The Practices Of Sustainability Factors And Perceived Sustainable Construction Firms’ Performance Among Contractors In Malaysia

Sustainability consists of three main pillars, namely: environmental, economic and social pillars. Its major thrust is an attempt to balance the use of resources between the present and future generations. As one of the major contributors to the global and local environmental problems, the construction industry is hard pressed to streamline its processes while executing projects. In the context of the Malaysian construction industry, there is a need for sustainability assessment framework that will integrate environmental, economic and social factors. This framework should answer a multitude of problems in achieving a higher degree of sustainability in the industry

Development of Software Tool for Scheduling Risk Analysis

There is a need in software industry for a tool to integrate scheduling risk. This tool will answer a multitude of problems in achieving a higher degree of scheduling accuracy. Successful project requires effective specialized tool for reducing risk. The objective of this study is to develop a project scheduling tool for the estimation of project risk. This study provides empirical evidence that scheduling tool are very much relevant, valid, and applicable in software industry. The output of this study is a tool which employs some of the main distribution methods such as Uniform, Beta, Triangular and Gaussian in order to decrease the risk of project delays. This tool was tested and the results were extracted, analyzed and discussed. The tool was developed using JAVA. It can be recommended that using a different type of probability distributions on project’s activities will give the project schedule more accurate estimations by using the developed tool early in the project development lifecycle

Modeling Of Construction Firms Sustainability

Sustainability is a hot debatable issue over the world, constantly being discussed by huge number of professionals’

The development of assessment framework for construction sustainability

Sustainability of construction firms is closely linked to its ability to procure projects that will generate sufficient turnover over time. The uncertainty concerning future procurement of projects remains the primary concern of any construction firm. Whilst this factor cannot be overcome in one single effort, there is no certainty that projects can continue to be procured if the construction firm are not well equip to face the challenges to remain competitive, innovative and is capable of strategically positioning itself to remain in business and thus sustaining its presence in the industry. The issue of the firm's sustainability is also closely linked to the general facets of sustainable development may exist in parallel; the main dominant thrust is sustainability and sustainable development may exist in parallel; the main dominant thrust is sustainability. A firm can sustain itself of it wholly adopts the concept of sustainable development; whilst maintaining its relevance in the industry it is operating. The objective of this paper is to analyze the frameworks, concepts and approaches of sustainability of construction firms that are embedded in social, economic and environment. The findings of this paper can provide insights for further research in understanding sustainability of constriction firms

Multi-Layer Web Services Discovery Using Word Embedding and Clustering Techniques

We propose a multi-layer data mining architecture for web services discovery using word embedding and clustering techniques to improve the web service discovery process. The proposed architecture consists of five layers: web services description and data preprocessing; word embedding and representation; syntactic similarity; semantic similarity; and clustering. In the first layer, we identify the steps to parse and preprocess the web services documents. In the second layer, Bag of Words with Term Frequency–Inverse Document Frequency and three word-embedding models are employed for web services representation. In the third layer, four distance measures, namely, Cosine, Euclidean, Minkowski, and Word Mover, are considered to find the similarities between Web services documents. In layer four, WordNet and Normalized Google Distance are employed to represent and find the similarity between web services documents. Finally, in the fifth layer, three clustering algorithms, namely, affinity propagation, K-means, and hierarchical agglomerative clustering, are investigated for clustering of web services based on observed similarities in documents. We demonstrate how each component of the five layers is employed in web services clustering using randomly selected web services documents. We conduct experimental analysis to cluster web services using a collected dataset consisting of web services documents and evaluate their clustering performances. Using a ground truth for evaluation purposes, we observe that clusters built based on the word embedding models performed better than those built using the Bag of Words with Term Frequency–Inverse Document Frequency model. Among the three word embedding models, the pre-trained Word2Vec’s skip-gram model reported higher performance in clustering web services. Among the three semantic similarity measures, path-based WordNet similarity reported higher clustering performance. By considering the different word representations models and syntactic and semantic similarity measures, we found that the affinity propagation clustering technique performed better in discovering similarities among Web services