MapReduce-Based Frequent Pattern Mining Framework with Multiple Item Support

Wang, Chen-Shu; Lin, Shiang-Lin; Chang, Jui-Yen

doi:10.1007/978-3-319-54430-4_7

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…Following that, the system dynamically organizes the intermediate key‐value pairs on the key and creates key‐value pairs (key2, list (value2)). The Reduce function processes each key2's matching value list 57 . Algorithm 2 indicates the steps of the presented method.…”

Section: System Model and Proposed Methodsmentioning

confidence: 99%

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A new service composition method in the cloud‐based Internet of things environment using a grey wolf optimization algorithm and MapReduce framework

Vakili,

Al‐Khafaji,

Darbandi

et al. 2024

Concurrency and Computation

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SummaryCloud computing is quickly becoming a common commercial model for software delivery and services, enabling companies to save maintenance, infrastructure, and labor expenses. Also, Internet of Things (IoT) apps are designed to ease developers' and users' access to networks of smart services, devices, and data. Although cloud services give nearly infinite resources, their reach is constrained. Designing coherent and organized apps is made possible by integrating the cloud and IoT. Expanding facilities by combining services is a critical component of this technology. Various services may be presented in this environment based on the user's demands. Considering their Quality of Service (QoS) attributes, discovering the appropriate available atomic services to construct the needed composite service with their collaboration in an orchestration model is an NP‐hard issue. This article suggests a service composition method using Grey Wolf Optimization (GWO) and MapReduce framework to compose services with optimized QoS. The simulation outcomes illustrate cost, availability, response time, and energy‐saving improvements through the suggested approach. Comparing the suggested technique to three baseline algorithms, the average gain is a 40% improvement in energy savings, a 14% decrease in response time, an 11% increase in availability, and a 24% drop in cost.

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Section: System Model and Proposed Methodsmentioning

confidence: 99%

“…The Reduce function processes each key2's matching value list. 57 Algorithm 2 indicates the steps of the presented method.…”

Section: Algorithm 1 Pseudocode Of the Gwo Algorithmmentioning

confidence: 99%

A new service composition method in the cloud‐based Internet of things environment using a grey wolf optimization algorithm and MapReduce framework

Vakili,

Al‐Khafaji,

Darbandi

et al. 2024

Concurrency and Computation

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show abstract

“…While the number of MapReduce approaches for FIM has been numerous (L. Wang, Feng, Zhang, & Liao, ; C. Wang, Lin, & Chang, ), existing methods still do not have a good scalability due to high workload skewness, large intermediate data, and large network communication overhead. In this sense, the BIGMiner algorithm (Chon & Kim, ) was recently proposed, which was described as a fast and scalable MapReduce proposal for FIM.…”

Section: Distributed Computing Solutionsmentioning

confidence: 99%

“…While the number of MapReduce approaches for FIM has been numerous (L. Wang, Feng, Zhang, & Liao, 2014;C. Wang, Lin, & Chang, 2017), existing methods still do not have a good scalability due to high workload skewness, large intermediate data, and large network communication overhead.…”

Section: Gpu-singlescanmentioning

confidence: 99%

Frequent itemset mining: A 25 years review

Luna

Fournier-Viger

Ventura

2019

WIREs Data Min & Knowl

175

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Frequent itemset mining (FIM) is an essential task within data analysis since it is responsible for extracting frequently occurring events, patterns, or items in data. Insights from such pattern analysis offer important benefits in decision‐making processes. However, algorithmic solutions for mining such kind of patterns are not straightforward since the computational complexity exponentially increases with the number of items in data. This issue, together with the significant memory consumption that is present in the mining process, makes it necessary to propose extremely efficient solutions. Since the FIM problem was first described in the early 1990s, multiple solutions have been proposed by considering centralized systems as well as parallel (shared or nonshared memory) architectures. Solutions can also be divided into exhaustive search and nonexhaustive search models. Many of such approaches are extensions of other solutions and it is therefore necessary to analyze how this task has been considered during the last decades. This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules

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“…The distributed data processing framework MapReduce was first introduced by Google and later it was incorporated into Hadoop as its strong capability [46,60]. Apache…”

Section: Distributed Processing Of Geospatial Datamentioning

confidence: 99%

Multi-dimensional geospatial data mining in a distributed environment using MapReduce

Alkathiri

Abdul²,

Potdar³

2019

J Big Data

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Data mining and machine learning techniques for processing raster data consider a single spectral band of data at a time. The individual results are combined to obtain the final output. The essence of related multi-spectral information is lost when the bands are considered independently. The proposed platform is based on Apache Hadoop ecosystem and supports performing analysis on large amounts of multispectral raster data using MapReduce. A novel technique of transforming the spectral space to the geometrical space is also proposed. The technique allows to consider multiple bands coherently. The results of clustering 10 6 pixels for multiband imagery with widely used GIS software have been tested and other machine learning methods are planned to be incorporated in the platform. The platform is scalable to support tens of spectral bands. The results from our platform were found to be better and are also available faster due to application of distributed processing.

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MapReduce-Based Frequent Pattern Mining Framework with Multiple Item Support

Cited by 6 publications

References 7 publications

A new service composition method in the cloud‐based Internet of things environment using a grey wolf optimization algorithm and MapReduce framework

A new service composition method in the cloud‐based Internet of things environment using a grey wolf optimization algorithm and MapReduce framework

Frequent itemset mining: A 25 years review

Multi-dimensional geospatial data mining in a distributed environment using MapReduce

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