Designing computational grids using best practices in software architechture

The basic principle of sharing and collaborative work by geographically separated computers is known by several names such as meta computing, scalable computing, cluster computing, internet computing and this has today metamorphosed into a new term known as grid computing. Grid computing is proving to be promising method of HPC, which is packaged with many challenges. This paper elucidates the role that pattern can play in architecting complex systems with specific reference to grid computing. We provide descriptions of a set of well-engineered patterns that the practicing developer can apply to crafting his or her own specific applications. We develop the Software Requirements Specification (SRS), with an attempt to drive to effectual design specifications for use by any grid developer. We analyze the grid using an Object Oriented approach and present the design using the unified Modeling Language (UML) which itself helps the identification of patterns at different phases

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment