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Big data present new opportunities for modern society while posing challenges for data scientists. Recent advancements in sensor networks and the widespread adoption of the Internet of Things (IoT) have led to the collection of physical-sensor data on an enormous scale. However, significant challenges arise in conducting high-quality data analytics within the realm of big data. To uncover big data challenges and enhance data quality, it is essential to quantitatively unveil data characteristics. Furthermore, the existing studies lack analysis of the specific time-related characteristics of physical-sensor data. Enhancing the efficiency and precision of data analytics through the big data lifecycle requires a comprehensive understanding of data characteristics to address the hidden big data challenges. To fill in the research gap, this paper proposes a systematic data characteristic framework based on a 6Vs model. The framework aims to unveil the data characteristics in terms of data volume, variety, velocity, veracity, value, and variability through a set of statistical indicators. This model improves the objectivity of data characteristic understanding by relying solely on data-driven indicators. The indicators related to time-related characteristics in physical-sensor data are also included for the analysis of temporal aspects in the physical-sensor data. Furthermore, the big data challenges are linked to each dimension of the 6Vs model to gain a quantitative understanding of the data challenges. Finally, a pipeline is developed to implement the proposed framework, and two case studies are conducted to illustrate the process of understanding the physical-sensor data characteristics and making recommendations for data preprocessing to address the big data challenges. The proposed framework is able to analyze the characteristics of all physical-sensor data, therefore, identifying potential challenges in subsequent analytics, and providing recommendations for data preprocessing. Furthermore, the characteristic indicators can be used to analyze other types of big data. [ABSTRACT FROM AUTHOR]

The rapid advancement of IoT technology presents transformative opportunities across various sectors, with education being a prominent beneficiary. Smart classrooms, a product of IoT integration, are being widely adopted to create technology-enhanced, student-centric learning environments that cater to students' information literacy needs, particularly during events like pandemics. This widespread adoption generates substantial amounts of educational data, commonly known as big data, necessitating innovative solutions for analysis and utilization. To solve these challenges, this paper proposes utilizing the Apriori algorithm—a data mining technique renowned for uncovering valuable patterns and associations within extensive datasets. This paper evaluates the impact of various information resources with differing quality, considering individuals' information literacy skills. Utilizing data mining techniques, it delves into university students' information literacy data, integrating it with the university library resources to establish a data-driven information literacy education model. It then focuses on criteria, components, and effective methods for instructing college students in information literacy. Finally, a diverse group of students, from first-year undergraduates to doctoral candidates at a specific university, is studied for their engagement in information literacy instruction. Based on the experimental findings, sophomore students exhibited the highest level of participation at 75.9% accuracy, while postgraduate students received more information literacy training than undergraduates and Ph.D. students. When comparing this method to others, such as SVM, KNN, LR, RF, and DT, it achieved superior performance. Additionally, the quality of information literacy training in university libraries was assessed through three dimensions: student learning, behavior, and achievements. Only junior, senior, and first-year graduate students scored above 4, with scores of 4.18, 4.15, and 4.26, respectively. [ABSTRACT FROM AUTHOR]

Edge computing, artificial intelligence (AI), and machine learning (ML) concepts have become increasingly prevalent in Internet of Things (IoT) applications. As the number of IoT devices continues to grow, relying solely on cloud computing for real-time data processing and analysis is proving to be more challenging. The synergy between edge computing and AI is particularly intriguing due to AI's reliance on rapid data processing, a capability facilitated by edge computing. Edge AI represents a significant paradigm shift, leveraging AI within edge computing frameworks to reduce reliance on internet connections and mitigate data latency issues. This approach accelerates data processing, supporting use cases that demand real-time inference. Additionally, as cloud storage costs continue to rise, the feasibility of streaming and storing large volumes of data comes into question. Edge AI offers a compelling solution by performing big data analytics closer to the end device where edge computing is deployed. This paper presents a systematic literature review (SLR) of 85 articles published between 2018 and 2023 within Edge AI. The study provides a comprehensive examination of the analysis of measurement environments and assesses factors applied to Edge AI for big data. It offers taxonomies specific to Edge AI within the big data domain, presents case studies, and outlines the challenges and open issues inherent in Edge AI for big data. [ABSTRACT FROM AUTHOR]

