Critical analysis of Big Data challenges and analytical methods

Sivarajah, Uthayasankar; Kamal, Muhammad Mustafa; Irani, Zahir; Weerakkody, Vishanth

doi:10.1016/j.jbusres.2016.08.001

Cited by 1,533 publications

(1,075 citation statements)

References 67 publications

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“…While related work emphasizes data-related challenges such as data acquisition, cleansing or aggregation (Sivarajah et al, 2017), this work focuses on process challenges.…”

Section: Data Science As An Innovation Challenge: From Big Data To Vamentioning

confidence: 99%

Data Science as an Innovation Challenge: From Big Data to Value Proposition

Kayser¹,

Nehrke²,

Zubovic³

2018

TIM Review

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“…While related work emphasizes data-related challenges such as data acquisition, cleansing or aggregation (Sivarajah et al, 2017), this work focuses on process challenges.…”

Section: Data Science As An Innovation Challenge: From Big Data To Vamentioning

confidence: 99%

Data Science as an Innovation Challenge: From Big Data to Value Proposition

Kayser¹,

Nehrke²,

Zubovic³

2018

TIM Review

View full text Add to dashboard Cite

“…Types of Big Data analytical methods generally include [40]: 1) descriptive analytics -involving the description and summarization of knowledge patterns; 2) predictive analytics -forecasting and statistical modelling to determine future possibilities; and 3) prescriptive analytics -helping analysts in decision-making by determining actions and assessing their impacts. Distributed systems, massive parallel processing (MPP) databases, nonrelational, or in-memory databases have been used for big data.…”

Section: Big Data Big Data Analytics and Big Data Toolsmentioning

confidence: 99%

Heterogeneous Data and Big Data Analytics

Wang¹

2017

ACIS

104

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Heterogeneity is one of major features of big data and heterogeneous data result in problems in data integration and Big Data analytics. This paper introduces data processing methods for heterogeneous data and Big Data analytics, Big Data tools, some traditional data mining (DM) and machine learning (ML) methods. Deep learning and its potential in Big Data analytics are analysed. The benefits of the confluences among Big Data analytics, deep learning, high performance computing (HPC), and heterogeneous computing are presented. Challenges of dealing with heterogeneous data and Big Data analytics are also discussed.

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“…In order to deal with huge and homogeneous datasets, Big Data provides predictive algorithms, user behaviour analytics, and aggregation/correlation functionalities [39]. These capabilities are mainly taken into account in monitoring and aggregation tasks.…”

Section: Design Principlesmentioning

confidence: 99%

An Approach to Data Analysis in 5G Networks

López

Vidal

Villalba

2017

Entropy

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5G networks expect to provide significant advances in network management compared to traditional mobile infrastructures by leveraging intelligence capabilities such as data analysis, prediction, pattern recognition and artificial intelligence. The key idea behind these actions is to facilitate the decision-making process in order to solve or mitigate common network problems in a dynamic and proactive way. In this context, this paper presents the design of Self-Organized Network Management in Virtualized and Software Defined Networks (SELFNET) Analyzer Module, which main objective is to identify suspicious or unexpected situations based on metrics provided by different network components and sensors. The SELFNET Analyzer Module provides a modular architecture driven by use cases where analytic functions can be easily extended. This paper also proposes the data specification to define the data inputs to be taking into account in diagnosis process. This data specification has been implemented with different use cases within SELFNET Project, proving its effectiveness.

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Critical analysis of Big Data challenges and analytical methods

Cited by 1,533 publications

References 67 publications

Data Science as an Innovation Challenge: From Big Data to Value Proposition

Data Science as an Innovation Challenge: From Big Data to Value Proposition

Heterogeneous Data and Big Data Analytics

An Approach to Data Analysis in 5G Networks

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