Genomic repeats detection using Boyer-Moore algorithm on Apache Spark Streaming

Riza, Lala Septem; Pratama, Farhan Dhiyaa; Piantari, Erna; Fahsi, Mahmoud

doi:10.12928/telkomnika.v18i2.14883

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…RDDs are well-suited for a diversity of applications. Figure 1 presents the spark-cluster framework [10]. A spark comprises a driver node that is equivalent to a master node and several worker nodes that are correspondent to slave nodes.…”

Section: Background Of the Study 21 Sparkmentioning

confidence: 99%

A smart method for spark using neural network for big data

Rahman

Hossen

Sultana

et al. 2021

IJECE

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Apache spark, famously known for big data handling ability, is a distributed open-source framework that utilizes the idea of distributed memory to process big data. As the performance of the spark is mostly being affected by the spark predominant configuration parameters, it is challenging to achieve the optimal result from spark. The current practice of tuning the parameters is ineffective, as it is performed manually. Manual tuning is challenging for large space of parameters and complex interactions with and among the parameters. This paper proposes a more effective, self-tuning approach subject to a neural network called Smart method for spark using neural network for big data (SSNNB) to avoid the disadvantages of manual tuning of the parameters. The paper has selected five predominant parameters with five different sizes of data to test the approach. The proposed approach has increased the speed of around 30% compared with the default parameter configuration.

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Section: Background Of the Study 21 Sparkmentioning

confidence: 99%

A smart method for spark using neural network for big data

Rahman

Hossen

Sultana

et al. 2021

IJECE

View full text Add to dashboard Cite

show abstract

“…This means that the proposed method in this research is an improvement of the previous one. The other study involving Apache Spark on discovery patterns was performed by Jiang et al [ 13 ], Riza et al [ 14 ], and Pérez-Chacón et al [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

SAX and Random Projection Algorithms for the Motif Discovery of Orbital Asteroid Resonance Using Big Data Platforms

Riza

Fazanadi

Utama

et al. 2022

Sensors

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The phenomenon of big data has occurred in many fields of knowledge, one of which is astronomy. One example of a large dataset in astronomy is that of numerically integrated time series asteroid orbital elements from a time span of millions to billions of years. For example, the mean motion resonance (MMR) data of an asteroid are used to find out the duration that the asteroid was in a resonance state with a particular planet. For this reason, this research designs a computational model to obtain the mean motion resonance quickly and effectively by modifying and implementing the Symbolic Aggregate Approximation (SAX) algorithm and the motif discovery random projection algorithm on big data platforms (i.e., Apache Hadoop and Apache Spark). There are five following steps on the model: (i) saving data into the Hadoop Distributed File System (HDFS); (ii) importing files to the Resilient Distributed Datasets (RDD); (iii) preprocessing the data; (iv) calculating the motif discovery by executing the User-Defined Function (UDF) program; and (v) gathering the results from the UDF to the HDFS and the .csv file. The results indicated a very significant reduction in computational time between the use of the standalone method and the use of the big data platform. The proposed computational model obtained an average accuracy of 83%, compared with the SwiftVis software.

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Genomic repeats detection using Boyer-Moore algorithm on Apache Spark Streaming

Cited by 2 publications

References 19 publications

A smart method for spark using neural network for big data

A smart method for spark using neural network for big data

SAX and Random Projection Algorithms for the Motif Discovery of Orbital Asteroid Resonance Using Big Data Platforms

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