Clustering a big distributed dataset of hundred gigabytes or more is a challenging task in distributed computing. A popular method to tackle this problem is to use a random sample of the big dataset to compute an approximate result as an estimation of the true result computed from the entire dataset. In this paper, instead of using a single random sample, we use multiple random samples to compute an ensemble result as the estimation of the true result of the big dataset. We propose a distributed computing framework to compute the ensemble result. In this framework, a big dataset is represented in the RSP data model as random sample data blocks managed in a distributed file system. To compute the ensemble clustering result, a set of RSP data blocks is randomly selected as random samples and clustered independently in parallel on the nodes of a cluster to generate the component clustering results. The component results are transferred to the master node, which computes the ensemble result. Since the random samples are disjoint and traditional consensus functions cannot be used, we propose two new methods to integrate the component clustering results into the final ensemble result. The first method uses component cluster centers to build a graph and the METIS algorithm to cut the graph into subgraphs, from which a set of candidate cluster centers is found. A hierarchical clustering method is then used to generate the final set of $k$ cluster centers. The second method uses the clustering-by-passing-messages method to generate the final set of $k$ cluster centers. Finally, the $k$ -means algorithm was used to allocate the entire dataset into $k$ clusters. Experiments were conducted on both synthetic and real-world datasets. The results show that the new ensemble clustering methods performed better than the comparison methods and that the distributed computing framework is efficient and scalable in clustering big datasets.

This study presents a systematic literature review on blockchain-based authentication in smart environments that include smart city, smart home, smart grid, smart healthcare, smart farming and smart transportation. The review incorporated 39 articles presenting blockchain solutions for security and privacy issues through authentication mechanisms in these smart environments. Guided by three research questions to determine the main issues in smart environment, the availability of blockchain-based authentication solutions and identified research gaps and future research endeavors, this review used PRISMA method to provide insights on the use of blockchain-based authentication schemes. The research gap is that blockchain solutions are mostly at the proposal, and sometimes conceptual stage is in the reviewed articles. In addition, solutions presented in smart environments require exploration into blockchain. The findings show similar situational issues across different smart environments and the flexibility and adaptability of blockchain to provide solutions to the identified issues pertaining to security and privacy. More clearly, the authentication problem posed across different smart environments can be adapted to blockchain technology provided that it is combined with other technologies to increase efficiency, despite the existence of large-scale authentication mechanisms. Now, blockchain still has the unique, distributed feature of converting the current database into blockchain databases. This review guided future research directions which could further contribute to the sustainable management of smart environments.

For decision-making support and evidence based on healthcare, high quality data are crucial, particularly if the emphasized knowledge is lacking. For public health practitioners and researchers, the reporting of COVID-19 data need to be accurate and easily available. Each nation has a system in place for reporting COVID-19 data, albeit these systems’ efficacy has not been thoroughly evaluated. However, the current COVID-19 pandemic has shown widespread flaws in data quality. We propose a data quality model (canonical data model, four adequacy levels, and Benford’s law) to assess the quality issue of COVID-19 data reporting carried out by the World Health Organization (WHO) in the six Central African Economic and Monitory Community (CEMAC) region countries between March 6,2020, and June 22, 2022, and suggest potential solutions. These levels of data quality sufficiency can be interpreted as dependability indicators and sufficiency of Big Dataset inspection. This model effectively identified the quality of the entry data for big dataset analytics. The future development of this model requires scholars and institutions from all sectors to deepen their understanding of its core concepts, improve integration with other data processing technologies, and broaden the scope of its applications.