Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

Emerging technologies for learning (volume 1)

Collection of 5 articles on emerging technologies and trend

Pair Analytics: Capturing Reasoning Processes in Collaborative Visual Analytics

Studying how humans interact with abstract, visual representations of massive amounts of data provides knowledge about how cognition works in visual analytics. This knowledge provides guidelines for cognitive-aware design and evaluation of visual analytic tools. Different methods have been used to capture and conceptualize these processes including protocol analysis, experiments, cognitive task analysis, and field studies. In this article, we introduce Pair Analytics: a method for capturing reasoning processes in visual analytics. We claim that Pair Analytics offers two advantages with respect to other methods: (1) a more natural way of making explicit and capturing reasoning processes and (2) an approach to capture social and cognitive processes used to conduct collaborative analysis in real-life settings. We support and illustrate these claims with a pilot study of three phenomena in collaborative visual analytics: coordination of attention, cognitive workload, and navigation of analysis

Proceedings of the first international workshop on Investigating dataflow in embedded computing architectures (IDEA 2015), January 21, 2015, Amsterdam, The Netherlands

IDEA '15 held at HiPEAC 2015, Amsterdam, The Netherlands on January 21st, 2015 is the rst workshop on Investigating Data ow in Embedded computing Architectures. This technical report comprises of the proceedings of IDEA '15. Over the years, data ow has been gaining popularity among Embedded Systems researchers around Europe and the world. However, research on data ow is limited to small pockets in dierent communities without a common forum for discussion. The goal of the workshop was to provide a platform to researchers and practitioners to present work on modelling and analysis of present and future high performance embedded computing architectures using data ow. Despite being the rst edition of the workshop, it was very pleasant to see a total of 14 submissions, out of which 6 papers were selected following a thorough reviewing process. All the papers were reviewed by at least 5 reviewers. This workshop could not have become a reality without the help of a Technical Program Committee (TPC). The TPC members not only did the hard work to give helpful reviews in time, but also participated in extensive discussion following the reviewing process, leading to an excellent workshop program and very valuable feedback to authors. Likewise, the Organisation Committee also deserves acknowledgment to make this workshop a successful event. We take this opportunity to thank everyone who contributed in making this workshop a success

End-to-end eScience: integrating workflow, query, visualization, and provenance at an ocean observatory

Journal ArticleData analysis tasks at an Ocean Observatory require integrative and and domain-specialized use of database, workflow, visualization systems. We describe a platform to support these tasks developed as part of the cyberinfrastructure at the NSF Science and Technology Center for Coastal Margin Observation and Prediction integrating a provenance-aware workflow system, 3D visualization, and a remote query engine for large-scale ocean circulation models. We show how these disparate tools complement each other and give examples of real scientific insights delivered by the integrated system. We conclude that data management solutions for eScience require this kind of holistic, integrative approach, explain how our approach may be generalized, and recommend a broader, application-oriented research agenda to explore relevant architectures

Long-Term Memory for Contemporary Dance is Distributed and Collaborative

Professional dancers appear to be the embodied records of works of choreography that have been created, rehearsed and performed. Their precision in recalling extended sequences of movement developed for these works defies the conventional methods used to investigate memory. A distributed cognition view holds that memory will not only be individualistic but also extended across a dance ensemble. Working closely with the highly skilled dancers of Australian Dance Theatre (ADT), we set out to develop an ecologically valid method that elicited memory recall and lapsing. Dancers were divided into two “teams” with each team asked to choose excerpts for memory recall from the company’s extensive repertoire that would challenge individual dancers in the other team. There were 14 trials; 12 involved the dancers recalling dance excerpts on their own (solo condition) and 2 with a partner (duet condition). In the ADT studio, seven dancers recalled (reproduced) as much of the excerpt as possible in the absence of an accompanying soundscape. Recall was extensive, but contained lapses, and these recall failures form the core of the analysis in this study. Four novel types of successful memory recall were identified: static shapes, isolated movements, partial and full movement sequences; and two strategies for recall: looking for movement by moving and collaborative sketching. Four types of errors were observed: errors of detail, order, omission, and additions. Analysis was conducted through a new method of counting ‘choreographic items’. The most detailed recall (73-96%) was for the two duet excerpts with significantly poorer recall of excerpts from complex group sections of a dance. Movement gist was generally retained. The types of recall and types of errors observed pointed to the use of not only procedural memory and chaining but also contextual cues as aids to movement recall. Collaborative elements of recall were observed that suggest the importance of distributed cognition and collective memory within a contemporary dance ensemble