A comprehensive and systematic literature review on the big data management techniques in the internet of things

The Internet of Things (IoT) is a communication paradigm and a collection of heterogeneous interconnected devices. It produces large-scale distributed, and diverse data called big data. Big Data Management (BDM) in IoT is used for knowledge discovery and intelligent decision-making and is one of the most significant research challenges today. There are several mechanisms and technologies for BDM in IoT. This paper aims to study the important mechanisms in this area systematically. This paper studies articles published between 2016 and August 2022. Initially, 751 articles were identified, but a paper selection process reduced the number of articles to 110 significant studies. Four categories to study BDM mechanisms in IoT include BDM processes, BDM architectures/frameworks, quality attributes, and big data analytics types. Also, this paper represents a detailed comparison of the mechanisms in each category. Finally, the development challenges and open issues of BDM in IoT are discussed. As a result, predictive analysis and classification methods are used in many articles. On the other hand, some quality attributes such as confidentiality, accessibility, and sustainability are less considered. Also, none of the articles use key-value databases for data storage. This study can help researchers develop more effective BDM in IoT methods in a complex environment.

Managing large-scale genomic datasets and translation into clinical practice

OBJECTIVE

To summarize excellent current research in the field of Bioinformatics and Translational Informatics with application in the health domain.

METHOD

We provide a synopsis of the articles selected for the IMIA Yearbook 2014, from which we attempt to derive a synthetic overview of current and future activities in the field. A first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section. Each section editor evaluated independently the set of 1,851 articles and 15 articles were retained for peer-review.

RESULTS

The selection and evaluation process of this Yearbook's section on Bioinformatics and Translational Informatics yielded three excellent articles regarding data management and genome medicine. In the first article, the authors present VEST (Variant Effect Scoring Tool) which is a supervised machine learning tool for prioritizing variants found in exome sequencing projects that are more likely involved in human Mendelian diseases. In the second article, the authors show how to infer surnames of male individuals by crossing anonymous publicly available genomic data from the Y chromosome and public genealogy data banks. The third article presents a statistical framework called iCluster+ that can perform pattern discovery in integrated cancer genomic data. This framework was able to determine different tumor subtypes in colon cancer.

CONCLUSIONS

The current research activities still attest the continuous convergence of Bioinformatics and Medical Informatics, with a focus this year on large-scale biological, genomic, and Electronic Health Records data. Indeed, there is a need for powerful tools for managing and interpreting complex data, but also a need for user-friendly tools developed for the clinicians in their daily practice. All the recent research and development efforts are contributing to the challenge of impacting clinically the results and even going towards a personalized medicine in the near future.