Effective data prediction method for in-memory database applications

Yun, Ji Tae; Yoon, Su Kyung; Kim, Jeong Geun; Kim, Shin Dug

doi:10.1007/s11227-019-03050-x

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…To address flash memory access latency and cost constraints, the authors of [24] presented a hybrid main memory structure that combines dynamic RAM and flash storage with a cluster-based migration mechanism positioned between them. To address performance loss brought on by unforeseen memory access patterns, a regression-based prefetching approach was developed.…”

Section: Literature On Prefetching and Caching Techniquesmentioning

confidence: 99%

HRFP: Highly Relevant Frequent Patterns-Based Prefetching and Caching Algorithms for Distributed File Systems

et al. 2023

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Data-intensive applications are generating massive amounts of data which is stored on cloud computing platforms where distributed file systems are utilized for storage at the back end. Most users of those applications deployed on cloud computing systems read data more often than they write. Hence, enhancing the performance of read operations is an important research issue. Prefetching and caching are used as important techniques in the context of distributed file systems to improve the performance of read operations. In this research, we introduced a novel highly relevant frequent patterns (HRFP)-based algorithm that prefetches content from the distributed file system environment and stores it in the client-side caches that are present in the same environment. We have also introduced a new replacement policy and an efficient migration technique for moving the patterns from the main memory caches to the caches present in the solid-state devices based on a new metric namely the relevancy of the patterns. According to the simulation results, the proposed approach outperformed other algorithms that have been suggested in the literature by a minimum of 15% and a maximum of 53%.

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Section: Literature On Prefetching and Caching Techniquesmentioning

confidence: 99%

HRFP: Highly Relevant Frequent Patterns-Based Prefetching and Caching Algorithms for Distributed File Systems

et al. 2023

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“…By caching data and objects in memory, the number of database reads is reduced and the access speed is improved. Applications can store frequently accessed data and data that requires significant processing time to create in memory, thereby improving performance [52].…”

Section: A Storage Of Obstetric Imagesmentioning

confidence: 99%

Obstetric Imaging Diagnostic Platform Based on Cloud Computing Technology Under the Background of Smart Medical Big Data and Deep Learning

2020

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The deep learning methods in the field of computer vision and big data are becoming more and more mature. Through the application of big data and deep learning technology, the diagnosis of artificial intelligence medical image can be realized, which provides a new opportunity for the automatic analysis of obstetrics medical image and the assistance of doctors to realize high-precision intelligent diagnosis of diseases. The current medical obstetric image diagnosis platform mainly targets low-resolution medical obstetric image files, and does not consider the data-sharing problem of the distributed file system in different storage nodes, which greatly reduces the efficiency of obstetric image storage and diagnosis. Based on this, this article designs an obstetric image diagnostic platform based on cloud computing technology. First, a medical imaging platform was designed by combining cloud computing technology, caching technology, and a distributed file system. Secondly, the use of contrast-enhanced ultrasound technology provides a more accurate ultrasound image for assessing the structure, size, location, and developmental abnormalities of the placenta. Finally, the effectiveness of the obstetric imaging diagnostic platform proposed in this paper is verified by experiments. The results show that the platform has fast data processing speed and convenient use, which greatly reduces the cost of medical equipment and improves efficiency. The hospital only needs to collect the obstetric image of the patient at the front end, transfer it to the cloud for image processing, and finally diagnose the disease.INDEX TERMS Smart medicine, big data, cloud computing technology, obstetric imaging, diagnosis.

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“…Yasin et al (2014) proposed a method of intelligent Web caching based on machine learning using the J48 algorithm. This study used IRcache data set.…”

Section: Caching Strategymentioning

confidence: 99%

Caching strategy for Web application – a systematic literature review

Zulfa

Hartanto

Permanasari

2020

IJWIS

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Purpose Internet users and Web-based applications continue to grow every day. The response time on a Web application really determines the convenience of its users. Caching Web content is one strategy that can be used to speed up response time. This strategy is divided into three main techniques, namely, Web caching, Web prefetching and application-level caching. The purpose of this paper is to put forward a literature review of caching strategy research that can be used in Web-based applications. Design/methodology/approach The methods used in this paper were as follows: determined the review method, conducted a review process, pros and cons analysis and explained conclusions. The review method is carried out by searching literature from leading journals and conferences. The first search process starts by determining keywords related to caching strategies. To limit the latest literature in accordance with current developments in website technology, search results are limited to the past 10 years, in English only and related to computer science only. Findings Note in advance that Web caching and Web prefetching are slightly overlapping techniques because they have the same goal of reducing latency on the user’s side. But actually, the two techniques are motivated by different basic mechanisms. Web caching uses the basic mechanism of cache replacement or the algorithm to change cache objects in memory when the cache capacity is full, whereas Web prefetching uses the basic mechanism of predicting cache objects that can be accessed in the future. This paper also contributes practical guidelines for choosing the appropriate caching strategy for Web-based applications. Originality/value This paper conducts a state-of-the art review of caching strategies that can be used in Web applications. Exclusively, this paper presents taxonomy, pros and cons of selected research and discusses data sets that are often used in caching strategy research. This paper also provides another contribution, namely, practical instructions for Web developers to decide the caching strategy.

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Effective data prediction method for in-memory database applications

Cited by 7 publications

References 22 publications

HRFP: Highly Relevant Frequent Patterns-Based Prefetching and Caching Algorithms for Distributed File Systems

HRFP: Highly Relevant Frequent Patterns-Based Prefetching and Caching Algorithms for Distributed File Systems

Obstetric Imaging Diagnostic Platform Based on Cloud Computing Technology Under the Background of Smart Medical Big Data and Deep Learning

Caching strategy for Web application – a systematic literature review

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