This document is a special issue of the journal "Neural Computing & Applications" focused on machine learning and big data analytics for IoT security and privacy. The Internet of Things (IoT) is a network of connected devices that presents challenges for security and analysis due to the large amounts of data involved. The issue includes selected papers from the 3rd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT2022), as well as additional submissions. The papers cover a range of topics including prototype selection, scheduling optimization, deep learning, text reading, task-technology matching, financial incentives, state estimation for robots, image recognition, speech recognition, and term extraction algorithms. The authors express gratitude to the editor, staff, authors, and reviewers involved in the creation of this issue. [Extracted from the article]

In recent years, technological improvements have provided remarkable possibilities for examining the association between education and mental health. However, an in-depth analysis is still needed to assess the impact of specific educational interventions on students. This paper proposes a new approach to measure music education’s positive impact on students by utilizing the desirable features of Big Data and the Internet of Things (IoT). Dependent on data collection, feature extraction, and predictive modeling with built-in Recurrent Neural Networks (RNNs) for LSTM layers, the proposed approach correlates and gives insights into the link between musical education and mental health. The framework forms the basis for thorough analysis by utilizing sensors to monitor physiological parameters such as heart rate and other relevant parameters concerning environment monitoring. The proposed model offers descriptive, diagnostic, predictive, and prescriptive insights using RNNs to predict data patterns, predict mental health trends, and utilize big data analytics to pinpoint influential factors. The study improves students’ mental health during music lessons by enabling the training of RNN models on the captured data, which revealed notable correlations between musical engagements. Furthermore, predictive analytics recommend personalized interventions to boost mental health benefits. The experimental results highlight the efficacy of technology-enhanced music education, with the proposed model showcasing superior performance metrics: accuracy (0.886), sensitivity (0.823), specificity (0.901), F1 score (0.841), MCC (0.724), MSE (0.106), MAE (0.261), and RMSE (0.321). The proposed model’s superiority validates comparative analysis with various learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, K-Nearest Neighbors, Support Vector Machine, and Neural Network. [ABSTRACT FROM AUTHOR]

This study aims to demonstrate a novel cloud big data system, s2Cloud, to enabled both smart and secure transmission and management of patient big data from Internet of Things (IoT), especially in the new common of long covid. More specifically, s2Cloud allows doctors to manage patient records effectively through the interactive web server. It further enables real-time, long-term, and continuous streaming and management of biomechanical dynamics from IoT phones/wearables. The big data can be visualized in both real-time and historical modes. Besides, s2Cloud achieves big data security through secure sign up and log in, as well as data transmission protection. It also allows doctor-patient interactions through instantaneous chat. s2Cloud has been evaluated on the real-world application, in which human IoT biomechanical dynamics are streamed, managed, stored, and visualized to demonstrate the effectiveness. Overall, the proposed system, empowered by smart and secure design innovations, has demonstrated the feasibility and potential for IoT big data-driven precision health. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), an enlarged Internet-based network, is a key element of the next information technology revolution. With the evolution of numerous IoT based smart applications like smart city, healthcare, huge amount of heterogenous data called big data with varying volume, velocity and variety is getting space on various storage systems. Data redundancy is a serious problem that wastes a lot of storage capacity and network bandwidth with less data security in setups that blend cloud integrated IoT data storage. Data deduplication/redundancy elimination strategies can effectively decrease and control this issue by removing duplicate data in cloud-integrated IoT storage systems. Security and privacy of data is another major concern. To maximise the storage effectively and extremely securely, with maintained data integrity, confidentiality, minimal storage cost and increased storage use, data deduplication (DD) over encrypted data is also a key problem in cloud integrated IoT storage and computing environment. With the dynamic nature of big data, the majority of current data deduplication techniques which primarily revolve around the static scenes like the backup and archive systems, are inappropriate to be applicable for real time scenarios in IoT environment. To overcome the aforementioned issues this survey presents an analysis of literature on conventional deduplication techniques. It is highlighting the need of deduplication in IoT oriented big data, its parameters and properties of effectiveness, with taxonomy for secure and conventional deduplication and scope of implementing it with new technologies; Blockchain. Further, it elaborates on issues and challenges along with future scope of deduplication schemes in various application domains. [ABSTRACT FROM AUTHOR]