Omnispective Analysis and Reasoning: a framework for managing intellectual concerns in scientific workflows

Scientific workflows are extensively used to support the management of experimental and computational research by connecting together different data sources, components and processes. However, certain issues such as the ability to check the appropriateness of the processes orchestrated, management of the context of workflow components and specification, and provision for robust management of intellectual concerns are not addressed adequately. Hence, it is highly desirable to add features to uplift focus from low level details to help clarify the rationale and intent behind the choices and decisions in the workflow specifications and provide a suitable level of abstraction to capture and organize intellectual concerns and map them to the workflow specification and execution semantics. In this paper, we present Omnispective Analysis and Reasoning (OAR), a novel framework for providing the above features and enhancements in scientific workflow management systems and processes. The OAR framework is aimed at supporting effective capture and reuse of intellectual concerns in workflow management

Knowledge Collaboration: Working with Data and Web Specialists

When resources are finite, people strive to manage resources jointly (if they do not rudely take possession of them). Organizing helps achieve—and even amplify—common purpose but often succumbs in time to organizational silos, teaming for the sake of teaming, and the obstacle course of organizational learning. The result is that organizations, be they in the form of hierarchies, markets, or networks (or, gradually more, hybrids of these), fail to create the right value for the right people at the right time. In the 21st century, most organizations are in any event lopsided and should be redesigned to serve a harmonious mix of economic, human, and social functions. In libraries as elsewhere, the three Ss of Strategy—Structure—Systems must give way to the three Ps of Purpose—Processes—People. Thence, with entrepreneurship and knowledge behaviors, data and web specialists can synergize in mutually supportive relationships of shared destiny

Contextual course design with Omnispective Analysis and Reasoning

In this paper, we present a novel approach to contextualize course design by the application of the Omnispective Analysis and Reasoning (OAR) framework to map the goals and intent of a course to its design and delivery. Effective design and delivery of courses requires alignment between planned learning activities and learning outcomes. However, it is generally not trivial to translate learning outcomes into course design, particularly so when using a Learning Management System (LMS). This is further compounded by the differences between the language of teaching theory and that used by the LMS. Thus, there is a need to effectively capture the rationale for design decisions in a course and map them to desired outcomes. We illustrate the application of the OAR framework with a process for translating a learning objective into course design using the Moodle LMS.This work is based on initial research supported by the Australian National University, and the Commonwealth of Australia, through the Cooperative Research Centre for Advanced Automotive Technology. Support from the Research Student Development Centre, the CECS Educational Development Group and the Division of Information at the ANU is gratefully acknowledged

Repairing Innovation: A Study of Integrating AI in Clinical Care

Over the past two years, a multi-disciplinary team of clinicians and technologists associated with Duke University and Duke Health system have developed and implemented Sepsis Watch, a sociotechnical system combining an artificial intelligence (AI) deep learning model with new hospital protocols to raise the quality of sepsis treatment. Sepsis is a widespread and deadly condition that can develop from any infection and is one of the most common causes of death in hospitals. And while sepsis is treatable, it is notoriously difficult to diagnose consistently. This makes sepsis a prime candidate for AI-based interventions, where new approaches to patient data might raise levels of detection, treatment, and, ultimately, patient outcomes in the form of fewer deaths.As an application of AI, the deep learning model tends to eclipse the other parts of the system; in practice, Sepsis Watch is constituted by a complex combination of human labor and expertise, as well as technical and institutional infrastructures. This report brings into focus the critical role of human labor and organizational context in developing an effective clinical intervention by framing Sepsis Watch as a complex sociotechnical system, not just a machine learning model

Managing large and complex systems with Omnispective Analysis and Reasoning

Development of newer and more sustainable systems requires a thorough understanding of the complex interactions in current systems. Therefore it is necessary to be able to switch between detailed knowledge of component systems and an overall appraisal of the entire system. Current efforts to develop ontologies capturing a "complete" and "universal" understanding of entire systems of systems often result in loss of depth and precision of knowledge contained in the participating systems. This further adds to the uncertainty and intractability in the management of the complex system. In addition, the absence of a single control and execution context makes it difficult to validate the system against desired intent and goals. All of these increase the likelihood of cost, effort and development time overruns in maintaining, enhancing, retiring and replacing systems. In this paper, we propose a novel approach to address these concerns by the application of Omnispective Analysis and Reasoning (OAR), an epistemic framework for managing intellectual concerns. By creating "localized ontologies" for capturing the ’silos’ of knowledge in component systems, we develop artifacts for only those concerns from the participating domains that are identified as relevant. These localized ontologies can unambiguously capture all relevant system artifacts with valuable information about their context of application within the system. With the OAR framework, we can analyze and manage large systems as an aggregation of all these localized ontologies with explicit specification of mutual interactions and influence at the concept, model and implementation levels. This omnispective outlook will not only enable better management of the system development lifecycle by taking into account details of individual subsystems and their interactions, but also facilitate validation and verification of the system. We illustrate this with an example from the Ubuntu software ecosystem.Systems Engineering Society of Australia, Southern Cross Chapter of the International Test & Evaluation Association, Asia Pacific Council on Systems Engineerin

