JANOLI International Journal of Big Data
The JANOLI International Journal of Big Data (JIJBD) is a peer-reviewed, open-access publication that publishes articles monthly. It serves as a platform for researchers, data scientists, and industry professionals to share innovative findings, methodologies, and applications in big data. As a globally recognized journal, it covers fundamental theories, advanced analytics, and real-world implementations of big data across various industries. Additionally, the journal plans to feature special issues on emerging trends and challenges in big data and its applications. Multidisciplinary subjects: Scalable Data Architectures and Storage Distributed Systems and Parallel Computing Real-time Analytics and Stream Processing Data Lakes and Data Warehousing Machine Learning with Big Data Privacy and Security in Big Data Internet of Things (IoT) Data Management Big Data in Smart Cities and Urban Planning SCOPE: Big Data Analytics and Visualisation Machine Learning and AI for Big Data Data Mining and Knowledge Discovery Scalable and Distributed Computing Cloud and Edge Computing in Big Data Data Privacy, Security, and Ethics Internet of Things (IoT) and Sensor Data Processing Real-time Data Processing and Streaming Analytics Big Data Applications in Healthcare, Finance, and Business Blockchain and Big Data Integration International Collaboration in Big Data Research
Shanti Educational Research Foundation, India
Enhancing Speech Compression by Intra-Inter Broad Attention: The Case of IBACodec
This study investigates the improvements in speech compression efficiency, particularly at low bitrates, by addressing the challenges of redundancy removal and enhancing context awareness. The research proposes IBACodec, a novel codec that leverages advanced attention mechanisms, such as the intra-inter broad transformer, and a dual-branch conformer for efficient redundancy elimination. Five core hypotheses were tested: the impact of context awareness, the effectiveness of dual-branch modeling, a comparative analysis with existing codecs, subjective evaluations, and objective metric performance. Results demonstrate that IBACodec outperforms traditional codecs like SoundStream and Opus in compression efficiency and quality at lower bitrates. Furthermore, subjective assessments reveal superior performance at low bitrates, while objective metrics such as ViSQOL, LLR, and CEP also confirm the codec's advantages. This research highlights the potential of IBACodec as a leading solution in speech compression, emphasizing the role of advanced machine learning techniques in enhancing codec performance.
Improving Table Extraction Accuracy and Automation for PDF-based Journal Articles
The paper provides insights into the obstacles in automatic table extraction from PDF-based journal articles with a focus on optimizing detection accuracy and minimizing the loss of information. The impact of the text size, border length, absolute location, and hierarchical clustering on compared performance with the previously developed solutions is studied. This paper adopted a quantitative research approach to explore how changes in independent variables influence detection accuracy and extraction efficiency. The results show that optimized text size and flexible border length greatly improve the detection and restoration of table structures, while hierarchical clustering improves the accuracy of table structures. The proposed method outperforms previous techniques in terms of reducing information loss and improving efficiency, and it is promising for automated data extraction in academic documents.
Improving Few-Shot Multi-Hop Reasoning in Temporal Knowledge Graphs with Reinforcement Learning
This problem has unique challenges for few-shot multi-hop reasoning in TKGs, considering that the graph is dynamic and previous methods mainly focused on static graphs. In this paper, a reinforcement learning framework is integrated with advanced path search strategies to enhance the accuracy of reasoning, entity representation of tasks, and interpretability in TKGs. Five research hypotheses are considered: the effect of reinforcement learning, the contribution of one-hop neighbors, the efficacy of path search strategies, the relationship between the existing paths and the current state, and the contribution of path analysis to better interpretability. Quantitative methodologies are used with benchmark datasets, such as ICEWS18-few, ICEWS14-few, and GDELT-few. The results indicate that the framework improves the reasoning process and reduces computational complexity. These findings address the current gaps in TKG reasoning research and lay a foundation for advancing dynamic reasoning approaches in knowledge graphs.
Enhancing Analysis of Earnings Calls: A Self-Supervised Approach to Extractive Summarization with ECT-SKIE
Earnings conference calls are among the most significant sources of information regarding a firm's financial performance and strategic outlook. However, growing transcripts length make it difficult to manually analyze. This paper investigates the potential of ECT-SKIE, a self-supervised extractive summarization model, in addressing these challenges. This work systematically assesses the performance of ECT-SKIE in extracting key insights, the efficiency of ECT-SKIE compared with traditional methods, and the application of advanced techniques such as variational information bottleneck theory and structure-aware contrastive learning to improve the model's performance. Besides, the effectiveness of the container-based key sentence extractor in redundancy reduction is emphasized. A large-scale dataset of U.S. market earnings call transcripts is leveraged to verify the model with the ability of ECT-SKIE in significantly improving the accuracy, efficiency, and clarity of the extraction, making it a standard for automated financial analysis.
