Research on Face Recognition Algorithms in the Context of Power Internet of Things

Na, Qionglan; Su, Dan; Yang, Yixi; Lou, Jing; Wu, Jia; Zeng, Jing

doi:10.1007/978-981-33-6609-1_27

Cited by 1 publication

(1 citation statement)

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“…Besides, self-service analytics can foster a data-driven culture within an organization, encouraging employees to explore and gain insights from data to drive innovation and competitive advantage (Thomas Publishing Company LLC, 2010). By enabling users to perform data analysis tasks on their own, self-service analytics reduces the burden on IT departments, allowing them to focus on more strategic initiatives (Vo, Q. D. et Literature Review SSA has been explored in various contexts, including the oil industry Akoum & Mahjoub (2013), power internet of things (Na et al, 2021), and offshore drilling. For example, Akoum and Mahjoub (2013) proposed a unified framework for implementing business intelligence, real-time operational intelligence, and big data analytics for upstream oil industry operators.…”

Section: Introductionmentioning

confidence: 99%

Research Trends on Self-service Analytics: A Bibliometric Review from year 2010 -2023

Yi,

Abdullah

2024

IJARBSS

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In the era of digital economy, massive data not only becomes an important asset, but also brings many challenges to enterprises. Self-service analytics (SSA) was born to solve these problems, which refers to the centralized data control and data distribution by enterprises through IT, and the use of data by business personnel without barriers. SSA is considered as a type of business intelligence. How to release data value through self-service data analysis has become the key to enterprise digital transformation. As a result, comprehending SSA has become essential in the development of digital transformation. This research paper presents a comprehensive bibliometric analysis of the emerging field of Self-service Analytics (SSA). The authors examine 69 publications from the Scopus database over a 14-year period, revealing a growing interest in SSA with an annual growth rate of 8.82%. The majority of these publications are theoretical solution papers, indicating that SSA is still in its early stages of theory with limited practical implementation. Given the apparent constraint of obtaining data from a single database, this paper recommend that future studies should examine the research outputs of SSA using other databases such as Google Scholar, Scopus, and Web of Science.

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Section: Introductionmentioning

confidence: 99%