The Role of IoT and Big Data in Modern Technological Arena: A Comprehensive Study

Priyadarshini, Sushree Bibhuprada B.; BhusanBagjadab, Amiya; Mishra, Brojo Kishore

doi:10.1007/978-3-030-04203-5_2

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…Due to the increasing integration of devices via the Internet (Wierse and Riedel 2017) along with the continuous adoption of smartphones, (connected) wearables, "smart" homes and cities, the internet of things becomes increasingly relevant (e.g., Capgemini 2019). The technology generally refers to an identifiable system of objects (e.g., furniture, wearables, vehicles) whose information is transmitted without human intervention, and has emerged in particular from the integration of microelectromechanical systems, wireless technologies and the Internet (Priyadarshini et al 2019;Spender et al 2019).…”

Section: Descriptionmentioning

confidence: 99%

How digitalization affects insurance companies: overview and use cases of digital technologies

Eckert

Osterrieder

2020

ZVersWiss

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The digital transformation is of increasing relevance for insurance companies’ business models. It leads to opportunities as well as challenges, especially for IT departments as core enablers or preventers. Against this background, the aim of this paper is to provide a comprehensive overview of digital technologies (e.g., artificial intelligence, cloud computing) and the resulting use cases for the insurance industry. To this end, we conduct a review of academic articles, industry studies and publications of the supervisory authorities. We point to the resulting requirements for an insurer’s IT and find many interdependencies between the digital technologies. Our results therefore emphasize the importance of a holistic digital strategy.

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Section: Descriptionmentioning

confidence: 99%

How digitalization affects insurance companies: overview and use cases of digital technologies

Eckert

Osterrieder

2020

ZVersWiss

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“…In their study, Rajan et al, assessed IoT systems that produce Big Data using semantics. 15,16 Similarly, Li et al, explain in detail the solutions and various obvious challenges for IoT security. 17…”

Section: Iot and Big Datamentioning

confidence: 99%

A Smart Data Pre-Processing Approach to Effective Management of Big Health Data in IoT Edge

Kaya¹,

Erdem²,

Güneş³

2021

SHTT

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Background: An IoT Big Data analysis platform should be able to dynamically manage IoT data and it should be appropriate the fundamental components, known as the 5V's of big data. Therefore, speed and accuracy are two important criteria to consider. In this context, there are no similar studies that prioritize speed and accuracy criteria in big health data. It is thought that this study and the experimental results obtained are a new approach in the field of healthcare, hence it will add novelty to the studies to be carried out. The main objective of this paper is to detect anomalies at the edge of IoT for the effective management of big health data. Methods: This study focuses on detecting anomalies on the data stream created with IoT sensors between the sensing and network layer. The classification success and data processing speed of the random cut forest, logistic regression, Naive Bayes, and neural network algorithms used for anomaly detection are compared. In order to detect anomalies in a data stream consisting of temperature, age, gender, weight, height, and time data and compare algorithms. Results: The speed and accuracy performances of ML Algorithms were compared. The performance comparison shows that the LR algorithm will be more successful in IoT systems in terms of speed, although it is very close to the RCF in terms of accuracy. Conclusion:The experimental results show that using ML algorithms on IoT edges will help make effective and timely decisions in the healthcare domain. Thus, the big data generated by the IoT sensing layer in healthcare will be formed at a more manageable level. Also, thanks to this, service providers, users, and other interested sides will be minimally affected by the negative effects of anomalous data.

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“…Big Data = {Data Volume, Data Variety, Data Velocity, Data Value, Data Veracity} (1) For the interpretation of big data, it is essential to understand the classification of data based on their behavior. Basically, big data is widely classified into different types as shown in…”

Section: Big Data Characteristicsmentioning

confidence: 99%

“…Apart from that, we have also used a bulky desktop for processing MBs of data. For repository, we used floppies, then hard disk for storing TBs of data and now storing data in the cloud for taking the edge off [1]. With this enhancement of existing technologies, data has been generated on a large scale.…”

Section: Introductionmentioning

confidence: 99%

A Roadmap Towards Big Data Opportunities, Emerging Issues and Hadoop as a Solution

Qayyum¹

2020

IJEME

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The concept of Big Data become extensively popular for their vast usage in emerging technologies. Despite being complex and dynamic, big data environment has been generating the colossal amount of data which is impossible to handle from traditional data processing applications. Nowadays, the Internet of things (IoT) and social media platforms like, Facebook, Instagram, Twitter, WhatsApp, LinkedIn, and YouTube generating data in various formats. Therefore, this promotes a drastic need for technology to store and process this tremendous volume of data. This research outlines the fundamental literature required to understand the concept of big data including its nature, definitions, types, and characteristics. Additionally, the primary focus of the current study is to deal with two fundamental issues; storing an enormous amount of data and fast data processing. Leading to objectives, the paper presents Hadoop as a solution to address the problem and discussed the Hadoop Distributed File System (HDFS) and MapReduce programming framework for storage and processing in Big Data efficiently. Future research directions in this field determined based on opportunities and several emerging issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal solutions to address Big Data storage and processing problems. Moreover, this study contributes to the existing body of knowledge by comprehensively addressing the opportunities and emerging issues of Big Data.

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The Role of IoT and Big Data in Modern Technological Arena: A Comprehensive Study

Cited by 13 publications

References 11 publications

How digitalization affects insurance companies: overview and use cases of digital technologies

How digitalization affects insurance companies: overview and use cases of digital technologies

A Smart Data Pre-Processing Approach to Effective Management of Big Health Data in IoT Edge

A Roadmap Towards Big Data Opportunities, Emerging Issues and Hadoop as a Solution

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