ICOPER Project - Deliverable 4.3 ISURE: Recommendations for extending effective reuse, embodied in the ICOPER CD&R

The purpose of this document is to capture the ideas and recommendations, within and beyond the ICOPER community, concerning the reuse of learning content, including appropriate methodologies as well as established strategies for remixing and repurposing reusable resources. The overall remit of this work focuses on describing the key issues that are related to extending effective reuse embodied in such materials. The objective of this investigation, is to support the reuse of learning content whilst considering how it could be originally created and then adapted with that ‘reuse’ in mind. In these circumstances a survey on effective reuse best practices can often provide an insight into the main challenges and benefits involved in the process of creating, remixing and repurposing what we are now designating as Reusable Learning Content (RLC). Several key issues are analysed in this report: Recommendations for extending effective reuse, building upon those described in the previous related deliverables 4.1 Content Development Methodologies and 4.2 Quality Control and Web 2.0 technologies. The findings of this current survey, however, provide further recommendations and strategies for using and developing this reusable learning content. In the spirit of ‘reuse’, this work also aims to serve as a foundation for the many different stakeholders and users within, and beyond, the ICOPER community who are interested in reusing learning resources. This report analyses a variety of information. Evidence has been gathered from a qualitative survey that has focused on the technical and pedagogical recommendations suggested by a Special Interest Group (SIG) on the most innovative practices with respect to new media content authors (for content authoring or modification) and course designers (for unit creation). This extended community includes a wider collection of OER specialists. This collected evidence, in the form of video and audio interviews, has also been represented as multimedia assets potentially helpful for learning and useful as learning content in the New Media Space (See section 4 for further details). Section 2 of this report introduces the concept of reusable learning content and reusability. Section 3 discusses an application created by the ICOPER community to enhance the opportunities for developing reusable content. Section 4 of this report provides an overview of the methodology used for the qualitative survey. Section 5 presents a summary of thematic findings. Section 6 highlights a list of recommendations for effective reuse of educational content, which were derived from thematic analysis described in Appendix A. Finally, section 7 summarises the key outcomes of this work

BIM Assisted Design Process Automation for Pre-Engineered Buildings (PEB)

The effective adoption and implementation of Building Information Modeling (BIM) is still challenging for the construction industry. However, studies and reports show a significant increase in the rate of BIM implementation and adoption in mainstream construction activities over the last five years. In contrast, Pre-Engineered Building (PEB) construction, a specialized construction system which provides a very efficient approach for construction of primarily industrial buildings, has not seen the same uptake in BIM implementation and adoption. The thesis reviews the benefits and the main applications of BIM for the PEB industry as well as challenges of its practical implementation. To facilitate the implementation of BIM in the PEB industry, a BIM framework is adapted from Pre-fabrication (Pre-fab) industry and new workflows, process maps, and data-exchange strategies are developed. As the PEB industry traditionally makes significant use of automation in its design and fabrication process, accordingly this work investigates the technical challenges of incorporating automation into the proposed BIM process. Two new BIM concepts, “Planar Concept” and “Floating LOD”, are then developed and implemented as a solution to these challenges. To define the proper input/output criteria for automated BIM design processes, a numerical study was performed to identify an “Optimum LOD”. A software implementation embodying the research outcomes was developed to illustrate the feasibility of the results. Its step-by-step deployment is analyzed and discussed using an example industry PEB design project. Further, the impact of this work is extended by integrating the developed BIM framework and automated design process with wind engineering design activities and tools and procurement systems. The study concludes that the deployment of the proposed BIM framework could significantly address existing issues in project design through to operation processes found in the PEB industry. Also, the results indicate the developed concepts have the potential for supporting the application of automation in the other sectors of the general construction industry. This thesis is written using the Integrated Article format and includes various complementary studies

A Scalable Machine Learning Online Service for Big Data Real-Time Analysis

Proceedings of: IEEE Symposium Series on Computational Intelligence (SSCI 2014). Orlando, FL, USA, December 09-12, 2014.This work describes a proposal for developing and testing a scalable machine learning architecture able to provide real-time predictions or analytics as a service over domain-independent big data, working on top of the Hadoop ecosystem and providing real-time analytics as a service through a RESTful API. Systems implementing this architecture could provide companies with on-demand tools facilitating the tasks of storing, analyzing, understanding and reacting to their data, either in batch or stream fashion; and could turn into a valuable asset for improving the business performance and be a key market differentiator in this fast pace environment. In order to validate the proposed architecture, two systems are developed, each one providing classical machine-learning services in different domains: the first one involves a recommender system for web advertising, while the second consists in a prediction system which learns from gamers' behavior and tries to predict future events such as purchases or churning. An evaluation is carried out on these systems, and results show how both services are able to provide fast responses even when a number of concurrent requests are made, and in the particular case of the second system, results clearly prove that computed predictions significantly outperform those obtained if random guess was used.This research work is part of Memento Data Analysis project, co-funded by the Spanish Ministry of Industry, Energy and Tourism with identifier TSI-020601-2012-99.Publicad