On the Auto-Tuning of Elastic-search based on Machine Learning

Lù, Zhongwen; Chen, Chao; Xin, Jinhan; Yu, Zhibin

doi:10.1145/3437802.3437828

Cited by 3 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the first instance, it works to ensure the real-time detection of fraudulent activities through patterns and key information pieces. The indexing and real-time data analysis ensures that companies easily recognize and mitigate fraudulent activities, leading to lesser losses and reputational damages [20]. The platform also has a capacity to recognize patterns and detect anomalies as Fig.…”

Section: Elastic Search For Real Time Analysismentioning

confidence: 99%

Enhancing Fra1ud Detection in Real Time using DataOps on Elastic Platforms

Ankur Mahida,

Pradeep Chintale,

Hrushikesh Deshmukh

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

View full text Add to dashboard Cite

Fraud detection has gained prominence as a measure of enhancing security and management of various organization’s networks. Moreover, having real time fraud detection enhances on-site handling of fraudulent activities and channels that could otherwise result in negative outcomes for companies. Real-time fraud detection can be conducted through DataOps practices, which enhance the capacity of data to have reliable information, and conducted on Elastic platforms which scale the amount of data to be provided and remarkable channel to achieve sustainable management of the data. Fraud detection on elastic platforms like elastic search have a capacity to detect real-time information through predetermined standards and approaches that provide alerts in case of any communication approach. Nonetheless, the fraud detection approach works with meaningful engagement to ensure sustainable compliance to privacy regulations such as anonymization, which ensures that personally identifiable data is not used for the wrong purpose. Hence, the act of fraud detection requires instrumental data handling mechanisms and appeal to scalability and flexibility of elastic platforms to achieve a scalable operation.

show abstract

Section: Elastic Search For Real Time Analysismentioning

confidence: 99%

Enhancing Fra1ud Detection in Real Time using DataOps on Elastic Platforms

Ankur Mahida,

Pradeep Chintale,

Hrushikesh Deshmukh

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

View full text Add to dashboard Cite

show abstract

“…A similar approach to the one presented here uses genetic algorithms to evolve the configuration of Elasticsearch (Lu et al, 2020 ). Machine learning algorithms, namely random forest and gradient boosting regression trees, were used to predict the performance of the configuration.…”

Section: Related Workmentioning

confidence: 99%

“…Since the performance of a complete-set is limited by the performance of the slowest server, it is suggested that all nodes in the complete-set should be reconfigured to match the fastest node speed. In a similar manner to Lu et al ( 2020 ), a random forest is trained to predict the performance of the NoSQL server for different configurations of VMs and databases.…”

Section: Related Workmentioning

confidence: 99%

Tuning of Elasticsearch Configuration: Parameter Optimization Through Simultaneous Perturbation Stochastic Approximation

2022

View full text Add to dashboard Cite

Elasticsearch is currently the most popular search engine for full-text database management systems. By default, its configuration does not change while it receives data. However, when Elasticsearch stores a large amount of data over time, the default configuration becomes an obstacle to improving performance. In addition, the servers that host Elasticsearch may have limited resources, such as internal memory and CPU. A general solution to these problems is to dynamically tune the configuration parameters of Elasticsearch in order to improve its performance. The sheer number of parameters involved in this configuration makes it a complex task. In this work, we apply the Simultaneous Perturbation Stochastic Approximation method for optimizing Elasticsearch with multiple unknown parameters. Using this algorithm, our implementation optimizes the Elasticsearch configuration parameters by observing the performance and automatically changing the configuration to improve performance. The proposed solution makes it possible to change the configuration parameters of Elasticsearch automatically without having to restart the currently running instance of Elasticsearch. The results show a higher than 40% improvement in the combined data insertion capacity and the system's response time.

show abstract

“…In this regard, we have used Elasticsearch engines which is a Java-based program provides by google. The Elasticsearch engine, is powered by classification-based supervised learning, uses a new type of boosted method called, boosted tree regression (24). The classification decision is based on decision tree algorithm with some special hyperparameters.…”

Section: Reliable Analysismentioning

confidence: 99%

Computational analysis of transition temperatures (Tts) of proteins fused to elastin-like polypeptide (ELP): deep fake evaluation of proteins, linkers, and trailers features

Ghafari

Rasooli

Khajeh

et al. 2021

Preprint

View full text Add to dashboard Cite

The phase transition temperature (Tt) prediction of the Elastin-like polypeptides (ELPs) is not trivial because it is related to complex sets of variables such as composition, sequence length, hydrophobic characterization, hydrophilic characterization, the sequence order in the fused proteins, linkers and trailer constructs. In this paper, two unique quantitative models are presented for the prediction of the Tt of a family of ELPs that could be fused to different proteins, linkers, and trailers. The lack of need to use multiple software, peptide information, such as PDB file, as well as knowing the second and third structures of proteins are the advantages of this model besides its high accuracy and speed. One of our models could predict the Tt values of the fused ELPs by entering the protein, linker, and trailer features with R2=99%. Also, another model is able to predict the Tt value by entering the fused protein feature with R2=96%. For more reliability, our method is enriched by Artificial Intelligence (AI) to generate similar proteins. In this regard, Generative Adversarial Network (GAN) is our AI method to create fake proteins and similar values. The experimental results show that our strategy for prediction of Tt is reliable in large data.

show abstract

On the Auto-Tuning of Elastic-search based on Machine Learning

Cited by 3 publications

References 15 publications

Enhancing Fra1ud Detection in Real Time using DataOps on Elastic Platforms

Enhancing Fra1ud Detection in Real Time using DataOps on Elastic Platforms

Tuning of Elasticsearch Configuration: Parameter Optimization Through Simultaneous Perturbation Stochastic Approximation

Computational analysis of transition temperatures (Tts) of proteins fused to elastin-like polypeptide (ELP): deep fake evaluation of proteins, linkers, and trailers features

Contact Info

Product

Resources

About