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This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.

We show how to use a smoothed empirical likelihood approach to conduct efficient semiparametric inference in models characterized as conditional moment equalities when data are collected by variable probability sampling. Results from a simulation experiment suggest that the smoothed empirical likelihood based estimator can estimate the model parameters very well in small to moderately sized stratified samples.

This paper aims to provide insights into the characteristics of avid non-fiction book readers, exploring their typical demographic characteristics in relation to reading volume and frequency. It also investigates their comparative library usage in relation to avid fiction readers, as well as their motivation to read, and barriers to reading. Findings from the subset of self-identified avid non-fiction readers from the 2015 International Study of Avid Book Readers are interrogated to provide insights into this under-researched group.

The authors have used a single-stage mixed-methods approach, using data from both qualitative and quantitative items in an international survey.

The quantitative data analysis of this study suggests that avid non-fiction book readers were more likely to be men and older than avid fiction readers, and that they also tended to read less frequently, though avid non-fiction readers tended to read a greater volume of books. Avid fiction readers reported greater library usage, and thus unsurprisingly were found to have a greater borrowing tendency than non-fiction readers. Our qualitative findings around reading motivation identified a range of recurrent themes. The authors also found three key barriers to reading: time, book access and concentration.

The findings of this study provide unique insights into the characteristics, preferences and motivation of avid non-fiction readers, with the relationship between pleasure and the reading of non-fiction of particular interest.

Literature, spanning industrial organization and strategic management disciplines, uses variance decomposition to understand the relative importance of firm, industry and business group effects in shaping profitability variations. Some literature analyzes firm profitability under transition to liberalization. Previous research has taken a static before-and-after view on institutional change. This paper aims to focus on the dynamic process of liberalization in India, analyzing how different institutional regime changes alter firm behavior leading to changes in profitability patterns.

Based on a panel data set of several thousand Indian firms, spanning the 26-year period between 1980-1981 and 2005-2006, the authors determine the relative importance of firm, industry and business group effects in explaining manufacturing firms’ profitability variances across different institutional phases. The authors evaluate three propositions that help assess transition dynamics between phases. They determine the quantum of catch-up or falling behind by firms.

Different industries emerge as profitability leaders, as the economy progresses through different liberalization phases. Business groups that have been more effective in resource appropriation, rent-seeking, politician management and non-market activities in a controlled regime are replaced as profit leaders by those that, in a free-market economy, can be capable of intra-business resource allocation tasks and leveraging corporate capabilities.

The approach demonstrates how to analyze the underlying detailed structure of firm-level data, and performance outcomes, to derive nuanced interpretation of factors giving rise to the effects that explain profitability variances, and how to assess the way these effects behave over time. The dynamic evidence-based approach highlights what factors matter, where, when and why, in influencing profitability variances, which are a key dimension of industrial and economic performance.

Researchers have not widely explored design-based factors that govern buildings’ physical properties and human–building interactions. This paper aims to understand the influence of design-related factors on the time-on-market (TOM) of listed houses and, consequently, study the effect of design features on the desirability of a given house.

This research analyzes a dataset of listed houses, provided by the REALTORS^® Association of Edmonton (RAE) and covers a period extending from January 2009 to August 2019, using Cox proportional-hazards regression model to identify building features that influence people purchasing decisions.

The research findings affirm the statistical insignificance of the price on the TOM compared to other design features, such as the construction method, the installed mechanical systems and cladding materials.

The data used in the analysis comes from a single North American region, i.e. Edmonton, Alberta, Canada. Also, the data provided by the RAE includes only records that involve a realtor.

The observations of the research presented in this paper influence the housing market players’ decisions about housing designs, mainly those concerned with building new residential dwellings such as speculative builders and designers.

The research novelty stems from two aspects: the medium used for analysis, i.e. Cox proportional-hazards regression model, which allows considering the listed-but-not-sold units and helps to eliminate the survivorship bias that leads to over-optimistic outcomes; and the assessment of design-related features which allows to understand people’s preferences in design alternatives.

