Results 61 to 70 of about 12,223 (205)
International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.With the deepening of industrial digital transformation, equipment fault diagnosis faces challenges including low utilization of unstructured data, weak cross‐modal semantic association, and lagging knowledge updates. Traditional methods relying on artificial rules and static knowledge bases struggle to effectively integrate multimodal information such
Yu Fang, Richard Murray
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MOON: MapReduce On Opportunistic eNvironments [PDF]
, 2009 —MapReduce offers a flexible programming model for processing and generating large data sets on dedicated resources, where only a small fraction of such resources are every unavailable at any given time. In contrast, when MapReduce is run on volunteer
Feng, Wu-chun +5 more
core +1 more source
Dache: A Data Aware Caching for Big-Data Applications Using the MapReduce Framework
Tsinghua Science and Technology, 2014 The buzz-word big-data refers to the large-scale distributed data processing applications that operate on exceptionally large amounts of data. Google’s MapReduce and Apache’s Hadoop, its open-source implementation, are the defacto software systems for ...
Yaxiong Zhao, Jie Wu, Cong Liu
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A-MapReduce: Executing Wide Search via Agentic MapReduce
CoRR33 ...
Mingju Chen +4 more
openaire +2 more sources
Canadian Journal of Statistics, Volume 53, Issue 4, December 2025.Abstract Modern longitudinal data from wearable devices consist of biological signals at high‐frequency time points. Distributed statistical methods have emerged as a powerful tool to overcome the computational burden of estimation and inference with large data, but methodology for distributed functional regression remains limited.
Cole Manschot, Emily C. Hector
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IEEE Access, 2018 In this paper, we discuss some challenges regarding the Hadoop framework. One of the main ones is the computing performance of Hadoop MapReduce jobs in terms of CPU, memory, and hard disk I/O. The networking side of a Hadoop cluster is another challenge,
Ali Khaleel, Hamed Al-Raweshidy
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A Systematic Overview of Caching Mechanisms to Improve Hadoop Performance
Concurrency and Computation: Practice and Experience, Volume 37, Issue 25-26, 30 November 2025.ABSTRACT In today's distributed computing environments, the rapid generation of large‐scale data from diverse sources poses significant challenges in terms of storage, management, and processing, particularly for traditional relational databases. Hadoop has emerged as a widely adopted framework for handling such data through parallel processing across ...
Rana Ghazali, Douglas G. Down
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MapReduce: within, outside, or on the side-by-side with parallel DBMSs?
Труды Института системного программирования РАН, 2018 The approaches of use of MapReduce technology together with analytical DBMSs are discussed. The paper considers approaches where one implements MapReduce within a kernel of a parallel DBMS, where MapReduce serves as a communication infrastructure of a ...
Sergey D. Kuznetsov.
doaj
Big data: modern approaches to storage and analysis
Труды Института системного программирования РАН, 2018 Big data challenged traditional storage and analysis systems in several new ways. In this paper we try to figure out how to overcome this challenges, why it's not possible to make it efficiently and describe three modern approaches to big data handling ...
Pavel Klemenkov, Sergey Kuznetsov
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A Pattern‐Referencing Model for Hourly Temperature Forecasting in Coastal Regions
Meteorological Applications, Volume 32, Issue 6, November/December 2025.A novel pattern‐referencing model forecasts hourly temperatures in Taiwan's southwestern coastal region. It robustly handles missing data, achieving high accuracy (MAE 0.323°C–0.539°C, RMSE 0.450°C–0.807°C) even during extreme weather, offering a practical solution for real‐time decision‐making.
Nan‐Jing Wu, Fan‐Hua Nan
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