Results 61 to 70 of about 9,555 (241)

Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework

ISPRS International Journal of Geo-Information, 2019
The Cellular Automata Markov model combines the cellular automata (CA) model’s ability to simulate the spatial variation of complex systems and the long-term prediction of the Markov model.
Junfeng Kang, Lei Fang, Shuang Li, Xiangrong Wang   +3 more
doaj   +1 more source

Aspect-based classification of product reviews using Hadoop framework

Cogent Engineering, 2020
The advancement of e-commerce along with the quick development of product review discussion in the most recent decade, an enormous measure of sentiment data or reviews are produced which made it practically difficult for a customer to take an educated ...
Anisha P. Rodrigues, Niranjan N. Chiplunkar, Roshan Fernandes   +2 more
doaj   +1 more source

Exploring the effects of RNNs and deep learning frameworks on real‐time, lightweight, adaptive time series anomaly detection

Concurrency and Computation: Practice and Experience, Volume 36, Issue 28, 25 December 2024.
Summary Real‐time, lightweight, adaptive time series anomaly detection is increasingly critical in cybersecurity, industrial control, finance, healthcare, and many other domains due to its capability to promptly process time series and detect anomalies without requiring extensive computation resources.
Ming‐Chang Lee, Jia‐Chun Lin, Sokratis Katsikas   +2 more
wiley   +1 more source

Measuring the Optimality of Hadoop Optimization [PDF]

arXiv, 2013
In recent years, much research has focused on how to optimize Hadoop jobs. Their approaches are diverse, ranging from improving HDFS and Hadoop job scheduler to optimizing parameters in Hadoop configurations. Despite their success in improving the performance of Hadoop jobs, however, very little is known about the limit of their optimization ...
arxiv  

Review of the opportunities and challenges to accelerate mass‐scale application of smart grids with large‐language models

IET Smart Grid, Volume 7, Issue 6, Page 737-759, December 2024.
The paper reviews the data‐empowered smart grids and proposes opportunities and future directions for adopting LLMs to accelerate the mass‐scale application of Smart Grids. Abstract Smart grids represent a paradigm shift in the electricity industry, moving from traditional one‐way systems to more dynamic, interconnected networks.
Heng Shi, Lurui Fang, Xiaoyang Chen, Chenghong Gu, Kang Ma, Xinsong Zhang, Zhong Zhang, Juping Gu, Eng Gee Lim   +8 more
wiley   +1 more source

HEAP: An Efficient and Fault-Tolerant Authentication and Key Exchange Protocol for Hadoop-Assisted Big Data Platform

IEEE Access, 2018
Hadoop framework has been evolved to manage big data in cloud. Hadoop distributed file system and MapReduce, the vital components of this framework, provide scalable and fault-tolerant big data storage and processing services at a lower cost.
Durbadal Chattaraj, Monalisa Sarma, Ashok Kumar Das, Neeraj Kumar, Joel J. P. C. Rodrigues, Youngho Park   +5 more
doaj   +1 more source

Reflecting on a Decade of Evolution: MapReduce‐Based Advances in Partitioning‐Based, Hierarchical‐Based, and Density‐Based Clustering (2013–2023)

WIREs Data Mining and Knowledge Discovery, Volume 14, Issue 6, November/December 2024.
Visualizing a Decade of MapReduce‐Based Clustering Research: A Bibliometric Review (2013–2023). Leveraging digital text‐mining and data analysis tools, this study assesses the evolution, impact, and challenges of MapReduce‐enabled partitioning, hierarchical, and density clustering algorithms. Key findings include trends in scholarly output, influential
Tanvir Habib Sardar
wiley   +1 more source

Empirical Analysis of Recent Advances, Characteristics and Challenges of Big Data [PDF]

EAI Endorsed Transactions on Scalable Information Systems, 2019
Here in this study, we provide an empirical analysis of recent advances, characteristic and challenges of big data. Initially, we acquaint the readers with the general background, history, and characteristics of big data including volume, velocity, value
Burhanullah Khattak, Aurangzeb Khan, Khairullah Khan, Wahab Khan, Muhammad Kamran, Muhammad Fahad   +5 more
doaj   +1 more source

Survey on Improved Scheduling in Hadoop MapReduce in Cloud Environments [PDF]

arXiv, 2012
Cloud Computing is emerging as a new computational paradigm shift. Hadoop-MapReduce has become a powerful Computation Model for processing large data on distributed commodity hardware clusters such as Clouds. In all Hadoop implementations, the default FIFO scheduler is available where jobs are scheduled in FIFO order with support for other priority ...
arxiv  

Hadoop MapReduce scheduling paradigms [PDF]

2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2017
Apache Hadoop is one of the most prominent and early technologies for handling big data. Different scheduling algorithms within the framework of Apache Hadoop were developed in the last decade. In this paper, we attempt to provide a comprehensive overview over the different paradigms for scheduling in Apache Hadoop.
Johannessen, Roger, Yazidi, Anis, Feng, Boning   +2 more
openaire   +3 more sources