Search Results - (( parallel distribution process algorithm ) OR ( data distribution system algorithm ))*

Parallel genetic algorithms for shortest path routing in high- performance computing / Mohd Erman Safawie Che Ibrahim by Che Ibrahim, Mohd Erman Safawie

“…High-performance computer is important because it is implemented in sectors where distributed parallel computing is needed to solve large scientific problems such as storing and processing large amounts of data. …”
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Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

“…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. …”

Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures by Koohi, Sina Zangbari

“…HPAs use parallel processors and assist in parallel execution of tasks with complex interdependency between data and operations. …”
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Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing by Elrasheed Ismail, Sultan

“…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 by Abdul Rahman, Prof. Dr. Mohd Nordin

“…The master processor responsibles for controlling the main system operations such as data partitioning, data allocation, and data merging which results from worker processors. …”
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Mapreduce algorithm for weather dataset by Khalid Adam, Ismail Hammad

“…This original dataset is stored in Hadoop Distributed File System. Next, MapReduce Algorithm is developed using Java programming. …”
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Design and analysis of management platform based on financial big data by Chen, Yuhua, Mustafa, Hasri, Zhang, Xuandong, Liu, Jing

“…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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MapReduce algorithm for weather dataset by Majid, Mazlina A., Romli, Awanis, Ahmad, Noraziah, Hammad, Khalid Adam Ismail

“…This original dataset is stored in Hadoop Distributed File System. Next, MapReduce Algorithm is developed using Java programming. …”
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Automatic generic process migration system in linux by Zarrabi, Amirreza

“…It introduces an intuitively appealing approach which facilitates dynamic load distribution, fault resilience, easy system administration, and data access locality. …”
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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 by Alias, Norma

“…The compatibility of the parallel implementation of AGE on the distributed parallel computer system is also discussed. …”
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Parallel guided image processing model for ficus deltoidea (Jack) moraceae varietal recognition by Ahmad Fakhri, Ab. Nasir, Ahmad Shahrizan, Abdul Ghani, M. Nordin, A. Rahman

“…As the number of cores increases, the computational time taken by the parallel algorithm becomes less.…”
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Managing Fragmented Database Replication for Mygrants Using Binary Vote Assignment on Cloud Quorum by Noraziah, Ahmad, Ainul Azila, Che Fauzi, Wan Maseri, Wan Mohd, Mohd Azhar, Mohd Amer, Herawan, Tutut

“…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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Parallel system for abnormal cell growth prediction based on fast numerical simulation by Alias, Norma, Islam, Md. Rajibul, Shahir, Rosdiana, Hamzah, Norhafizah, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Ludin, Eliana, Azami, Masrin

“…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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Managing Heterogeneous Database Replication Using Persistence Layer Synchronous Replication (PLSR) by Noraziah, Ahmad, Abdalla, Ahmed N., Beg, Abul Hashem

“…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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Parallelizing web scraping to improve performance and scalability by Na, Yi Chun

“…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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K Nearest Neighbor Joins And Mapreduce Process Enforcement For The Cluster Of Data Sets In Bigdata by Md Shah, Wahidah, Othman, Mohd Fairuz Iskandar, Hussian Hassan, Ali Abdul, Talib, Mohammed Saad, Mohammed, Ali Abdul Jabbar

“…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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Adaptive persistence layer for synchronous replication (PLSR) in heterogeneous system by Beg, Abul Hashem

“…Nowadays, in the grid community, distributed and clustering system, a lot of work has been focused on providing efficient and safe replication management services through designing of algorithms and systems. …”
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Load-Balancing Models for Scheduling Divisible Load on Large Scale Data Grids by Abduh Kaid, Monir Abdullah

“…In many data grid applications, data can be decomposed into multiple independent sub datasets and distributed for parallel execution. This property has been successfully employed using Divisible Load Theory (DLT) , which has been proven to be a powerful tool for modeling divisible load problems in large scale data grid. …”
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DC-based PV-powered home energy system by Sabry, Ahmad H.

“…The system can be managed via a central computer which attains the energy data via only one remote XBee RF wireless node which is processing the sensors measurement of the system components. …”
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Critical insight for MAPReduce optimization in Hadoop by Khan, Burhan ul Islam, Olanrewaju, Rashidah Funke, Altaf, Hunain, Shah, Asadullah

“…The predominant philosophy behind Hadoop optimization is the optimization of MapReduce, which is a dominant programming platform effective in bringing a=bout many functional enhancements as per scheduling algorithms developed and implemented. MapReduce has emerged as the most significant part of Hadoop system that establishes itself as a framework that can effectively simplify the overall complexity of running parallel data processes across the network of computing nodes. …”
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