Relevant bibliographies by topics / MAPREDUCE FRAMEWORKS

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Academic literature on the topic 'MAPREDUCE FRAMEWORKS'

Author: Grafiati
Published: 11 September 2023
Last updated: 19 July 2025

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Journal articles on the topic "MAPREDUCE FRAMEWORKS"

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Ajibade Lukuman Saheed, Abu Bakar Kamalrulnizam, Ahmed Aliyu, and Tasneem Darwish. "Latency-aware Straggler Mitigation Strategy in Hadoop MapReduce Framework: A Review." Systematic Literature Review and Meta-Analysis Journal 2, no. 2 (2021): 53–60. http://dx.doi.org/10.54480/slrm.v2i2.19.

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Processing huge and complex data to obtain useful information is challenging, even though several big data processing frameworks have been proposed and further enhanced. One of the prominent big data processing frameworks is MapReduce. The main concept of MapReduce framework relies on distributed and parallel processing. However, MapReduce framework is facing serious performance degradations due to the slow execution of certain tasks type called stragglers. Failing to handle stragglers causes delay and affects the overall job execution time. Meanwhile, several straggler reduction techniques ha
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Utami, Firmania Dwi, and Femi Dwi Astuti. "Comparison of Hadoop Mapreduce and Apache Spark in Big Data Processing with Hgrid247-DE." Journal of Applied Informatics and Computing 8, no. 2 (2024): 390–99. https://doi.org/10.30871/jaic.v8i2.8557.

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In today’s rapidly evolving information technology landscape, managing and analyzing big data has become one of the most significant challenges. This paper explores the implementation of two major frameworks for big data processing: Hadoop MapReduce and Apache Spark. Both frameworks were tested in three scenarios sorting, summarizing, and grouping using HGrid247-DE as the primary tool for data processing. A diverse set of datasets sourced from Kaggle, ranging in size from 3 MB to 260 MB, was employed to evaluate the performance of each framework. The findings reveal that Apache Spark generally
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Shailin Saraiya. "Technical Evolution and Performance Analysis of MapReduce in Modern Distributed Systems." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 29–35. https://doi.org/10.32628/cseit25111206.

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MapReduce has emerged as a cornerstone technology in the big data ecosystem, fundamentally transforming how organizations process and analyze massive datasets. This article provides a detailed examination of MapReduce's architecture, exploring its evolution from Google's original implementation to its current role in modern distributed computing systems. This article classifies into the three key phases of MapReduce—Map, Shuffle, Sort, and Reduce—analyzing how each contributes to efficient parallel data processing. This article demonstrates MapReduce's versatility and impact on real-world appl
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Darapaneni, Chandra Sekhar, Bobba Basaveswara Rao, Boggavarapu Bhanu Venkata Satya Vara Prasad, and Suneetha Bulla. "An Analytical Performance Evaluation of MapReduce Model Using Transient Queuing Model." Advances in Modelling and Analysis B 64, no. 1-4 (2021): 46–53. http://dx.doi.org/10.18280/ama_b.641-407.

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Today the MapReduce frameworks become the standard distributed computing mechanisms to store, process, analyze, query and transform the Bigdata. While processing the Bigdata, evaluating the performance of the MapReduce framework is essential, to understand the process dependencies and to tune the hyper-parameters. Unfortunately, the scope of the MapReduce framework in-built functions is limited to evaluate the performance till some extent. A reliable analytical performance model is required in this area to evaluate the performance of the MapReduce frameworks. The main objective of this paper i
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Wei, Peng. "Analysis of Aliyun-based serverless on MapReduce efficiency." Applied and Computational Engineering 88, no. 1 (2024): 61–68. http://dx.doi.org/10.54254/2755-2721/88/20241499.

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In the context of the current era of big data, traditional Hadoop and cluster-based MapReduce frameworks are unable to meet the demands of modern research. This paper presents a MapReduce framework based on the AliCloud Serverless platform, which has been developed with the objective of optimizing word frequency counting in large-scale English texts. Leveraging AliCloud's dynamic resource allocation and elastic scaling, we have created an efficient and flexible text data processing system. This paper details the design and implementation of the Map and Reduce phases and analyses the impact of
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Kang, Sol Ji, Sang Yeon Lee, and Keon Myung Lee. "Performance Comparison of OpenMP, MPI, and MapReduce in Practical Problems." Advances in Multimedia 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/575687.

