STA/LTA trigger algorithm implementation on a seismological dataset using Hadoop MapReduce

Choubik, Youness; Mahmoudi, Abdelhak; Himmi, Mohammed Majid; Moudnib, Lahcen El

doi:10.11591/ijai.v9.i2.pp269-275

Cited by 5 publications

(4 citation statements)

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“…MapReduce is a programming methodology created by Google to handle large-scale data analysis. It is based on the Hadoop framework [11], [58], [78]- [81], [64]- [67], [69]- [71], [74]. It is employed in a wide variety of applications.…”

Section: Mapreducementioning

confidence: 99%

Big data classification based on improved parallel k-nearest neighbor

Ali

Mohammed²,

Hasan³

et al. 2023

TELKOMNIKA

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In response to the rapid growth of many sorts of information, highway data has continued to evolve in the direction of big data in terms of scale, type, and structure, exhibiting characteristics of multi-source heterogeneous data. The k-nearest neighbor (KNN) join has received a lot of interest in recent years due to its wide range of applications. Processing KNN joins is time-consuming and inefficient due to the quadratic structure of the join method. As the number of applications dealing with vast amounts of data develops, KNN joins get more sophisticated. The authors seek to save money on computer resources by leveraging a large number of threads and multiprocessors. Six popular datasets are used to apply the method and evaluate the sequential and parallel performance of the KNN technique. These datasets are used to compare the sequential and parallel performance of the KNN method. When compared to a matching multi-core solution, the final implementation saves computing resources. It has been optimized to utilize as little RAM as possible, allowing it to manage high-resolution photo data without sacrificing efficiency. The authors will use the technique they presented using Spark Radoop. Our performance research validates the supplied method's efficacy and scalability.

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Section: Mapreducementioning

confidence: 99%

Big data classification based on improved parallel k-nearest neighbor

Ali

Mohammed²,

Hasan³

et al. 2023

TELKOMNIKA

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“…RapidMiner Radoop is an extension of the in-memory functionality of RapidMiner that allows for the provision of sophisticated operators that are implementable for in-Hadoop execution [61]- [66]. It was developed as an extension of the in-memory functionality of RapidMiner for the provision of sophisticated operators that are implementable for in-Hadoop execution [67]- [73]. For data transformation in Radoop [61], there are more than 60 operators available.…”

Section: Apache Spark Radoopmentioning

confidence: 99%

An effective classification approach for big data with parallel generalized Hebbian algorithm

et al. 2021

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Advancements in information technology is contributing to the excessive rate of big data generation recently. Big data refers to datasets that are huge in volume and consumes much time and space to process and transmit using the available resources. Big data also covers data with unstructured and structured formats. Many agencies are currently subscribing to research on big data analytics owing to the failure of the existing data processing techniques to handle the rate at which big data is generated. This paper presents an efficient classification and reduction technique for big data based on parallel generalized Hebbian algorithm (GHA) which is one of the commonly used principal component analysis (PCA) neural network (NN) learning algorithms. The new method proposed in this study was compared to the existing methods to demonstrate its capabilities in reducing the dimensionality of big data. The proposed method in this paper is implemented using Spark Radoop platform.

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“…In the recent years, the semantic data capacity is maximized with amount of research description framework (RDF) datasets excessive of trillion triples in RDF archives. So, RDF dataset's size grows simultaneously [16]. Although with an increasing amount of RDF triples, the complex multi-RDF queries become major demand [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Structured query language query join optimization by using rademacher averages and mapreduce algorithms

Chandrashekariah,

H. A.

2024

Bulletin EEI

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Query optimization involves identifying and implementing the most effective and efficient methods and strategies to enhance the performance of queries. This is achieved by intelligently utilizing system resources and considering various performance metrics. Table joining optimization involves optimizing the process of combining two or more tables within a database. Structured query language (SQL) optimization is the progress of utilizing SQL queries in the possible way to achieve fast and accurate database results. SQL optimization is critical to decreasing the no of queries in research description framework (RDF) and the time for processing a huge number of relatable data. In this paper, four new algorithms are proposed such as hash-join, sort-merge, rademacher averages and mapreduce for the progress of SQL query join optimization. The proposed model is evaluated and tested using waterloo sparql diversity test suite (WatDiv) and lehigh university benchmark (LUBM) benchmark datasets in terms of time execution. The results represented that the proposed method achieved an enhanced performance of less execution time for various queries such as Q3 of 5362, Q8 of 5921, Q9 of 5854 and Q10 of 5691 milliseconds. The proposed gives better performance than other existing methods like hybrid database-map reduction system (AQUA+) and join query processing (JQPro).

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STA/LTA trigger algorithm implementation on a seismological dataset using Hadoop MapReduce

Cited by 5 publications

References 0 publications

Big data classification based on improved parallel k-nearest neighbor

Big data classification based on improved parallel k-nearest neighbor

An effective classification approach for big data with parallel generalized Hebbian algorithm

Structured query language query join optimization by using rademacher averages and mapreduce algorithms

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