Interpretable Legal Judgment Reasoning Framework: Improving Forecasts of Case Outcomes with Multi-Source Knowledge
This paper presents an interpretable legal judgment reasoning framework that aims to improve both the accuracy and interpretability of legal judgment predictions. The framework covers five key areas: limitations of existing methods, the role of factual logic in judgments, integration of external legal knowledge, handling of long-tail and ambiguous cases, and overall interpretability. The methodology adopted is qualitative, involving experimental data analysis and user feedback. This framework combines factually based logic with legal knowledge using a chain prompt reasoning module and a contrastive knowledge fusing module. Therefore, the result shows considerable improvement in terms of prediction accuracy and interpretability. These advances will fill important gaps in the existing literature on LJP research and represent a dynamic, transparent approach to judicial decision-making
Exploring Blockchain-Based Solutions for Secure Multi-Party Data Sharing in Emergency Management
This study looks into the role blockchain technology can play in improving the safe sharing of data between various parties in emergency management. It seeks to explore how blockchain, coupled with attribute-based access control, can address the issues in centralized data platforms and transform the nature of decentralized data governance. The five core research questions focus on improving data security and privacy, the role of ABAC in dynamic policy adaptation, data sharing efficiencies and reliability, minimizing risks of single-point failures and unauthorized access, and the feasibility of using Hyperledger Fabric for real-world applications. By applying a quantitative research approach, the study assesses how blockchain and ABAC can influence data security, policy adaptation, and system reliability in emergency management. The results are confirmed in terms of significant improvements in data security due to blockchain; the ease of dynamic policy adaptation; better efficiency in sharing data; risk mitigation; and validation of the feasibility of using Hyperledger Fabric for applications in emergency management. This research contributes to the understanding of decentralized data governance and underscores the importance of blockchain in enhancing emergency response systems.
Enhancing Speech Codec Efficiency with Intra-Inter Broad Attention Mechanism
This paper introduces a new approach in speech compression through advanced attention mechanisms, integration of LSTM, and dual-branch conformer structures for optimizing codec efficiency. The study focuses on five research questions, which are: intra-inter broad attention, multi-head attention networks, LSTM for sequence modeling, redundancy elimination, and comparative performance of IBACodec against traditional codecs. The study uses a quantitative methodology with performance metrics that include bitrate efficiency and quality evaluation. Results confirm that IBACodec significantly enhances context awareness, compression efficiency, sequence modeling, and redundancy elimination compared to existing solutions. These findings position IBACodec as a leading solution for speech compression. Further research is needed to explore real-time applications and broader datasets.
Enhancing Few-Shot Multi-Hop Reasoning in Temporal Knowledge Graphs through Reinforcement Learning
This research delves into the challenges and advances of few-shot multi-hop reasoning for Temporal Knowledge Graphs (TKGs), particularly in the combination of reinforcement learning and path search strategies. The central research question investigates the efficiency of a new few-shot multi-hop reasoning model called TFSM, which employs reinforcement learning for TKGs. The study addresses five sub-research questions on the issues of model interpretability, entity representation, path search strategy, comparative performance, and the contribution of individual model components. A quantitative methodology has been used in this work, using datasets such as ICEWS18-few, ICEWS14-few, and GDELT-few to analyze the performance of the TFSM model. Results. It shows that reinforcement learning considerably enhances interpretability, one-hop neighbors improve the entity representation, path search strategies decrease node complexity, and TFSM outperforms baseline methods in few-shot scenarios. This work contributes to the advancement of knowledge on few-shot reasoning in TKGs and presents further research directions for improving model components and broadening application.
Enhancing Legal Judgment Prediction: An Interpretable Framework with Multi-Source Knowledge
This paper studies the challenges and progressions of Legal Judgment Prediction, in particular on the grounds of improving efficiency, accuracy, and fairness in judicial systems. The study examines five sub-research questions: the limitation of existing LJP methods, the role of factual logic in judgment reasoning, the integration of external legal knowledge, the effectiveness of a chain prompt reasoning module, and the impact of contrastive knowledge fusion on long-tail cases. A qualitative research methodology is followed to design and validate an interpretable framework for LJP, featuring a chain prompt reasoning module to strengthen factual logic and a contrastive knowledge fusing module to incorporate external legal knowledge. Results indicate notable improvements in the prediction accuracy, interpretability, and handling of complex long-tail cases. Despite the specific datasets used, the proposed framework is a demonstration of its potential in wider applications and theoretical contributions to legal AI. Future work will be based on diverse data sources and methodologies to generalize and improve these findings.