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With problem size and complexity increasing, several parallel and distributed programming models and frameworks have been developed to efficiently handle such problems. This paper briefly reviews the parallel computing models and describes three widely recognized parallel programming frameworks: OpenMP, MPI, and MapReduce. OpenMP is the de facto standard for parallel programming on shared memory systems. MPI is the de facto industry standard for distributed memory systems. MapReduce framework has become the de facto standard for large scale data-intensive applications. Qualitative pros and con
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Srirama, Satish Narayana, Oleg Batrashev, Pelle Jakovits, and Eero Vainikko. "Scalability of Parallel Scientific Applications on the Cloud." Scientific Programming 19, no. 2-3 (2011): 91–105. http://dx.doi.org/10.1155/2011/361854.

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Cloud computing, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. To study the effects of moving parallel scientific applications onto the cloud, we deployed several benchmark applications like matrix–vector operations and NAS parallel benchmarks, and DOUG (Domain decomposition On Unstructured Grids) on the cloud. DOUG is an open source software package for parallel iterative solution of very large sparse systems of linear equations. The detailed analysis of DOUG on the cloud showed that parallel applications benefit
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Zhang, Yuhong. "MapReduce based on serverless platforms." Applied and Computational Engineering 40, no. 1 (2024): 168–73. http://dx.doi.org/10.54254/2755-2721/40/20230645.

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This study intends to investigate the application of the MapReduce (MR) framework based on serverless computing in big data processing. By combining the MapReduce model with serverless computing, efficient data processing is achieved. In this framework, the phases of Map task execution, reduce task execution, etc. are accomplished through stateless serverless functions, and data storage is realized with the help of cloud storage platforms (e.g., OSS). In this paper, the author introduces the basic theory of MR, the basic theory of serverless computing, describes the framework implementation pr
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Adornes, Daniel, Dalvan Griebler, Cleverson Ledur, and Luiz Gustavo Fernandes. "Coding Productivity in MapReduce Applications for Distributed and Shared Memory Architectures." International Journal of Software Engineering and Knowledge Engineering 25, no. 09n10 (2015): 1739–41. http://dx.doi.org/10.1142/s0218194015710096.

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MapReduce was originally proposed as a suitable and efficient approach for analyzing and processing large amounts of data. Since then, many researches contributed with MapReduce implementations for distributed and shared memory architectures. Nevertheless, different architectural levels require different optimization strategies in order to achieve high-performance computing. Such strategies in turn have caused very different MapReduce programming interfaces among these researches. This paper presents some research notes on coding productivity when developing MapReduce applications for distribu
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Senthilkumar, M., and P. Ilango. "A Survey on Job Scheduling in Big Data." Cybernetics and Information Technologies 16, no. 3 (2016): 35–51. http://dx.doi.org/10.1515/cait-2016-0033.

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Abstract Big Data Applications with Scheduling becomes an active research area in last three years. The Hadoop framework becomes very popular and most used frameworks in a distributed data processing. Hadoop is also open source software that allows the user to effectively utilize the hardware. Various scheduling algorithms of the MapReduce model using Hadoop vary with design and behavior, and are used for handling many issues like data locality, awareness with resource, energy and time. This paper gives the outline of job scheduling, classification of the scheduler, and comparison of different
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Dissertations / Theses on the topic "MAPREDUCE FRAMEWORKS"

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de, Souza Ferreira Tharso. "Improving Memory Hierarchy Performance on MapReduce Frameworks for Multi-Core Architectures." Doctoral thesis, Universitat Autònoma de Barcelona, 2013. http://hdl.handle.net/10803/129468.

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La necesidad de analizar grandes conjuntos de datos de diferentes tipos de aplicaciones ha popularizado el uso de modelos de programación simplicados como MapReduce. La popularidad actual se justifica por ser una abstracción útil para expresar procesamiento paralelo de datos y también ocultar eficazmente la sincronización de datos, tolerancia a fallos y la gestión de balanceo de carga para el desarrollador de la aplicación. Frameworks MapReduce también han sido adaptados a los sistema multi-core y de memoria compartida. Estos frameworks proponen que cada core de una CPU ejecute una tarea Map
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Kumaraswamy, Ravindranathan Krishnaraj. "Exploiting Heterogeneity in Distributed Software Frameworks." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/64423.

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The objective of this thesis is to address the challenges faced in sustaining efficient, high-performance and scalable Distributed Software Frameworks (DSFs), such as MapReduce, Hadoop, Dryad, and Pregel, for supporting data-intensive scientific and enterprise applications on emerging heterogeneous compute, storage and network infrastructure. Large DSF deployments in the cloud continue to grow both in size and number, given DSFs are cost-effective and easy to deploy. DSFs are becoming heterogeneous with the use of advanced hardware technologies and due to regular upgrades to the system. For in
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Venumuddala, Ramu Reddy. "Distributed Frameworks Towards Building an Open Data Architecture." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc801911/.

