Scielo RSS <![CDATA[South African Computer Journal]]> http://www.scielo.org.za/rss.php?pid=2313-783520210002&lang=en vol. 33 num. 2 lang. en <![CDATA[SciELO Logo]]> http://www.scielo.org.za/img/en/fbpelogp.gif http://www.scielo.org.za <![CDATA[<b>Editorial: Moving on</b>]]> http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S2313-78352021000200001&lng=en&nrm=iso&tlng=en <![CDATA[<b>nf-rnaSeqCount: A Nextflow pipeline for obtaining raw read counts from RNA-seq data</b>]]> http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S2313-78352021000200002&lng=en&nrm=iso&tlng=en The rate of raw sequence production through Next-Generation Sequencing (NGS) has been growing exponentially due to improved technology and reduced costs. This has enabled researchers to answer many biological questions through "multi-omics" data analyses. Even though such data promises new insights into how biological systems function and understanding disease mechanisms, computational analyses performed on such large datasets comes with its challenges and potential pitfalls. The aim of this study was to develop a robust portable and reproducible bioinformatic pipeline for the automation of RNA sequencing (RNA-seq) data analyses. Using Nextflow as a workflow management system and Singularity for application containerisation, the nf-rnaSeqCount pipeline was developed for mapping raw RNA-seq reads to a reference genome and quantifying abundance of identified genomic features for differential gene expression analyses. The pipeline provides a quick and efficient way to obtain a matrix of read counts that can be used with tools such as DESeq2 and edgeR for differential expression analysis. Robust and flexible bioinformatic and computational pipelines for RNA-seq data analysis, from QC to sequence alignment and comparative analyses, will reduce analysis time, and increase accuracy and reproducibility of findings to promote transcriptome research. Categories: · Applied computing ~ Bioinformatics <![CDATA[<b>Consumer-centric factors for the implementation of smart meters in South Africa</b>]]> http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S2313-78352021000200003&lng=en&nrm=iso&tlng=en Smart meter implementation is still in its infancy in many African countries, including South Africa. This is evident from the fact that most research studies are either Eurocentric or American-centric. Hence, this research aimed to identify consumer-centric factors for planning considerations in implementation of smart meters in South Africa. We used various behavioural theoretical models found in literature to identify potential factors relevant to this study. Based on quantitatively gathered data (n = 705), a structural equation model (SEM) was used to evaluate the identified factors. This study found that only ten consumer-centric factors were significant to smart meter consumers. These factors include behavioural intention, attitude, trust in technology, social norms, facilitating conditions, perceived usefulness, perceived ease of use, privacy risk, monetary cost, and perceived value. In conclusion, the study shows that not all factors suggested within the European and American context are relevant for smart meter implementation within the South African context. Hence, results of this study hold some practical implications in assisting utility companies in identifying consumer-centric factors that are relevant to the South African population. Finally, consumer-centric factors can be used by policy makers and energy regulators as baseline factors for future pervasive technology acceptance studies. CATEGORIES: · Human-centered computing ~ Ubiquitous and mobile computing theory, concepts and paradigms <![CDATA[<b>Big Data Driven Decision Making Model: A case of the South African banking sector</b>]]> http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S2313-78352021000200004&lng=en&nrm=iso&tlng=en The quest to develop a Big Data Driven Decision Making framework to support the incorporation of big data analytics into the decision-making process resulted in the development of a decision making model. The study was conducted within the banking sector of South Africa, with participants from three leading South African banking institutions. The conducted research followed the design science research process of awareness, suggestion, development, evaluation and conclusion. This study developed a theoretical Big Data Driven Decision Making model which illustrates the decision-making process in banking using big data. The study further determined the organizational supports that need to be in place to support the big data analytics decision-making process. CATEGORIES: · Information systems ~ Data analytics <![CDATA[<b>Citation and referencing guidelines</b>]]> http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S2313-78352021000200005&lng=en&nrm=iso&tlng=en The quest to develop a Big Data Driven Decision Making framework to support the incorporation of big data analytics into the decision-making process resulted in the development of a decision making model. The study was conducted within the banking sector of South Africa, with participants from three leading South African banking institutions. The conducted research followed the design science research process of awareness, suggestion, development, evaluation and conclusion. This study developed a theoretical Big Data Driven Decision Making model which illustrates the decision-making process in banking using big data. The study further determined the organizational supports that need to be in place to support the big data analytics decision-making process. CATEGORIES: · Information systems ~ Data analytics