queuing algorithm to divide scraping tasks into smaller units and utilizes multiple worker nodes for concurrent web data extraction. …”
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Load-Balancing Models for Scheduling Divisible Load on Large Scale Data Grids
Published 2009“…In many data grid applications, data can be decomposed into multiple independent sub datasets and distributed for parallel execution. …”
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K Nearest Neighbor Joins And Mapreduce Process Enforcement For The Cluster Of Data Sets In Bigdata
Published 2018“…K Nearest Neighbor Joins (KNN join) are regarded as highly primitive and expensive operations in the data mining.The efficient use of KNN join has proven good results in finding the objects from two data sets prevailed in the huge databases.This has been achieved with the combination of K-Nearest Neighbor query and join operation to find the distinct objects from different data sets.MapReduce is a newly introduced program with the combination of Map Procedure method and Reduce Method widely used in BigData.MapReduce is enriched with parallel distributed algorithm to find the results on a cluster of data sets in BigData.In this paper,the combination of KNN join and MapReduce methods are utilized on the cluster of data sets in BigData for knowledge discovery.Exploring the pinpoint data from huge data sets stored in Big Data demands the distributed large scale data processing.The present research paper is focusing on generic steps for KNN joins exploration operations on MapReduce.The operations of KNN Join are targeted to perform the data partitioning and data pre-processing and necessary calculations.By utilizing the combination of KNN joins with MapReduce methods on BigData data sets will demonstrate a solution for complex computational analysis. …”
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