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Data is everywhere. The current Technological advancements in Digital, Social media and the ease at which the availability of different application services to interact with variety of systems are causing to generate tremendous volumes of data. Due to such varied services, Data format is now not restricted to only structure type like text but can generate unstructured content like social media data, videos and images etc. The generated Data is of no use unless been stored and analyzed to derive some Value. Traditional Database systems comes with limitations on the type of data format schema, a
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Peddi, Sri Vijay Bharat. "Cloud Computing Frameworks for Food Recognition from Images." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32450.

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Distributed cloud computing, when integrated with smartphone capabilities, contribute to building an efficient multimedia e-health application for mobile devices. Unfortunately, mobile devices alone do not possess the ability to run complex machine learning algorithms, which require large amounts of graphic processing and computational power. Therefore, offloading the computationally intensive part to the cloud, reduces the overhead on the mobile device. In this thesis, we introduce two such distributed cloud computing models, which implement machine learning algorithms in the cloud in paralle
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Elteir, Marwa Khamis. "A MapReduce Framework for Heterogeneous Computing Architectures." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28786.

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Nowadays, an increasing number of computational systems are equipped with heterogeneous compute resources, i.e., following different architecture. This applies to the level of a single chip, a single node and even supercomputers and large-scale clusters. With its impressive price-to-performance ratio as well as power efficiently compared to traditional multicore processors, graphics processing units (GPUs) has become an integrated part of these systems. GPUs deliver high peak performance; however efficiently exploiting their computational power requires the exploration of a multi-dimensional s
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Alkan, Sertan. "A Distributed Graph Mining Framework Based On Mapreduce." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12611588/index.pdf.

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The frequent patterns hidden in a graph can reveal crucial information about the network the graph represents. Existing techniques to mine the frequent subgraphs in a graph database generally rely on the premise that the data can be fit into main memory of the device that the computation takes place. Even though there are some algorithms that are designed using highly optimized methods to some extent, many lack the solution to the problem of scalability. In this thesis work, our aim is to find and enumerate the subgraphs that are at least as frequent as the designated threshold in a given grap
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Wang, Yongzhi. "Constructing Secure MapReduce Framework in Cloud-based Environment." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2238.

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MapReduce, a parallel computing paradigm, has been gaining popularity in recent years as cloud vendors offer MapReduce computation services on their public clouds. However, companies are still reluctant to move their computations to the public cloud due to the following reason: In the current business model, the entire MapReduce cluster is deployed on the public cloud. If the public cloud is not properly protected, the integrity and the confidentiality of MapReduce applications can be compromised by attacks inside or outside of the public cloud. From the result integrity’s perspective, if any
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Zhang, Yue. "A Workload Balanced MapReduce Framework on GPU Platforms." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1450180042.

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Raja, Anitha. "A Coordination Framework for Deploying Hadoop MapReduce Jobs on Hadoop Cluster." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-196951.

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Apache Hadoop is an open source framework that delivers reliable, scalable, and distributed computing. Hadoop services are provided for distributed data storage, data processing, data access, and security. MapReduce is the heart of the Hadoop framework and was designed to process vast amounts of data distributed over a large number of nodes. MapReduce has been used extensively to process structured and unstructured data in diverse fields such as e-commerce, web search, social networks, and scientific computation. Understanding the characteristics of Hadoop MapReduce workloads is the key to ach
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Lakkimsetti, Praveen Kumar. "A framework for automatic optimization of MapReduce programs based on job parameter configurations." Kansas State University, 2011. http://hdl.handle.net/2097/12011.

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Master of Science<br>Department of Computing and Information Sciences<br>Mitchell L. Neilsen<br>Recently, cost-effective and timely processing of large datasets has been playing an important role in the success of many enterprises and the scientific computing community. Two promising trends ensure that applications will be able to deal with ever increasing data volumes: first, the emergence of cloud computing, which provides transparent access to a large number of processing, storage and networking resources; and second, the development of the MapReduce programming model, which provides a high
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Book chapters on the topic "MAPREDUCE FRAMEWORKS"

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Singh, Jaspreet, S. N. Panda, and Rajesh Kaushal. "Performance Evaluation of Big Data Frameworks: MapReduce and Spark." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5903-2_167.

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Dhanani, Jenish, Rupa Mehta, Dipti Rana, and Bharat Tidke. "Back-Propagated Neural Network on MapReduce Frameworks: A Survey." In Smart Innovations in Communication and Computational Sciences. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2414-7_35.

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Noh, Hyunho, and Jun-Ki Min. "An Efficient Data Access Method Exploiting Quadtrees on MapReduce Frameworks." In Database Systems for Advanced Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40270-8_8.

