Results 41 to 50 of about 9,135 (230)

Massive power device condition monitoring data feature extraction and clustering analysis using MapReduce and graph model

CES Transactions on Electrical Machines and Systems, 2019
Effective storage, processing and analyzing of power device condition monitoring data faces enormous challenges. A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun ...
Hongtao Shen, Peng Tao, Pei Zhao, Hao Ma
doaj   +1 more source

Clustering‐based recommendation method with enhanced grasshopper optimisation algorithm

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract In the era of big data, personalised recommendation systems are essential for enhancing user engagement and driving business growth. However, traditional recommendation algorithms, such as collaborative filtering, face significant challenges due to data sparsity, algorithm scalability, and the difficulty of adapting to dynamic user preferences.
Zihao Zhao, Yingchun Xia, Wenjun Xu, Hui Yu, Shuai Yang, Cheng Chen, Xiaohui Yuan, Xiaobo Zhou, Qingyong Wang, Lichuan Gu   +9 more
wiley   +1 more source

Secure Grouping and Aggregation with MapReduce [PDF]

Proceedings of the 15th International Joint Conference on e-Business and Telecommunications, 2018
MapReduce programming paradigm allows to process big data sets in parallel on a large cluster. We focus on a scenario where the data owner outsources her data on an honest-but-curious server. Our aim is to evaluate grouping and aggregation with SUM, COUNT, AVG, MIN, and MAX operations for an authorized user. For each of these five operations, we assume
Ciucanu, Radu, Giraud, Matthieu, Lafourcade, Pascal, Ye, Lihua   +3 more
openaire   +4 more sources

An Alternative C++ based HPC system for Hadoop MapReduce [PDF]

arXiv, 2020
MapReduce is a technique used to vastly improve distributed processing of data and can massively speed up computation. Hadoop and its MapReduce relies on JVM and Java which is expensive on memory. High Performance Computing based MapReduce framework could be used that can perform more memory-efficiently and faster than the standard MapReduce.
arxiv  

Interpretable decision-tree induction in a big data parallel framework

International Journal of Applied Mathematics and Computer Science, 2017
When running data-mining algorithms on big data platforms, a parallel, distributed framework, such asMAPREDUCE, may be used. However, in a parallel framework, each individual model fits the data allocated to its own computing node without necessarily ...
Weinberg Abraham Itzhak, Last Mark
doaj   +1 more source

D 3 -MapReduce: Towards MapReduce for Distributed and Dynamic Data Sets [PDF]

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), 2015
International ...
He, Haiwu, Simonet, Anthony, Anjos, Julio, Saray, José-Francisco, Fedak, Gilles, Tang, Bing, Lu, Lu, Shi, Xuanhua, Jin, Hai, Moca, Mircea, Silaghi, Gheorghe, Cosmin, Ben Cheikh, Asma, Abbes, Heithem   +12 more
openaire   +5 more sources

Hadoop and MapReduce [PDF]

Journal of the Korean Data and Information Science Society, 2013
As the need for large-scale data analysis is rapidly increasing, Hadoop, or the platform that realizes large-scale data processing, and MapReduce, or the internal computational model of Hadoop, are receiving great attention. This paper reviews the basic concepts of Hadoop and MapReduce necessary for data analysts who are familiar with statistical ...
Joong-Ho Won, Jeong-Hyeok Park, Da Hyun Kang, Sang-Yeol Lee   +3 more
openaire   +2 more sources

Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review

Progress in Photovoltaics: Research and Applications, Volume 33, Issue 3, Page 381-409, March 2025.
This article examines the autonomous monitoring and analysis of PV plants, highlighting key barriers and research directions for smart monitoring of PV systems. The discussed topics cover UAV choice and related standards, camera varieties, AI applications in monitoring, datasets, and data enhancement methods, as well as data exchange between UAVs and ...
M. Aghaei, M. Kolahi, A. Nedaei, N. S. Venkatesh, S. M. Esmailifar, A. M. Moradi Sizkouhi, A. Aghamohammadi, A. K. V. Oliveira, A. Eskandari, P. Parvin, J. Milimonfared, V. Sugumaran, R. Rüther   +12 more
wiley   +1 more source

SparkGrid: Blockchain Assisted Secure Query Scheduling and Dynamic Risk Assessment for Live Migration of Services in Apache Spark‐Based Grid Environment

IET Blockchain, Volume 5, Issue 1, January/December 2025.
Grid computing is an emerging technology that enabled the heterogeneous collection of data and provisioning of services to the users. Due to the high amount of incoming heterogeneous request, grid computing needs an efficient scheduling to reduce execution time and satisfy service level agreement (SLA) and quality of service (QoS) requirements.
Gangasandra Mahadevaiah Kiran, Narasimaiah Nalini   +1 more
wiley   +1 more source

Big Data Analytics for Healthcare Industry: Impact, Applications, and Tools

Big Data Mining and Analytics, 2019
In recent years, huge amounts of structured, unstructured, and semi-structured data have been generated by various institutions around the world and, collectively, this heterogeneous data is referred to as big data.
Sunil Kumar, Maninder Singh
doaj   +1 more source