A taxonomy of software-based and hardware-based approaches for energy efficiency management in the Hadoop

Shabestari, Fatemeh; Rahmani, Amir Masoud; Navimipour, Nima Jafari; Jabbehdari, Sam

doi:10.1016/j.jnca.2018.11.007

Cited by 36 publications

(13 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, there have been few reviews on this topic, also, KS benefits in SC have not been discussed well. While systematic reviews are very important for performing a sound review (Ghanbari et al , 2019; Shabestari et al , 2019), these surveys did not present a complete Systematic Literature Review (SLR)-based review of the KS application in the SC with an analysis of their taxonomy and future challenges. Table I provides a brief summary of the reviewed surveys and their main properties.…”

Section: Related Workmentioning

confidence: 99%

The role of knowledge sharing in supply chain success

Rajabion

Mokhtari²,

Khordehbinan

et al. 2019

JEDT

View full text Add to dashboard Cite

Purpose The aim of this paper is to provide a comprehensive and detailed review of the state-of-the-art mechanisms of knowledge sharing (KS) in the supply chain (SC) field, as well as directions for future research. Briefly, this paper tries to offer a systematic and methodical review of the KS mechanisms in the SC to provide a comparative summary of the selected articles, to collect and describe the factors that have the influence on KS and SC, to explore some main challenges in this field and to present the guidelines to face the existing challenges and outlining the key areas where the KS mechanisms in SC can be improved. Design/methodology/approach In the current study, a systematic literature review up to 2018 is presented on the supply chain’s mechanisms of KS. The authors identified 21,907 papers, which are reduced to 25 primary studies through the paper-selection process. Findings The results showed that the KS in SC helps to increase the success of the organizations, improve employee performance, increase competitive advantage, enhance innovation and improve relationships between supplier and consumer. However, there were some weaknesses, such as staff resistance to share knowledge in the SC because of fear of job loss. Research limitations/implications There are several limitations to this study. This study limited the search to Google Scholar. There might be other academic journals where Google does not find their paper and they can offer a more complete picture of the related articles. Finally, non-English publications were omitted from this study. It is possible that the research about the application of KS in SC can also be published in other languages. In addition, more studies need to be carried out using other methodologies such as interviews. Originality/value The paper presents a comprehensive structured literature review of the articles’ mechanisms of KS in SC. The paper’s findings can offer insights into future research needs. By providing comparative information and analyzing the current developments in this area, this paper will directly support academics and practicing professionals for better knowing the progress in KS mechanisms.

show abstract

Section: Related Workmentioning

confidence: 99%

The role of knowledge sharing in supply chain success

Rajabion

Mokhtari²,

Khordehbinan

et al. 2019

JEDT

View full text Add to dashboard Cite

show abstract

“…It puts out the flexibility to execute software for clusters. YARN is a resource management tool, as well 55 …”

Section: Introductionmentioning

confidence: 99%

Leveraging big data in smart cities: A systematic review

Karimi

Kashani

Akbari

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

Recently, the notion of a smart city, which includes smart well‐being, smart transit, and smart society, has attracted much attention due to its impact on people's quality of living. Data in smart cities are characterized by variety, velocity, volume, value, and veracity that are the well‐known characteristics of big data. The fast pace expanding of IoT devices and sensors in smart cities generates a huge volume of data that can help decision‐makers and managers in city management. The aim of this article is to wholly and systematically review big data handling approaches in smart cities, in which we analyze research efforts published between 2013 and February 2021, where these techniques are categorized based on their algorithms and architectures. Further, the main ideas, evaluation techniques, tools, evaluation metrics, algorithm types, advantages, and disadvantages are explored. Additionally, essential evaluation factors are introduced in which scalability and availability by 16%, time by 15% and accuracy by 11% are more in focus, and finally, some of the challenges, open issues, and future trends that are valuable for further research are suggested in big data handling approaches in smart cities.

show abstract

“…However, clustering such large datasets is a challenge for traditional clustering algorithms due to its huge processing times. This can be addressed by using MapReduce, a scalable and distributed processing technique (Shabestari et al, 2019). It is a programming paradigm used for parallel distributed processing of large datasets (Dean & Ghemawat, 2008).…”

Section: Introductionmentioning

confidence: 99%

A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method

Azhir

Navimipour

Hosseinzadeh

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

Query optimization is the process of identifying the best Query Execution Plan (QEP). The query optimizer produces a close to optimal QEP for the given queries based on the minimum resource usage. The problem is that for a given query, there are plenty of different equivalent execution plans, each with a corresponding execution cost. To produce an effective query plan thus requires examining a large number of alternative plans. Access plan recommendation is an alternative technique to database query optimization, which reuses the previously-generated QEPs to execute new queries. In this technique, the query optimizer uses clustering methods to identify groups of similar queries. However, clustering such large datasets is challenging for traditional clustering algorithms due to huge processing time. Numerous cloud-based platforms have been introduced that offer low-cost solutions for the processing of distributed queries such as Hadoop, Hive, Pig, etc. This paper has applied and tested a model for clustering variant sizes of large query datasets parallelly using MapReduce. The results demonstrate the effectiveness of the parallel implementation of query workloads clustering to achieve good scalability.

show abstract

A taxonomy of software-based and hardware-based approaches for energy efficiency management in the Hadoop

Cited by 36 publications

References 73 publications

The role of knowledge sharing in supply chain success

The role of knowledge sharing in supply chain success

Leveraging big data in smart cities: A systematic review

A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method

Contact Info

Product

Resources

About