Design thinking support: information systems versus reasoning

Numerous attempts have been made to conceive and implement appropriate information systems to support architectural designers in their creative design thinking processes. These information systems aim at providing support in very diverse ways: enabling designers to make diverse kinds of visual representations of a design, enabling them to make complex calculations and simulations which take into account numerous relevant parameters in the design context, providing them with loads of information and knowledge from all over the world, and so forth. Notwithstanding the continued efforts to develop these information systems, they still fail to provide essential support in the core creative activities of architectural designers. In order to understand why an appropriately effective support from information systems is so hard to realize, we started to look into the nature of design thinking and on how reasoning processes are at play in this design thinking. This investigation suggests that creative designing rests on a cyclic combination of abductive, deductive and inductive reasoning processes. Because traditional information systems typically target only one of these reasoning processes at a time, this could explain the limited applicability and usefulness of these systems. As research in information technology is increasingly targeting the combination of these reasoning modes, improvements may be within reach for design thinking support by information systems

Why do patients seek primary medical care in emergency departments? An ethnographic exploration of access to general practice

Objectives: To describe how processes of primary care access influence decisions to seek help at the emergency department (ED).Design: Ethnographic case study combining non-participant observation, informal and formal interviewing.Setting: Six general practitioner (GP) practices located in three commissioning organisations in England.Participants and methods: Reception areas at each practice were observed over the course of a working week (73 hours in total). Practice documents were collected and clinical and non-clinical staff were interviewed (n=19). Patients with recent ED use, or a carer if aged 16 and under, were interviewed (n=29).Results: Past experience of accessing GP care recursively informed patient decisions about where to seek urgent care, and difficulties with access were implicit in patient accounts of ED use. GP practices had complicated, changeable systems for appointments. This made navigating appointment booking difficult for patients and reception staff, and engendered a mistrust of the system. Increasingly, the telephone was the instrument of demand management, but there were unintended consequences for access. Some patient groups, such as those with English as an additional language, were particularly disadvantaged, and the varying patient and staff semantic of words like ‘urgent’ and ‘emergency’ was exacerbated during telephone interactions. Poor integration between in-hours and out-of-hours care and patient perceptions of the quality of care accessible at their GP practice also informed ED use.Conclusions: This study provides important insight into the implicit role of primary care access on the use of ED. Discourses around ‘inappropriate’ patient demand neglect to recognise that decisions about where to seek urgent care are based on experiential knowledge. Simply speeding up access to primary care or increasing its volume is unlikely to alleviate rising ED use. Systems for accessing care need to be transparent, perceptibly fair and appropriate to the needs of diverse patient groups

A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.Comment: 12 pages,55 reference

Knowledge Base Population using Semantic Label Propagation

A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we present a method that maximizes the effectiveness of newly trained relation extractors at a minimal annotation cost. Manual labeling can be significantly reduced by Distant Supervision, which is a method to construct training data automatically by aligning a large text corpus with an existing knowledge base of known facts. For example, all sentences mentioning both 'Barack Obama' and 'US' may serve as positive training instances for the relation born_in(subject,object). However, distant supervision typically results in a highly noisy training set: many training sentences do not really express the intended relation. We propose to combine distant supervision with minimal manual supervision in a technique called feature labeling, to eliminate noise from the large and noisy initial training set, resulting in a significant increase of precision. We further improve on this approach by introducing the Semantic Label Propagation method, which uses the similarity between low-dimensional representations of candidate training instances, to extend the training set in order to increase recall while maintaining high precision. Our proposed strategy for generating training data is studied and evaluated on an established test collection designed for knowledge base population tasks. The experimental results show that the Semantic Label Propagation strategy leads to substantial performance gains when compared to existing approaches, while requiring an almost negligible manual annotation effort.Comment: Submitted to Knowledge Based Systems, special issue on Knowledge Bases for Natural Language Processin

Information Systems Skills Differences between High-Wage and Low-Wage Regions: Implications for Global Sourcing

Developing Information Systems (IS) skills for a company’s workforce has always been challenging, but global sourcing growth has caused the determination of needed IS skills to be more complex. The increased use of outsourcing to an IS service provider and from high-wage regions to low-wage regions has affected what IS skills are required globally and how to distribute the workforce to meet these needs. To understand what skills are needed in locations that seek and those that provide outsourcing, we surveyed IS service provider managers in global locations. Results from 126 reporting units provide empirical evidence that provider units in low-wage regions value technical skills more than those in high-wage regions. Despite the emphasis on commodity skills in low-wage areas, high- and low-wage providers value project management skills. Low-wage regions note global and virtual teamwork more than high-wage regions do. The mix of skills and the variation by region have implications for domestic and offshore sourcing. Service providers can vary their staffing models in global regions which has consequences for recruiting, corporate training, and curriculum

Analysis and design of multiagent systems using MAS-CommonKADS

This article proposes an agent-oriented methodology called MAS-CommonKADS and develops a case study. This methodology extends the knowledge engineering methodology CommonKADSwith techniquesfrom objectoriented and protocol engineering methodologies. The methodology consists of the development of seven models: Agent Model, that describes the characteristics of each agent; Task Model, that describes the tasks that the agents carry out; Expertise Model, that describes the knowledge needed by the agents to achieve their goals; Organisation Model, that describes the structural relationships between agents (software agents and/or human agents); Coordination Model, that describes the dynamic relationships between software agents; Communication Model, that describes the dynamic relationships between human agents and their respective personal assistant software agents; and Design Model, that refines the previous models and determines the most suitable agent architecture for each agent, and the requirements of the agent network