Results 31 to 40 of about 36,780 (203)
Energy Efficient Scheduling of MapReduce Jobs
, 2014 MapReduce is emerged as a prominent programming model for data-intensive computation. In this work, we study power-aware MapReduce scheduling in the speed scaling setting first introduced by Yao et al. [FOCS 1995].
Bampis, Evripidis +5 more
core +4 more sources
Spatial hotspot detection using polygon propagation
International Journal of Digital Earth, 2019 Spatial scan statistics is one of the most important models in order to detect high activity or hotspots in real world applications such as epidemiology, public health, astronomy and criminology applications on geographic data. Traditional scan statistic
Satya Katragadda +2 more
doaj +1 more source
An alternative C++-based HPC system for Hadoop MapReduce
Open Computer Science, 2022 MapReduce (MR) is a technique used to improve distributed data processing vastly and can massively speed up computation. Hadoop and MR rely on memory-intensive JVM and Java. A MR framework based on High-Performance Computing (HPC) could be used, which is
Srinivasakumar Vignesh +3 more
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H-word: Supporting job scheduling in Hadoop with workload-driven data redistribution [PDF]
, 2016 The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44039-2_21Today’s distributed data processing systems typically follow a query shipping approach and exploit data locality for reducing network traffic.
Abelló Gamazo, Alberto +3 more
core +1 more source
Evaluating MapReduce for seismic data processing using a practical application
Tongxin xuebao, 2012 Huge amounts of seismic data undergo complex iterative processing in the oil industry to get knowledge of the earth’s subsurface structure to detect where oil can be found and recovered.To evaluate the suitability of MapReduce for seismic processing ...
Chang-hai ZHAO +4 more
doaj +2 more sources
A Distributed Approach for High-Dimensionality Heterogeneous Data Reduction
IEEE Access, 2019 The recent explosion of data size in number of records and attributes has triggered the development of a number of Big Data analytics as well as parallel data processing methods and algorithms.
Rania Mkhinini Gahar +3 more
doaj +1 more source
Automatically Leveraging MapReduce Frameworks for Data-Intensive Applications
, 2018 MapReduce is a popular programming paradigm for developing large-scale, data-intensive computation. Many frameworks that implement this paradigm have recently been developed.
Cheung Alvin +2 more
core +1 more source
Enhanced Failure Detection Mechanism in MapReduce [PDF]
, 2012 The popularity of MapReduce programming model has increased interest in the research community for its improvement. Among the other directions, the point of fault tolerance, concretely the failure detection issue seems to be a crucial one, but that until
Antoniu, Gabriel +2 more
core +4 more sources
A Bibliometric Analysis of Process Mining
WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 2, June 2026.Process mining studies with numbers. ABSTRACT Process mining (PM) has emerged as a pivotal discipline in data science, bridging traditional process analysis with data‐driven techniques to extract actionable insights from event logs. This study conducts a comprehensive bibliometric analysis of 1764 peer‐reviewed articles from the Web of Science database
Seyfullah Tokumaci +2 more
wiley +1 more source
Applying MapReduce to Conformance Checking
Труды Института системного программирования РАН, 2018 Process mining is a relatively new research field, offering methods of business processes analysis and improvement, which are based on studying their execution history (event logs).
I. S. Shugurov, A. A. Mitsyuk
doaj +1 more source