Active artefact management for distributed software engineering

We describe a software artefact repository that provides its contents with some awareness of their own creation. "Active" artefacts are distinguished from their passive counterparts by their enriched meta-data model which reflects the work-flow process that created them, the actors responsible, the actions taken to change the artefact, and various other pieces of organisational knowledge. This enriched view of an artefact is intended to support re-use of both software and the expertise gained when creating the software. Unlike other organisational knowledge systems, the meta-data is intrinsically part of the artefact and may be populated automatically from sources including existing data-format specific information, user supplied data and records of communication. Such a system is of increased importance in the world of "virtual teams" where transmission of vital organisational knowledge, at best difficult, is further constrained by the lack of direct contact between engineers and differing development cultures

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings

Failure is an option:an innovative engineering curriculum

PurposeAdvancements and innovation in engineering design are based on learning from previous failures but students are encouraged to ‘succeed’ first time and hence can avoid learning from failure in practice. The purpose of the study was to design and evaluate a curriculum to help engineering design students to learn from failure.Design/Methodology/ApproachA new curriculum design provided a case study for evaluating the effects of incorporating learning from failure within a civil engineering course. An analysis of the changes in course output was undertaken in relation to graduate destination data covering 2006 to 2016 and student satisfaction from 2012 to 2017 and a number of challenges and solutions for curriculum designers were identified.FindingsThe design and delivery of an innovative curriculum, within typical constraints, can provide opportunities for students to develop resilience to failure as an integral part of their learning in order to think creatively and develop novel engineering solutions. The key issues identified were: the selection of appropriate teaching methods, creating an environment for exploratory learning, group and team assessments with competitive elements where practicable, and providing students with many different pedagogical approaches to produce a quality learning experience.OriginalityThis case study demonstrates how to design and implement an innovative curriculum that can produce positive benefits of learning from failure. This model can be applied to other disciplines such as building surveying and construction management. This approach underpins the development of skills necessary in the educational experience to develop as a professional building pathologist

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science