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Salto, Carolina, Gabriela Minetti, Enrique Alba, and Gabriel Luque. "Developing Genetic Algorithms Using Different MapReduce Frameworks: MPI vs. Hadoop." In Advances in Artificial Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00374-6_25.

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Reinders, James, Ben Ashbaugh, James Brodman, Michael Kinsner, John Pennycook, and Xinmin Tian. "Common Parallel Patterns." In Data Parallel C++. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5574-2_14.

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Abstract When we are at our best as programmers, we recognize patterns in our work and apply techniques that are time proven to be the best solution. Parallel programming is no different, and it would be a serious mistake not to study the patterns that have proven to be useful in this space. Consider the MapReduce frameworks adopted for Big Data applications; their success stems largely from being based on two simple yet effective parallel patterns—map and reduce.
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Xu, Huanle, Ronghai Yang, Zhibo Yang, and Wing Cheong Lau. "Solving Large Graph Problems in MapReduce-Like Frameworks via Optimized Parameter Configuration." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27122-4_36.

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Jeyaraj, Rathinaraja, Ganeshkumar Pugalendhi, and Anand Paul. "Hadoop Framework." In Big Data with Hadoop MapReduce. Apple Academic Press, 2020. http://dx.doi.org/10.1201/9780429321733-2.

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Suryawanshi, Sahiba, and Praveen Kaushik. "Efficient MapReduce Framework Using Summation." In Data, Engineering and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6351-1_1.

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Cho, Kyung Soo, Ji Yeon Lim, Jae Yeol Yoon, Young Hee Kim, Seung Kwan Kim, and Ung Mo Kim. "Opinion Mining in MapReduce Framework." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22365-5_7.

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Liu, Xiufeng, Christian Thomsen, and Torben Bach Pedersen. "The ETLMR MapReduce-Based ETL Framework." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22351-8_48.

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Conference papers on the topic "MAPREDUCE FRAMEWORKS"

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Lee, Haejoon, Minseo Kang, Sun-Bum Youn, Jae-Gil Lee, and YongChul Kwon. "An Experimental Comparison of Iterative MapReduce Frameworks." In CIKM'16: ACM Conference on Information and Knowledge Management. ACM, 2016. http://dx.doi.org/10.1145/2983323.2983647.

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Wang, Haoyu, Haiying Shen, Charles Reiss, Arnim Jain, and Yunqiao Zhang. "Improved Intermediate Data Management for MapReduce Frameworks." In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2020. http://dx.doi.org/10.1109/ipdps47924.2020.00062.

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Hsaini, Sara, Salma Azzouzi, and My El Hassan Charaf. "A Secure Testing Based Approach for Mapreduce Frameworks." In 2018 International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS). IEEE, 2018. http://dx.doi.org/10.1109/icecocs.2018.8610596.

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Jakovits, Pelle, and Satish Narayana Srirama. "Evaluating MapReduce frameworks for iterative Scientific Computing applications." In 2014 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2014. http://dx.doi.org/10.1109/hpcsim.2014.6903690.

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Haque, Ahsanul, and Latifur Khan. "MapReduce Based Frameworks for Classifying Evolving Data Stream." In 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). IEEE, 2013. http://dx.doi.org/10.1109/icdmw.2013.145.

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Ahmad, Maaz Bin Safeer, and Alvin Cheung. "Automatically Leveraging MapReduce Frameworks for Data-Intensive Applications." In SIGMOD/PODS '18: International Conference on Management of Data. ACM, 2018. http://dx.doi.org/10.1145/3183713.3196891.

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Trong-Tuan Vu and Fabrice Huet. "A Lightweight Continuous Jobs Mechanism for MapReduce Frameworks." In 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, 2013. http://dx.doi.org/10.1109/ccgrid.2013.36.

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Ghit, Bogdan, and Dick Epema. "Tyrex: Size-Based Resource Allocation in MapReduce Frameworks." In 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, 2016. http://dx.doi.org/10.1109/ccgrid.2016.82.

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Rivas-Gomez, Sergio, Stefano Markidis, Erwin Laure, Keeran Brabazon, Oliver Perks, and Sai Narasimhamurthy. "Decoupled Strategy for Imbalanced Workloads in MapReduce Frameworks." In 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2018. http://dx.doi.org/10.1109/hpcc/smartcity/dss.2018.00153.

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Guo, Jia, and Gagan Agrawal. "Achieving Performance and Programmability for MapReduce(-Like) Frameworks." In 2018 IEEE 25th International Conference on High Performance Computing (HiPC). IEEE, 2018. http://dx.doi.org/10.1109/hipc.2018.00043.

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