Distributed Streaming Big Data Analytics for Internet of Things (IoT)

2022 ◽  
pp. 722-757
Author(s):  
Sornalakshmi Krishnan ◽  
Kayalvizhi Jayavel
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cloud Services ◽  
Sensor Data ◽  
Efficient Processing ◽  
Streaming Big Data ◽  
The Internet Of Things

In this chapter, a discussion on the integration of distributed streaming Big Data Analytics with the Internet of Things is presented. The chapter begins with the introduction of these two technologies by discussing their features and characteristics. Discussion on how the integration of these two technologies benefit in efficient processing of IoT device generated sensor data follows next. Such data centric processing of IoT data powered by cloud, services and other enablers will be the architecture of most of the realtime systems involving sensors and real-time monitoring and actuation. The Volume, Variety and Velocity of sensor generated data make it a Big Data scenario. In addition, the data is real time and requires decisions or actuations immediately. This chapter discusses how IoT data can be processed using distributed, scalable stream processing systems. The chapter is concluded with future directions of such real time Big Data Analytics in IoT.

Distributed Streaming Big Data Analytics for Internet of Things (IoT)

2018 ◽  
pp. 303-338 ◽  
Author(s):  
Sornalakshmi Krishnan ◽  
Kayalvizhi Jayavel
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cloud Services ◽  
Sensor Data ◽  
Efficient Processing ◽  
Streaming Big Data ◽  
The Internet Of Things

In this chapter, a discussion on the integration of distributed streaming Big Data Analytics with the Internet of Things is presented. The chapter begins with the introduction of these two technologies by discussing their features and characteristics. Discussion on how the integration of these two technologies benefit in efficient processing of IoT device generated sensor data follows next. Such data centric processing of IoT data powered by cloud, services and other enablers will be the architecture of most of the realtime systems involving sensors and real-time monitoring and actuation. The Volume, Variety and Velocity of sensor generated data make it a Big Data scenario. In addition, the data is real time and requires decisions or actuations immediately. This chapter discusses how IoT data can be processed using distributed, scalable stream processing systems. The chapter is concluded with future directions of such real time Big Data Analytics in IoT.

Big Data Analytics and Its Applications in IoT

2021 ◽  
pp. 146-158
Author(s):  
Shaila S. G. ◽  
Bhuvana D. S. ◽  
Monish L.
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The Internet ◽  
Iot Applications ◽  
Maximum Benefit ◽  
The Internet Of Things ◽  
Achieve Maximum Benefit

Big data and the internet of things (IoT) are two major ruling domains in today's world. It is observed that there are 2.5 quintillion bytes of data created each day. Big data defines a very huge amount of data in terms of both structured and unstructured formats. Business intelligence and other application domains that have high information density use big data analytics to make predictions and better decisions to improve the business. Big data analytics is used to analyze a high range of data at a time. In general, big data and IoT were built on different technologies; however, over a period of time, both of them are interlinked to build a better world. Companies are not able to achieve maximum benefit, just because the data produced by the applications are not utilized and analyzed effectively as there is a shortage of big data analysts. For real-time IoT applications, synchronization among hardware, programming, and interfacing is needed to the greater extent. The chapter discusses about IoT and big data, relation between them, importance of big data analytics in IoT applications.

RELATIONSHIP IN IOT AND BIG DATA ANALYTICS: THE OPPORTUNITIES AND OPEN CHALLENGES

2021 ◽  
Vol 83 (4) ◽  
pp. 100-111
Author(s):  
Ahmad Anwar Zainuddin ◽  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Physical World ◽  
Future Directions ◽  
Wide Range ◽  
Time Utilization ◽  
Relationship Of ◽  
The Internet Of Things

Internet of Things (IoT) is an up-and-coming technology that has a wide variety of applications. It empowers physical objects to be organized in a specialized framework to grow its convenience in terms of ease and time utilization. It is to convert the thought of bridging the crevice between the physical world and the machine world. It is also being use in the wide range of the technology in this current situation. One of its applications is to monitor and store data over time from numerous devices allows for easy analysis of the dataset. This analysis can then be the basis of decisions made on the same. In this study, the concept, architecture, and relationship of IoT and Big Data are described. Next, several use cases in IoT and big data in the research methodology are studied. The opportunities and open challenges which including the future directions are described. Furthermore, by proposing a new architecture for big data analytics in the Internet of Things, this paper adds value. Overall, the various types of big IoT data analytics, their methods, and associated big data mining technologies are discussed.

scholarly journals Requirements for a Product Lifecycle Management System Using Internet of Things and Big Data Analytics for Product-as-a-Service

2021 ◽  
Vol 2 ◽  
Author(s):  
Tomohiko Sakao ◽  
Alex Kim Nordholm
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Product Lifecycle Management ◽  
Product Lifecycle ◽  
Use Phase ◽  
Lifecycle Management ◽  
Original Equipment Manufacturers ◽  
The Internet Of Things

Product-as-a-service (PaaS) offerings have advantages and potential for transforming societies to a circular economy and for improving environmental performance. Original equipment manufacturers providing PaaS offerings take higher responsibility for product performances in the use phase than those selling products. This responsibility can be supported by digital technologies such as the Internet of Things (IoT) and big data analytics (BDA). However, insights on how data of product designs and in-use services are managed for PaaS offerings in product lifecycle management (PLM) software are scarce. This mini-review first gives an account of extant major research works that successfully applied BDA, a specific technique of artificial intelligence (AI), to cases in industry through a systematic literature review. Then, these works are analyzed to capture requirements for a PLM system that will exploit the IoT and BDA for PaaS offerings. The captured requirements are summarized as (1) facilitate product and service integration, (2) address multiple lifecycles, (3) adopt an ontology approach encompassing several product standards, and (4) include reading data to process in an interoperation layer.

Big Data Analytics in Bioinformatics

Biotechnology ◽  
2019 ◽  
pp. 1967-1984
Author(s):  
Dharmendra Trikamlal Patel
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Annual Growth ◽  
Predictive Analysis ◽  
The Internet ◽  
Data Generation ◽  
Intelligent Devices ◽  
The Internet Of Things

Voluminous data are being generated by various means. The Internet of Things (IoT) has emerged recently to group all manmade artificial things around us. Due to intelligent devices, the annual growth of data generation has increased rapidly, and it is expected that by 2020, it will reach more than 40 trillion GB. Data generated through devices are in unstructured form. Traditional techniques of descriptive and predictive analysis are not enough for that. Big Data Analytics have emerged to perform descriptive and predictive analysis on such voluminous data. This chapter first deals with the introduction to Big Data Analytics. Big Data Analytics is very essential in Bioinformatics field as the size of human genome sometimes reaches 200 GB. The chapter next deals with different types of big data in Bioinformatics. The chapter describes several problems and challenges based on big data in Bioinformatics. Finally, the chapter deals with techniques of Big Data Analytics in the Bioinformatics field.

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