Big data analytics for data-driven industry: a review of data sources, tools, challenges, solutions, and research directions

Ikegwu, Anayo Chukwu; Nweke, Henry Friday; Anikwe, Chioma Virginia; Alo, Uzoma Rita; Okonkwo, Obikwelu Raphael

doi:10.1007/s10586-022-03568-5

Cited by 49 publications

(23 citation statements)

References 129 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To mine these data requires more knowledge about big data, which can be classified according to the distribution form of the data: structured big data, semi-structured big data, and unstructured big data. In the field of philosophical thought dissemination, unstructured big data have the highest percentage, which can reach 83.9%, and this percentage will continue to increase with the continuous improvement of data collection and storage [18][19][20]. is study, based on the understanding and recognition of the characteristics of big data, also requires technology as a means to bring the value of big data into play with the various algorithms that are popular and common today, and most of these algorithms are already relatively well developed and mature.…”

Section: Introductionmentioning

confidence: 99%

The Basic Principles of Marxism with the Internet as a Carrier

Ruichen

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

We live in a real society today with the Internet as the carrier. Marxist philosophy is of great significance to the study of computer science and technology majors, such as the standardization of learning methods and the improvement of learning horizons. In order to study the ideology of the current Internet carrier, this study first introduces the ideology in the Internet scenario and again describes the basic principles of Marxism. Web text data were collected from both static and dynamic websites, and the data were initially preprocessed using the ICTCLAS word separation tool. After that, a text lexicon set related to Marxism was built and combined with the surplus value calculation formula. Analysis of the text data shows that the expectation error of the Internet population approximately obeys a positive-term distribution, and the online text data are feasible in the estimation of the influence of Marxism. The statistical results show that the percentage of serious philosophical discussions from 2017 to 2021 in which Marxism is mentioned has steadily increased from 62% to 78%, indicating that Marxism still has great influence to this day. For these three different attitudes toward Marxism, the number of comments agreeing and disagreeing differed significantly, indicating that different groups of people show very different attitudes toward Marxism.

show abstract

Section: Introductionmentioning

confidence: 99%

The Basic Principles of Marxism with the Internet as a Carrier

Ruichen

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…YARN is an important framework because it dynamically handles multiple processing and real-time interactive processing of climate big data. Furthermore, it improves multi-tenancy, cluster utilization, scalability, and compatibility in climate big data processing and management (iv) Google BigTable Climate big data storage is important and requires an e cient implementation framework that can handle distributed, column-oriented data storage and a large amount of climate-unstructured data [104].…”

Section: Data Types and Sourcesmentioning

confidence: 99%

Recently Emerging Trends in Big Data Analytic Methods for Modeling and Combating Climate Change Effects

Ikegwu

Nweke

Alo

et al. 2023

Preprint

View full text Add to dashboard Cite

Big climate change data have become a pressing issue that organizations faced with methods to analyse data generated from various data types. However, storage, processing, and analysis of data generated from climate change activities are massive, which is challenging for the current algorithms to handle. Therefore, big data analytics methods are designed for significant data that is required to enhance seasonal change monitoring and understanding, ascertain the health risk of climate change, and improve the allocation, and utilisation of natural resources. This paper provides an outlook on big data analytic methods and describes how climate change and sustainability issues can be analysed through these methods. We extensively discuss big data analytic methods, strengths, and weaknesses. The purpose of analysing big climate change using these methods, the common datasets and implementation frameworks for climate change modeling using the big data analytics approach was also discussed. This big data analytics method is well timed to solve the inherent issues of data analysis and easy realization of sustainable development goals.

show abstract

“…In [46], recent big data analytics tools and their features are presented. Data-driven industrial applications and the challenges of big data analytics projects are discussed.…”

Section: Related Workmentioning

confidence: 99%

“…Data is everywhere, and data processing and analytics are crucial to accomplishing various purposes in almost all diverse domains, such as education, transportation, healthcare, business, crisis informatics, etc. [46]. For example, crisis informatics researchers in academia study crisis data to explore public behaviors before, during, and after emergencies and to investigate disaster-affected people during emergencies to help and support crisis management [88].…”

Section: Applications and Use Casesmentioning

confidence: 99%

A Comparative Perspective on Technologies of Big Data Value Chain

Aydin

2023

IEEE Access

View full text Add to dashboard Cite

Data is one of the most valuable assets in the digital era because it may conceal hidden valuable insights. Diverse organizations in diverse domains overcome the challenges of the big data value chain by employing a wide range of technologies to meet their needs and achieve a variety of goals to support their decision-making. Due to the significance of data-oriented technologies, this paper presents a model of the big data value chain based on technologies used in the acquisition, storage, and analysis of data. The following are the paper's contributions: First, a model of the big data value chain is developed to illustrate a comprehensive representation of the big data value chain that depicts the relationships between the characteristics of big data and the technologies associated with each category. Second, in contrast to previous research, this paper presents an overview of technologies for each category of the big data value chain. The third contribution of this paper is to assist researchers and developers of data-intensive systems in selecting the appropriate technology for their specific application development use cases by providing examples of applications and use cases from prominent papers in a variety of fields and by describing the capabilities and stages of the technologies being presented so that the right technology is used at the right time in the big data collection, processing, storage, and analytics tasks.

show abstract

Big data analytics for data-driven industry: a review of data sources, tools, challenges, solutions, and research directions

Cited by 49 publications

References 129 publications

The Basic Principles of Marxism with the Internet as a Carrier

The Basic Principles of Marxism with the Internet as a Carrier

Recently Emerging Trends in Big Data Analytic Methods for Modeling and Combating Climate Change Effects

A Comparative Perspective on Technologies of Big Data Value Chain

Contact Info

Product

Resources

About