Computational Business Intelligence, Big Data, and Their Role in Business Decisions in the Age of the Internet of Things

Web Services ◽  
2019 ◽  
pp. 1048-1067
Author(s):  
Javier Vidal-García ◽  
Marta Vidal ◽  
Rafael Hernández Barros
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Business Intelligence ◽  
The Internet ◽  
Self Assessment ◽  
Business Decisions ◽  
Business Operations ◽  
Large Databases ◽  
The Social ◽  
The Internet Of Things

The evolution of the big data and new techniques related to the processing and analysis of large databases is revolutionizing the management of companies in the age of the Internet of Things (IoT). In this chapter, we examine the possibilities of big data to improve the services offered by companies and the customer experience and increase the efficiency of these companies. Companies must accept the challenge of self-assessment and measure the barriers that threaten to prevent them from reaching to get the maximum potential derived from big data and analytics. The combination of big data and computational business intelligence will change completely processes, logistics and distribution strategies, the choice of marketing channels and any aspect of the production and marketing of products and services. A case of GE is presented to showcase the use of the IoT and big data. All companies, regardless of size or sector, will improve their business operations due to big data generated from the social media and IoT applications and its use in computational business intelligence.

Computational Business Intelligence, Big Data, and Their Role in Business Decisions in the Age of the Internet of Things

2017 ◽  
pp. 249-268 ◽  
Author(s):  
Javier Vidal-García ◽  
Marta Vidal ◽  
Rafael Hernandez Barros
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Business Intelligence ◽  
The Internet ◽  
Self Assessment ◽  
Business Decisions ◽  
Business Operations ◽  
Large Databases ◽  
The Social ◽  
The Internet Of Things

The evolution of the big data and new techniques related to the processing and analysis of large databases is revolutionizing the management of companies in the age of the Internet of Things (IoT). In this chapter, we examine the possibilities of big data to improve the services offered by companies and the customer experience and increase the efficiency of these companies. Companies must accept the challenge of self-assessment and measure the barriers that threaten to prevent them from reaching to get the maximum potential derived from big data and analytics. The combination of big data and computational business intelligence will change completely processes, logistics and distribution strategies, the choice of marketing channels and any aspect of the production and marketing of products and services. A case of GE is presented to showcase the use of the IoT and big data. All companies, regardless of size or sector, will improve their business operations due to big data generated from the social media and IoT applications and its use in computational business intelligence.

scholarly journals Introduction to Urban Big Data Infrastructure

2021 ◽  
pp. 543-545
Author(s):  
Michael F. Goodchild
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Social Issues ◽  
Geospatial Data ◽  
The Internet ◽  
Data Infrastructure ◽  
Spatial Search ◽  
3D Data ◽  
The Social ◽  
The Internet Of Things

AbstractThis chapter provides a brief introduction to Part IV of the book and its focus on urban big data infrastructure. Eight chapters (Chaps. 31 to 38) explore the various dimensions of the topic, ranging from massive archives of 3D data and the Internet of Things to spatial search and the social issues of privacy that are raised by big geospatial data.

scholarly journals Financial Big Data Based on Internet of Things and Wireless Network Communication

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lingqi Xue
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Wireless Communication ◽  
Wireless Network ◽  
Social Utility ◽  
Network Communication ◽  
The Internet ◽  
The Social ◽  
Internet Of Things Technology ◽  
The Internet Of Things

With the advent of the era of big data, Internet of things technology and wireless communication technology have been in a state of rapid development. Opportunities and challenges in all walks of life are being subverted. Financial management, as the foundation of corporate governance, is important for improving economic efficiency and achieving sustainable business development which plays an important role. In order to realize the management and classification of financial big data, better identify the financial data of different enterprises, strengthen the safe storage of financial information, and provide early warning for the security issues involved, this article is based on the Internet of things and wireless communication networks. In the method part, this article introduces the framework of the Internet of things, Bluetooth, and infrared data transmission in wireless network communication and the principles of financial big data. The algorithm introduces a single-user MIMO system, free space propagation, and spectrum and energy efficiency. The analysis part analyzes the spectrum efficiency of different algorithms, social utility, average number of retransmissions, comprehensive scores of competitiveness in various fields of the Internet of things, and the significance of financial indicators. By comparing the data, it can be seen that the algorithm in this paper is superior to the two algorithms of IAN-CoMP and IA-CoMP. When the number of users is 100, the social utility of the algorithm in this paper is 4.45, while IAN-CoMP is 3.43 and IA-CoMP is 3.67. When the number of users increases to 700, the social utility of the algorithm in this paper is 28.34. The other two algorithms are, respectively, 24.45 and 25.99, and we know that the social utility of the algorithm in this paper is the best. Through comprehensive analysis, it is concluded that the financial big data model based on the Internet of things and wireless network communication in this paper can better realize data management and collection, so as to meet the needs of information developers.

scholarly journals Epistemological Equation for Analysing Uncontrollable States in Complex Systems: Quantifying Cyber Risks from the Internet of Things

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Pete Burnap ◽  
Omar Santos
Keyword(s):  
Risk Assessment ◽  
Internet Of Things ◽  
Empirical Analysis ◽  
Quantitative Risk Assessment ◽  
The Internet ◽  
Self Assessment ◽  
Target State ◽  
Epistemological Analysis ◽  
Cyber Risk ◽  
The Internet Of Things

AbstractThe Internet-of-Things (IoT) triggers data protection questions and new types of cyber risks. Cyber risk regulations for the IoT, however, are still in their infancy. This is concerning, because companies integrating IoT devices and services need to perform a self-assessment of its IoT cyber security posture. At present, there are no self-assessment methods for quantifying IoT cyber risk posture. It is considered that IoT represent a complex system with too many uncontrollable risk states for quantitative risk assessment. To enable quantitative risk assessment of uncontrollable risk states in complex and coupled IoT systems, a new epistemological equation is designed and tested though comparative and empirical analysis. The comparative analysis is conducted on national digital strategies, followed by an empirical analysis of cyber risk assessment approaches. The results from the analysis present the current and a target state for IoT systems, followed by a transformation roadmap, describing how IoT systems can achieve the target state with a new epistemological analysis model. The new epistemological analysis approach enables the assessment of uncontrollable risk states in complex IoT systems—which begin to resemble artificial intelligence—and can be used for a quantitative self-assessment of IoT cyber risk posture.

Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

2021 ◽  
Vol 21 (3) ◽  
pp. 1-22
Author(s):  
Celestine Iwendi ◽  
Saif Ur Rehman ◽  
Abdul Rehman Javed ◽  
Suleman Khan ◽  
Gautam Srivastava
Keyword(s):  
Internet Of Things ◽  
Detection System ◽  
Experimental Testing ◽  
Cyber Attacks ◽  
Internet Technology ◽  
The Internet ◽  
Business Operations ◽  
Private Data ◽  
The Internet Of Things ◽  
Better Than

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

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