A Framework for Identifying Sequences of Interactions That Cause Usability Problems in Collaborative Systems

Collaborative systems support shared spaces, where groups of users exchange interactions. In order to ensure the usability of these systems, an intuitive interactions´ organization and that each user has awareness information to know the activity of others are necessary. Usability laboratories allow evaluators to verify these requirements. However, laboratory usability evaluations can be problematic for reproducing mobile and ubiquitous contexts, as they restrict the place and time in which the user interacts with the system. This paper presents a framework for building software support that it collects human?machine interactions in mobile and ubiquitous contexts and outputs an assessment of the system´s usability. This framework is constructed through learning that is based on neural networks, identifying sequences of interactions related to usability problems when users carry out collaborative activities. The paper includes a case study that puts the framework into action during the development process of a smartphone application that supports collaborative sport betting.This research and the APC was funded by the University of Cantabria and the Government of Cantabria through the industrial doctorate grant DI27, given to Santos Bringas. Alicia Nieto-Reyes was supported by a Spanish Ministerio de Ciencia, Innovación y Universidades grant MTM2017-86061-C2-2-P

Using natural user interfaces to support synchronous distributed collaborative work

Synchronous Distributed Collaborative Work (SDCW) occurs when group members work together at the same time from different places together to achieve a common goal. Effective SDCW requires good communication, continuous coordination and shared information among group members. SDCW is possible because of groupware, a class of computer software systems that supports group work. Shared-workspace groupware systems are systems that provide a common workspace that aims to replicate aspects of a physical workspace that is shared among group members in a co-located environment. Shared-workspace groupware systems have failed to provide the same degree of coordination and awareness among distributed group members that exists in co-located groups owing to unintuitive interaction techniques that these systems have incorporated. Natural User Interfaces (NUIs) focus on reusing natural human abilities such as touch, speech, gestures and proximity awareness to allow intuitive human-computer interaction. These interaction techniques could provide solutions to the existing issues of groupware systems by breaking down the barrier between people and technology created by the interaction techniques currently utilised. The aim of this research was to investigate how NUI interaction techniques could be used to effectively support SDCW. An architecture for such a shared-workspace groupware system was proposed and a prototype, called GroupAware, was designed and developed based on this architecture. GroupAware allows multiple users from distributed locations to simultaneously view and annotate text documents, and create graphic designs in a shared workspace. Documents are represented as visual objects that can be manipulated through touch gestures. Group coordination and awareness is maintained through document updates via immediate workspace synchronization, user action tracking via user labels and user availability identification via basic proxemic interaction. Members can effectively communicate via audio and video conferencing. A user study was conducted to evaluate GroupAware and determine whether NUI interaction techniques effectively supported SDCW. Ten groups of three members each participated in the study. High levels of performance, user satisfaction and collaboration demonstrated that GroupAware was an effective groupware system that was easy to learn and use, and effectively supported group work in terms of communication, coordination and information sharing. Participants gave highly positive comments about the system that further supported the results. The successful implementation of GroupAware and the positive results obtained from the user evaluation provides evidence that NUI interaction techniques can effectively support SDCW

Graphical Database Architecture For Clinical Trials

The general area of the research is Health Informatics. The research focuses on creating an innovative and novel solution to manage and analyze clinical trials data. It constructs a Graphical Database Architecture (GDA) for Clinical Trials (CT) using New Technology for Java (Neo4j) as a robust, a scalable and a high-performance database. The purpose of the research project is to develop concepts and techniques based on architecture to accelerate the processing time of clinical data navigation at lower cost. The research design uses a positivist approach to empirical research. The research is significant because it proposes a new approach of clinical trials through graph theory and designs a responsive structure of clinical data that can be deployed across all the health informatics landscape. It uniquely contributes to scholarly literature of the phenomena of Not only SQL (NoSQL) graph databases, mainly Neo4j in CT, for future research of clinical informatics. A prototype is created and examined to validate the concepts, taking advantage of Neo4j’s high availability, scalability, and powerful graph query language (Cypher). This research study finds that integration of search methodologies and information retrieval with the graphical database provides a solid starting point to manage, query, and analyze the clinical trials data, furthermore the design and the development of a prototype demonstrate the conceptual model of this study. Likewise the proposed clinical trials ontology (CTO) incorporates all data elements of a standard clinical study which facilitate a heuristic overview of treatments, interventions, and outcome results of these studies

On the Challenges of Collaborative Data Processing

The last 30 years have seen the creation of a variety of electronic collaboration tools for science and business. Some of the best-known collaboration tools support text editing (e.g., wikis). Wikipedia's success shows that large-scale collaboration can produce highly valuable content. Meanwhile much structured data is being collected and made publicly available. We have never had access to more powerful databases and statistical packages. Is large-scale collaborative data analysis now possible? Using a quantitative analysis of Web 2.0 data visualization sites, we find evidence that at least moderate open collaboration occurs. We then explore some of the limiting factors of collaboration over data.Comment: to appear as a chapter in an upcoming book (Collaborative Information Behavior