An Exploratory analysis of Machine Learning adaptability in Big Data Analytics Environments: A Data Aggregation in the age of Big Data and the Internet of Things

Rajendran, Ratchana; Sharma, Pramodita; Saran, Nitin Kumar; Ray, Samrat; Alanya-Beltrán, Joel; Tongkachok, Korakod

doi:10.1109/iciptm54933.2022.9753921

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…According to Rajendran et al (2022), the currency fluctuations affected BYJU'S international operations as the company expanded into global markets. Changes in exchange rates may affect financial performance by driving revenue fluctuations if foreign operations convert their overseas earnings into the reporting currency.…”

Section: Currency Fluctuationsmentioning

confidence: 99%

A case study of BYJU’S failure

Samrat Ray,

Kaviya Jain,

Piyush Birru

et al. 2024

World J. Adv. Res. Rev.

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Once an education IT pioneer, BYJU'S, failure convoluted startup workers, investors. Byju failed due to financial mismanagement. Bad decisions and inconsistent financing prevented the corporation from paying its obligations. Company disagreements and leadership instability hurt BYJU'S operations. Due to negative press, BYJU'S brand suffers, its reputation suffered from data breaches, false advertising, and forced purchases. When the economy is terrible, earning more is tougher. These issues must be addressed for the future of technology. Businesses must be frugal, transparent, and financially aware. Customise marketing and market segmentation to accommodate everyone. Openness to new ideas and flexibility are needed to compete in a fast-changing market. Innovation, perseverance, and morality help Ed-tech overcome problems and capture opportunities. Businesses may learn from BYJU'S errors to grow sustainably, establish trust, and lead in the fast-paced, competitive educational technology business. Ed-tech must innovate after Byju failed. The company couldn't innovate; therefore, it struggled to compete and survive. Edtech companies must be inventive, customer-focused, and adaptable to compete in a competitive, ever-changing business. Learning from setbacks and encouraging resilience and creativity may help ed-tech companies thrive.

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Section: Currency Fluctuationsmentioning

confidence: 99%

A case study of BYJU’S failure

Samrat Ray,

Kaviya Jain,

Piyush Birru

et al. 2024

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

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“…•Maintenance and fault data are collected over time and based on which a prediction is made to infer when the next fault will occur [249] Integrating and adapting Machine Learning for security monitoring in Big Data IoT data aggregation platforms.…”

Section: Processmentioning

confidence: 99%

“…b) Improving composition platforms and processes. This is the most common role among the pointed out AI/ML primary studies aggregated in Table 15, which shows how AI/ML can play a role in improving composition platforms as in [248], where AI/ML is leveraged to improve maintenance of IoT composition platforms and in [249] where AI/ML techniques were used to improve security. To improve the composition processes, in [32] and [33], an AIbased classification method applied to a pipeline consisting of ML services was used to maximize the classification accuracy, leading to an improved service selection process.…”

Section: B) Composite Capabilitiesmentioning

confidence: 99%

IoT Capabilities Composition and Decomposition: A Systematic Review

et al. 2023

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As billions of IoT devices join the Internet, researchers and innovators increasingly explore IoT capabilities achieved via service composition or reuse of existing capabilities via service decomposition. Many systematic literature reviews (SLRs) were produced on this subject; however, two issues remain to be addressed: i) a reference taxonomy of the different aspects of IoT capabilities composition and decomposition is needed, and ii) many formal questions (e.g., standards role, formal representations applications, state-space explosion countermeasures, etc.), technical questions (e.g., composition process types and automation levels synergies, service decomposition categories, the role of AI/ML, etc.), and QoS questions (e.g., privacy, interoperability, and scalability challenges and solutions, etc.) remain unanswered. We introduce this work by discussing notions of IoT capabilities composition and decomposition in a layered IoT architecture while highlighting the strengths and weaknesses of existing SLRs. We identify unanswered questions through gaps in related work and motivate these questions using the PICOC methodology. We explain the search methodology and organize the topic questions using the proposed reference taxonomy. The identified research questions are answered, and trends and gaps that need additional attention from the research community are highlighted. This effort benefits city planners and end-users of IoT systems as it contributes to a better understanding of the role of composition and decomposition of IoT capabilities in building valueadded services or reusing existing ones for resource optimization. For researchers, this effort contributes a reference taxonomy for the topic and sheds light on important questions while highlighting corresponding trends and gaps requiring further attention.

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“…In [13], Xu et al put forward a multiuser mobile-edge computing (MEC) system with latency constraint, which compresses the offloaded data before transmission to reduce the data size. In [14], Rajendran et al extended the well-known microservice design pattern API gateway. In [15], Agarwal et al proposed a structuring data methodology, which provided the option of controlling the compression ratio of the cost at an acceptable distortion, visualizing data.…”

Section: Introductionmentioning

confidence: 99%

PLC-based information aggregation and data compression for distributed PV access

Shi,

Li,

et al. 2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

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With the expanding scale of photovoltaic (PV) power generation installed in the grid, the scattered communication terminals and the large-scale data communication led to the problem that the master station cannot aggregate information from multiple terminals and analyze data efficiently in real time. In this paper, a power line communication (PLC)-based efficient information aggregation and data compression architecture is constructed. On the basis, we propose genetic algorithm-based information aggregation algorithm to optimize PLC channel selection and reduce information aggregation delay. Moreover, we propose a multi-intersection approximation data estimation algorithm to support the master station for regulation and decision-making by improving data compression accuracy. Simulation results show that the proposed algorithm has superior performance in terms of information aggregation delay and data compression accuracy.

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An Exploratory analysis of Machine Learning adaptability in Big Data Analytics Environments: A Data Aggregation in the age of Big Data and the Internet of Things

Cited by 5 publications

References 12 publications

A case study of BYJU’S failure

A case study of BYJU’S failure

IoT Capabilities Composition and Decomposition: A Systematic Review

PLC-based information aggregation and data compression for distributed PV access

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