Enhancing Multi-Chain Data Sharing: The GAM Framework for Scalability and Efficiency
This study will assess how effective the GAM is for solving scalability and efficiency issues in multi-chain data sharing. Using qualitative research methods, the five sub-questions answered deal with some of the shortcomings of models currently in place, potential improvements in terms of scalability and efficiency, hybrid storage options, and whether GAM really applies to the real world. Thematic analysis of blockchain transaction logs and user feedback provides the following key findings: GAM significantly enhances scalability via virtual group formation, optimizes efficiency through on-chain and off-chain processes, and demonstrates practical advantages with hybrid storage. This study concludes that GAM indeed offers robust solutions to problems in multi-chain data sharing, outperforming traditional models. However, it needs further testing across diverse blockchain environments and integration with emerging blockchain technologies for its broader adoption.
Adaptive Distributed Deep Learning Framework for Real-Time Predictive Maintenance in Industrial IoT Environments
This paper presents an adaptive distributed deep learning framework designed for real-time predictive maintenance within Industrial Internet of Things (IIoT) environments. The framework addresses the challenges of processing massive, high-velocity data streams generated by industrial sensors. We propose a novel architecture that combines edge computing with cloud-based deep learning, enabling real-time anomaly detection and predictive failure analysis. The framework incorporates an adaptive learning mechanism that dynamically adjusts model parameters based on the evolving characteristics of the data stream, ensuring sustained accuracy and robustness. We evaluate the performance of the proposed framework using a real-world industrial dataset and demonstrate its superiority over existing methods in terms of prediction accuracy, latency, and resource utilization.
Leveraging Distributed Deep Learning and Feature Engineering for Enhanced Predictive Maintenance in Industrial IoT Big Data
The advent of the Industrial Internet of Things (IIoT) has led to an explosion of sensor-generated data within industrial environments, creating new opportunities and challenges for predictive maintenance (PdM). Traditional statistical and machine learning approaches often fall short in addressing the scale, complexity, and real-time demands of IIoT data. This paper introduces a novel, scalable framework that integrates distributed deep learning with advanced feature engineering to enhance PdM performance. Leveraging Apache Spark for large-scale data processing and TensorFlow for distributed training, we develop a Long Short-Term Memory (LSTM) model tailored for time-series prediction of equipment failures. Our methodology extracts meaningful features from raw sensor data to improve model accuracy and reliability. Experimental evaluation on a simulated industrial dataset demonstrates that our approach significantly outperforms conventional PdM techniques in prediction accuracy, false positive reduction, and operational efficiency. The proposed framework illustrates the transformative potential of distributed deep learning and feature engineering in realizing robust PdM solutions for IIoT-driven industries.
Federated Learning with Differential Privacy for Enhanced Predictive Modeling in Healthcare: A Big Data Approach
The increasing volume and complexity of healthcare data present both opportunities and challenges for predictive modeling. While big data analytics holds immense promise for improving diagnosis, treatment, and patient outcomes, the sensitive nature of medical information necessitates stringent privacy safeguards. This paper proposes a novel approach that combines Federated Learning (FL) with Differential Privacy (DP) to enable collaborative model training across multiple healthcare institutions without directly sharing patient data. We develop and evaluate a framework that allows for distributed model training while ensuring patient privacy through the application of DP mechanisms during the aggregation of model updates. Our results demonstrate that this approach can achieve comparable predictive performance to centralized training while significantly mitigating privacy risks. We analyze the trade-off between privacy and accuracy and provide insights into the optimal configuration of DP parameters for healthcare applications. The proposed framework offers a practical and scalable solution for leveraging the power of big data in healthcare while upholding ethical and legal obligations related to data privacy.
A Hybrid Deep Learning Framework for Anomaly Detection in High-Dimensional Streaming Data: Integrating Autoencoders and LSTM Networks
The rapid growth of data generation, particularly in streaming environments, presents significant challenges for anomaly detection. High-dimensionality, temporal dependencies, and the sheer volume of data necessitate sophisticated approaches. This paper proposes a novel hybrid deep learning framework that integrates the strengths of autoencoders and Long Short-Term Memory (LSTM) networks for anomaly detection in high-dimensional streaming data. The autoencoder component reduces dimensionality and extracts salient features, while the LSTM network models temporal dependencies to identify deviations from normal patterns. The framework is evaluated on a real-world network traffic dataset and compared with state-of-the-art anomaly detection methods. The results demonstrate that the proposed hybrid approach achieves superior performance in terms of accuracy, precision, recall, and F1-score, offering a robust and efficient solution for anomaly detection in complex big data environments.