Whether immigrants are positively or negatively self-selected is much disputed. Whereas most previous studies have addressed this question by comparing the earnings of immigrants to those of U.S. natives, this analysis uses occupation to examine the skill level of immigrants relative to their home country population. Data on the occupational distribution of individuals granted legal permanent residence in 1995 indicate that the proportion of immigrants in skilled occupations is related to the corresponding proportion in source countries but not necessarily to the return to skill and other economic factors in the country of origin.

Aims to formalize the benchmarking process by proposing methods and tools for the steps of the process. Focuses on the first two phases of the benchmarking process: measurement and self analysis (phase 1), and determination of subject and partners of a benchmarking study (phase 2). For this purpose, related to the first phase, a method of diagnosis is employed. In the second phase, three different data analysis methods (lexical analysis, principal components analysis and common factor analysis) are utilized. These two phases serve the purpose of an internal benchmarking (comparison between different domains of activities in the enterprise) and of an external benchmarking study (comparison between different enterprises). Three enterprises are selected for the benchmarking application. Related data are collected and analyzed, and results are reported.

Companies that are successful take the training of their people very seriously. Though training makes an essential contribution to the business, it does not provide the complete solution for the development of a company′s employees. But, unless people have the skills, knowledge and theoretical framework, they cannot make the right decisions or take the right actions. Assesses the importance of training and its effectiveness in organizations today. Giving the author′s organization as an example, discusses the difficulties and processes involved in implementing a training process. Part 1 provides an overview of the training process including defining direction, setting up training, establishing checkpoints and improvement of training. Goes on in Part 2 to define validation and discuss its use and value, detailing various methods and guidelines.

This paper aims to be a balance of mixed management and engineering concepts that aims to fuse classical engineering methodologies into a systems engineering framework to assess and compare systematically and comprehensively services rendered by engineering systems.

An auditing framework is developed to assess the performance of engineering services in the context of engineering services found within a facility. As a result of a system heterogeneity factor, an approach to remove this confounding issue is developed to provide a different insight into the performance of engineering services.

The output of the audit exercise serves as an input to the second methodology, direct age‐adjusted failure, which overcomes systems attributes confounding issues when comparison is made between different systems populations of the same class type. This method allows management to identify areas in which extra resources are needed to improve maintenance performance.

The proposed standardization technique, which can be applied to system attributes other than age, overcomes the systems heterogeneity issue between localities. This research work is positioned in the context of building engineering services, as they are the most important in terms of socio‐economical impact. A case study based on an actual facilities assessment in Singapore is used to demonstrate the usefulness of such an integrated systems approach.

This paper presents a qualitative‐quantitative assessment framework that consists of two major methodologies to help in identifying and prioritising engineering system services in order to allocate limited resources to the appropriate engineering service so as to improve its performance.

Hemisphericity in behaviour studies is the role and dominance of right bran/left brain in the human brain, each side of which has varying roles and characteristics, thereby leading to different thinking orientations in the human. A study was conducted to measure the hemisphericity orientation of construction and design engineers in a large public construction organization. We know from literature that people of different hemisphericity orientations have unlike personalities and dissimilar perspectives of specific situations. The essential characteristics of hemisphericity as established in literature are presented and used in analysing the organization. From this study of engineers, it was seen that construction engineers in the organization, State Department of Engineering Construction (SDEC), are predominantly left‐brained while design engineers are predominantly right‐brained. This difference in orientation partially explains why the design and construction engineers at SDEC are unable to see eye‐to‐eye in issues concerning implementation of drawings. Left hemisphere dominant engineers are also seen to desire more organizational changes than their right hemisphere dominant counterparts. Ideally, researchers believe that a 50–50 distribution of hemisphericities in large organizations is desirable. Details, analysis, and recommendations are presented in the paper.