Review of Web Mapping: Eras, Trends and Directions

Web mapping and the use of geospatial information online have evolved rapidly over the past few decades. Almost everyone in the world uses mapping information, whether or not one realizes it. Almost every mobile phone now has location services and every event and object on the earth has a location. The use of this geospatial location data has expanded rapidly, thanks to the development of the Internet. Huge volumes of geospatial data are available and daily being captured online, and are used in web applications and maps for viewing, analysis, modeling and simulation. This paper reviews the developments of web mapping from the first static online map images to the current highly interactive, multi-sourced web mapping services that have been increasingly moved to cloud computing platforms. The whole environment of web mapping captures the integration and interaction between three components found online, namely, geospatial information, people and functionality. In this paper, the trends and interactions among these components are identified and reviewed in relation to the technology developments. The review then concludes by exploring some of the opportunities and directions

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

A cross-disciplinary examination of the user behaviours involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval

Linked Data and Linked Open Data Projects for Libraries, Archives and Museums: Constructing Pathways to Information Discovery and Cultural Heritage Sector Collaboration

This paper examines current Cultural Heritage-based Linked data and linked open data projects developed by Libraries, Archives and Museums (LAMs). The following research questions are explored: R1: Are there similarities and/or differences between libraries, archives and museums in how their linked data and linked open data projects, approaches and strategies are being implemented? R2: What specific linked data and linked open data tools and tactics are being employed, and are there key variations between libraries, archives and museums? The linked data/linked open data landscape has advanced since Tim Berners-Lee (et al.) introduced the concept of the Semantic Web, but challenges for LAMs remain as they work with their collections’ data to create new web-based projects. Fundamental to these efforts is the creation, linking, and publishing of good quality metadata that will allow LAM collections to be discovered, accessed, and disseminated through viable methods. Trends across LAM sectors for linked data and linked open data projects include: global communication and collaborative research, use of wiki-based technologies, and efforts to improve sustainability. Application concepts from the Digital Curation Centre’s Curation Lifecycle Model and Adrian Brown’s Digital Preservation Maturity Model may help guide LAMs toward greater sustainability of linked data and linked open data collections’ projects. Keywords

Site-based data curation: bridging data collection protocols and curatorial processes at scientifically significant sites

Research conducted at scientifically significant sites produces an abundance of important and highly valuable data. Yet, though sites are logical points for coordinating the curation of these data, their unique needs have been under supported. Previous studies have shown that two principal stakeholder groups – scientific researchers and local resource managers – both need information that is most effectively collected and curated early in research workflows. However, well-designed site-based data curation interventions are necessary to accomplish this. Additionally, further research is needed to understand and align the data curation needs of researchers and resource managers, and to guide coordination of the data collection protocols used by researchers in the field and the data curation processes applied later by resource managers. This dissertation develops two case studies of research and curation at scientifically significant sites: geobiology at Yellowstone National Park and paleontology at the La Brea Tar Pits. The case studies investigate: What information do different stakeholders value about the natural sites at which they work? How do these values manifest in data collection protocols, curatorial processes, and infrastructures? And how are sometimes conflicting stakeholder priorities mediated through the use and development of shared information infrastructures? The case studies are developed through interviews with researchers and resource managers, as well as participatory methods to collaboratively develop “minimum information frameworks” – high level models of the information needed by all stakeholders. Approaches from systems analysis are adapted to model data collection and curation workflows, identifying points of curatorial intervention early in the processes of generating and working with data. Additionally, a general information model for site-based data collections is proposed with three classes of information documenting key aspects of the research project, a site’s structure, and individual specimens and measurements. This research contributes to our understanding of how data from scientifically significant sites can be aggregated, integrated and reused over the long term, and how both researcher and resource manager needs can be reflected and supported during information modeling, workflow documentation and the development of data infrastructure policy. It contributes prototypes of minimal information frameworks for both sites, as well as a general model that can serve as the basis for later site-based standards and infrastructure development