Federated Learning for Enhanced Intrusion Detection in IoT Networks: A Privacy-Preserving and Scalable Approach
The proliferation of Internet of Things (IoT) devices has led to a surge in network traffic and potential security vulnerabilities, making intrusion detection systems (IDS) crucial for protecting IoT infrastructure. However, traditional centralized IDS approaches face challenges in handling the volume, velocity, and variety of IoT data, while also raising privacy concerns due to the collection and storage of sensitive network information. This paper proposes a federated learning (FL) framework for enhanced intrusion detection in IoT networks. FL enables collaborative model training across decentralized IoT devices without directly sharing raw data, thus addressing privacy concerns. The proposed framework utilizes a deep learning model trained in a federated manner to detect various types of network intrusions. We evaluate the performance of our approach using a simulated IoT network environment and demonstrate its effectiveness in detecting intrusions while preserving data privacy and achieving scalability. The results show that the FL-based IDS achieves comparable or superior performance to centralized approaches, while significantly reducing the risk of data breaches and complying with data privacy regulations. Finally, we discuss the challenges and future directions for applying FL in IoT security.
Implementing GDPR-Compliant Solutions for Genomics Data Analysis on HPC Cloud Infrastructure
The rapid advancement of genomics data analysis on High-Performance Computing (HPC) cloud infrastructures presents unique challenges in ensuring compliance with the General Data Protection Regulation (GDPR). This study examines GDPR-compliant solutions for large-scale genomics data analysis, focusing on the strategies employed by the CINECA supercomputing center. Key research questions explore data security, compliance measures, efficient data processing, integration of diverse diagnostic data, and scalability across institutions. A quantitative research approach is employed, analyzing relationships between data security measures and compliance rates. Findings indicate that dynamic GDPR-compliant protocols, advanced encryption, optimized data processing, standardized integration processes, and adaptable frameworks significantly enhance security, efficiency, and compliance. The study contributes to the field by bridging gaps in scalable and secure genomics data processing, offering insights into sustainable GDPR-compliant implementations.
Multimodal Text-Emoji Fusion for Enhanced Emotion Detection in Online Communication
This paper explores the integration of emoji analysis into text-based emotion detection, emphasizing the significance of multimodal fusion in online communication. With the increasing use of emojis as emotional cues, understanding their impact on sentiment classification is crucial. The study investigates five key areas: the effect of emoji usage on emotion detection accuracy, the role of emojis in differentiating supportive and contrastive sentiments, the impact of emoji context on sarcasm interpretation, the integration of emojis in hybrid deep learning frameworks, and the effectiveness of multimodal fusion techniques in enhancing emotion classification. Using a quantitative research approach, this study leverages the GoEmotions dataset to analyze the relationship between emoji usage and emotion detection performance. Findings demonstrate that incorporating emojis significantly improves classification accuracy, sentiment differentiation, and sarcasm interpretation. Additionally, hybrid frameworks integrating emojis enhance emotion detection capabilities, and multimodal fusion techniques improve classification performance. The research contributes to the growing field of emotion detection by highlighting the essential role of emojis in enriching sentiment analysis models. Future work should address dataset diversity and cultural factors to refine emotion detection frameworks further.
Federated Learning-Driven IoT System for Automated Freshness Monitoring in Resource-Constrained Vending Carts
Food spoilage in vending carts leads to economic losses and health risks, particularly in resource-constrained environments. This study proposes a Federated Learning-driven IoT System to enable automated freshness monitoring in vending carts while preserving data privacy. The research investigates five key aspects: (1) the role of IoT sensors in environmental monitoring, (2) the effectiveness of machine learning models in freshness classification, (3) the capability of federated learning in ensuring data privacy, (4) comparative performance of federated learning techniques, and (5) the impact of ensemble methods on system robustness. Data from 2020 to 2023, collected from vending carts equipped with IoT sensors, were analyzed using machine learning and federated learning frameworks. The findings demonstrate that advanced IoT sensor integration improves environmental monitoring accuracy, ensemble-based machine learning models enhance freshness classification, and federated learning effectively maintains data privacy. Furthermore, stacking ensemble methods outperform other federated learning techniques, providing higher accuracy and robustness. The results emphasize the potential of federated learning in optimizing freshness monitoring while ensuring privacy and reliability, addressing critical gaps in food safety and IoT-based monitoring systems.