Efficient big data management is a dire necessity to manage the exponential growth in data generated by digital information systems to produce usable knowledge. Structured databases, data lakes, and warehouses have each provided a solution with varying degrees of success. However, a new and superior solution, the data Lakehouse, has emerged to extract actionable insights from unstructured data ingested from distributed sources. By combining the strengths of data warehouses and data lakes, the data Lakehouse can process and merge data quickly while ingesting and storing high-speed unstructured data with post-storage transformation and analytics capabilities. The Lakehouse architecture offers the necessary features for optimal functionality and has gained significant attention in the big data management research community. In this paper, we compare data lake, warehouse, and lakehouse systems, highlight their strengths and shortcomings, identify the desired features to handle the evolving challenges in big data management and analysis and propose an advanced data Lakehouse architecture. We also demonstrate the performance of three state-of-the-art data management systems namely HDFS data lake, Hive data warehouse, and Delta lakehouse in managing data for analytical query responses through an experimental study.

During the COVID-19 pandemic, the traditional emergency healthcare systems faced unprecedented strain due to the sharp rise in demands for urgent care, scarcity of resources, and increased risks of people getting infected while waiting at the emergency care facility. We present Triage-Bot, an online medical triage provisioning service, that can revolutionize emergency care by decreasing the load on emergency departments (ED), reducing healthcare expenses, and improving the quality of care. Empowered by artificial intelligence and natural language processing, the Triage-Bot service assesses and prioritizes patients' needs based on symptoms, medical history, and perceived conditions from multimodal video, audio, and text data captured during patients' interactions. The captured summarized information with a severity ranking is sent to a human expert to suggest the next action on the user's part. The diverse data types used by the Triage-Bot in communication, authentication, data collection, storage, and analytics requires a robust and scalable system architecture for online service provisioning. In this paper, we specifically focus on the system design and architecture of the Triage-Bot for emergency healthcare settings. With integrated electronic medical records (EMR) and online platforms, the bot fosters collaboration among healthcare professionals and enables swift and informed decision-making even in the face of crises. By partially automating and offering a hybrid triage process, the Triage-Bot improves resource allocation, reduces healthcare management costs for emergency care, minimizes patient waiting times, and improves wellbeing. To address the complexities and demands of healthcare data management, our proposed system incorporates MongoDB database for flexibility, scalability, and versatility in supporting different types of data. Additionally, we implement a data linking and analytics pipeline utilizing a data Lakehouse system to effectively ingest, manage, process, and generate knowledge from heterogeneous data sources.

With the advancements in digital media, a large number of news items get posted on various social media platforms every minute, which has a significant impact on society. Fake news and hate speech are pervading all social media platforms. With the emergence of neural networks and their promising performance in automation, multiple methods have been introduced to discriminate between fake and real news. One of these methods focuses on the propagation pattern of news in social media since fake news and real news spread differently. Graph Neural Networks (GNNs) can efficiently model linked entities and information propagation among the entities. In this study, we examine multiple graph neural networks (GNNs) to assess their effectiveness in predicting fake news items based on the news propagation pattern. We implement and explore the complexity of five different GNNs namely the Graph Convolution Neural Network (BiGCN-A, BiGCN-B), Graph Attention Neural Network (BiGAT), GraphSage (BiSAGE), Graph Convolution with ARMA filters (BiARMA), and Simplified Graph Convolution Neural Network (BiSGCN). We identify networks with reduced complexity and high efficiency for early detection of fake news at a lower computational cost in our study. The experiments show that BiSAGE performs the best with 94% accuracy in 980 seconds for Twitter16 dataset which is comparable to the BiGCN-B which reported 93% accuracy. Additionally, BiGCN-A performs the best with 94 % accuracy in 896 seconds for Twitter 15 dataset.

Digital information systems currently generate a vast amount of data every minute which emphasizes the continuing need to advance big data management systems with efficient data ingestion and knowledge extraction capabilities. To address the ‘big data’ problems due to high volume, velocity, variety, and veracity, data management systems evolved from structured databases to big data storage systems, graph databases, data warehouses, and data lakes but each solution has its strengths and shortcomings. The need to produce actionable knowledge fast from unstructured data ingested from distributed sources requires a marriage of data warehouses and data lakes to create a data Lakehouse (LH). The objective is to use the strengths of the data warehouse in producing insights fast from processed merged data, and of the data lake in ingesting and storing high-speed unstructured data with post-storage transformation and analytics capabilities. In this paper, we present a comparative review of the existing data warehouse and data lake technology to highlight their strengths and weaknesses and propose the desired and necessary features of the LH architecture, which has recently gained a lot of attention in the big data management research community.