Big data is an enabling technology in digital transformation. It perfectly complements ordinary linear models and generalized linear models, as training well-performed ordinary linear models and generalized linear models require huge amounts of data. With the help of big data, ordinary and generalized linear models can be well-trained and thus offer better services to human beings. However, there are still many challenges to address for training ordinary linear models and generalized linear models in big data. One of the most prominent challenges is the computational challenges. Computational challenges refer to the memory inflation and training inefficiency issues occurred when processing data and training models. Hundreds of algorithms were proposed by the experts to alleviate/overcome the memory inflation issues. However, the solutions obtained are locally optimal solutions. Additionally, most of the proposed algorithms require loading the dataset to RAM many times when updating the model parameters. If multiple model hyper-parameters needed to be computed and compared, e.g. ridge regression, parallel computing techniques are applied in practice. Thus, multiple learning with sufficient statistics arrays are proposed to tackle the memory inflation and training inefficiency issues.

<p>In the field of the construction industry, the research work has been widely focused on identifying key performance indicators and critical success factors without assessing the impact of conflict environment factors. This study focusses on the impact of post-conflict environment factors on local construction organization performance. This research presents a performance prediction model comprising the effect of post-conflict environment factors on construction organization performance. The proposed framework of this study has four stages: identify key performance indicators (KPIs), identify post-conflict environment impacting factors, determine critical success factors (CSFs), and formulate success strategy to improve performance. Analytical hierarchy process (AHP) and multiple linear regression (MLR) techniques are applied to analyze the data.</p> <p>The study finding indicates that there is a significant relationship between the post-conflict condition impacting factors and local construction organization performance, which is insufficiently studied in previous research work. Thus, the developed framework will benefit academic scholars and industry practitioners to analyze and evaluate challenges and opportunities caused by different external environment conditions in the post-conflict construction industry. </p>