Knowledge Nodes: the Building Blocks of a Distributed Approach to Knowledge Management

Abstract: In this paper we criticise the objectivistic approach that underlies most current systems for Knowledge Management. We show that such an approach is incompatible with the very nature of what is to be managed (i.e., knowledge), and we argue that this may partially explain why most knowledge management systems are deserted by users. We propose a different approach - called distributed knowledge management - in which subjective and social (in a word, contextual) aspects of knowledge are seriously taken into account. Finally, we present a general technological architecture in which these ideas are implemented by introducing the concept of knowledge node

Creating R Packages: A Tutorial

This tutorial gives a practical introduction to creating R packages. We discuss how object oriented programming and S formulas can be used to give R code the usual look and feel, how to start a package from a collection of R functions, and how to test the code once the package has been created. As running example we use functions for standard linear regression analysis which are developed from scratch

BEAT: An Open-Source Web-Based Open-Science Platform

With the increased interest in computational sciences, machine learning (ML), pattern recognition (PR) and big data, governmental agencies, academia and manufacturers are overwhelmed by the constant influx of new algorithms and techniques promising improved performance, generalization and robustness. Sadly, result reproducibility is often an overlooked feature accompanying original research publications, competitions and benchmark evaluations. The main reasons behind such a gap arise from natural complications in research and development in this area: the distribution of data may be a sensitive issue; software frameworks are difficult to install and maintain; Test protocols may involve a potentially large set of intricate steps which are difficult to handle. Given the raising complexity of research challenges and the constant increase in data volume, the conditions for achieving reproducible research in the domain are also increasingly difficult to meet. To bridge this gap, we built an open platform for research in computational sciences related to pattern recognition and machine learning, to help on the development, reproducibility and certification of results obtained in the field. By making use of such a system, academic, governmental or industrial organizations enable users to easily and socially develop processing toolchains, re-use data, algorithms, workflows and compare results from distinct algorithms and/or parameterizations with minimal effort. This article presents such a platform and discusses some of its key features, uses and limitations. We overview a currently operational prototype and provide design insights.Comment: References to papers published on the platform incorporate

Supplementary skills guides for built environment researchers

Deepening specialised knowledge-base and wider skills of researchers in a wider variety of disciplines are prerequisite for developing successful leadership in higher education, the public sector and industry. In response to this repeated calls for enhancing supplementary skills of the built environment researchers, TG53 (Postgraduate Research Training in Building and Construction) initiated steps to develop and nurture understanding of supplementary skills and providing a common frame of reference for use and further discourse and has developed 6 good practice examples highlighting skills for researchers within the built environment. Accordingly, this TG53 publication is in response to the repeated calls for enhancing supplementary skills of the built environment researchers

Easylife: the data reduction and survey handling system for VIPERS

We present Easylife, the software environment developed within the framework of the VIPERS project for automatic data reduction and survey handling. Easylife is a comprehensive system to automatically reduce spectroscopic data, to monitor the survey advancement at all stages, to distribute data within the collaboration and to release data to the whole community. It is based on the OPTICON founded project FASE, and inherits the FASE capabilities of modularity and scalability. After describing the software architecture, the main reduction and quality control features and the main services made available, we show its performance in terms of reliability of results. We also show how it can be ported to other projects having different characteristics.Comment: pre-print, 17 pages, 4 figures, accepted for publication in Publications of the Astronomical Society of the Pacifi

An ES process framework for understanding the strategic decision making process of ES implementations

Enterprise systems (ES) implementations are regarded costly, time and resource consuming and have a great impact on the organization in terms of the risks they involve and the opportunities they provide. The steering committee (SC) represents the group of individuals who is responsible for making strategic decisions throughout the ES implementation lifecycle. It is evident from recent studies that there is a relationship between the decision making process and ES implementation success. One of the key elements that contribute to the success of ES implementations is a quick decision making process (Brown and Vessey, 1999; Gupta, 2000; Parr, et al., 1999). This study addresses the strategic decision-making process by SC through its focus on four research questions (1) How can the strategic decision-making process in the implementation of ES be better understood, during each phase of the ES implementation lifecycle? (2) What is the process by which the SC makes strategic decisions? (3) How are fast decisions made? and (4) How does decision speed link to the success of ES implementation? Process models of ES implementation will provide a framework to investigate the strategic decision making process during each phases of the ES implementation lifecycle. Patterns in the decision making process will be explored using strategic choice models. This study develops a research model that focuses on the decision making process by steering committee to explore research questions. It concludes with identifying contributions to both IS research and business practitioners