A Community-Consensus Approach to Knowledge Interoperability Within Heterogeneous Earth System Science Based Observational Systems

Within complex domains - such as Earth System Science - knowledge is constantly evolving. Observational systems used to observe Earth’s complex processes are often built in isolation, and data representations are not adequately designed for secondary use and higher order knowledge generation. Cross-community sharing of computable information is therefore difficult to achieve. Barriers to interoperability of information means that specialists cannot fully exploit the data that may be available. Much of the work done to date within the Information Science community has been to enable interoperability through standardisation, particularly at the syntactic level. The Open Geospatial Consortium’s (OGC) Observations & Measurements (O&M) standard [1] is a good example of the ongoing work towards enabling interoperability among observational systems. However, standards have a codifying and constraining effect on information. Object-oriented approaches commonly employed assume a static understanding of entities or classes of information. Therefore, these design methodologies cannot represent the true nature of knowledge within an evolving domain. Standards such as O&M avoid over constraining information objects by allowing variability. Where variability exists, interoperability is often compromised for individual use-cases. The Health domain also faces similar challenges to representing complex and evolving domain concepts. Within complex domains two categories or levels of domain concepts exist. Those concepts that remain stable over a long period of time, and those concepts that are prone to change, as the domain knowledge evolves. Health informaticians have developed a sophisticated two-level systems design approach for electronic health documentation over many years, and with the use of archetypes, have shown how knowledge interoperability among heterogeneous systems can be achieved [2]. The authors are currently engaged in translating two-level modelling approaches to geo-observational based systems [3][4]. A key differentiator of two-level modelling compared to other approaches is that it allows domain experts to be the primary drivers of digital artefacts, while also ensuring that technical validity is maintained in one highly accessible and integrated process; leading to a managed and interoperable extensibility mechanism to standards such as O&M. This presentation will highlight this ongoing work and demonstrate the tools under development to allow domain practitioners to define and manage a set of Earth System Science community defined archetypes to enable interoperability, beyond the syntactic level of observational systems. [1] S. Cox, Observations and measurements, Open Geospatial Consortium Best Practices Document. Open Geospatial Consortium, 2006. [2] T. Beale, Archetypes: Constraint-based domain models for future-proof information systems, in OOPSLA 2002 Workshop on Behavioral Semantics, 2002. [3] P. Stacey, D. Berry, “Applying two-level modelling to remote sensor systems design to enable future knowledge generation,” in IEEE YP Conference in Remote Sensing Abstracts, Barcelona, 2015. [4] P. Stacey, D. Berry, “Design and Implementation of an Archetype Based Interoperable Knowledge EcoSystem for Data Buoys” [in press] to appear in proceedings of IEEE/MTS Oceans conference, Aberdeen, June 2017

Towards a Digital Earth: Using Archetypes to Enable Knowledge Interoperability within Geo-Observational Sensor Systems Design

Earth System Science (ESS) observational data are often inadequately semantically enriched by geo-observational information systems in order to capture the true meaning of the associated data sets. Data models underpinning these information systems are often too rigid in their data representation to allow for the ever-changing and evolving nature of ESS domain concepts. This impoverished approach to observational data representation reduces the ability of multi-disciplinary practitioners to share information in a computable way. Object oriented techniques typically employed to model data in a complex domain (with evolving domain concepts) can unnecessarily exclude domain specialists from the design process, invariably leading to a mismatch between the needs of the domain specialists, and how the concepts are modelled. In many cases, an over simplification of the domain concept is captured by the computer scientist. This paper proposes that two-level modelling methodologies developed by Health Informaticians to tackle similar problems of specific domain use-case knowledge modelling can be re-used within ESS Informatics. A proposed methodology to re-use two-level modelling within geo-observational sensor systems is described. We show how the Open Geospatial Consortium’s (OGC) Observations & Measurements (O&M) standard can act as a pragmatic solution for a stable reference-model (necessary for two-level modelling), and upon which more volatile domain specific concepts can be defined and managed using archetypes. A use-case is presented, followed by a worked example showing the implementation methodology and considerations leading to an O&M based, two-level modelling design approach, to realise semantically rich and interoperable Earth System Science based geo-observational sensor systems

Computer Aided Drawing software delivered through Emotional Learning. The use of Emoticons and GIFs as a Tool for Increasing Student Engagement.

It is known that one of the key factors for many manufacturing companies, who are involved in the design and development process, is represented by the quality of the skills, capacity and experience of computer-aided design draftsman and designers. This means that effective, up-to-date and engaging training has to be performed by teachers and instructors, since the early stage lectures for novice engineering students. When learners are engaged and actively participate in the training process, then this transfers in to a high, deep level of learning, quality of the learnt topics and perceived passion. The following question arises, “how can the process of improving the absorption of information concerning Computer Aided Drawing software lectures, through an iterative, engaging process, be facilitated?”. This work represents a fist attempt to analyse and discuss, by using some of the main theories related to the learning process, how student engagement can be positively affected by using emoticons and GIFs during CAD software lectures

Extending Two-level Information Modeling to the Internet of Things

Interoperability is a major challenge for the Internet of Things (IoT). The real potential of the IoT lies in facilitating largescale sharing of high-quality context-rich information through systems-of-IoT-systems, rather than IoT systems that operate as isolated technology silos. Real large-scale interoperability requires layers of standards, and each layer addresses different interoperability challenges. The SensorThings API data model seeks to tackle data interoperability at the data and informational layers of IoT platforms. SensorThings API is aligned to the ISO/OGC O&M data standard, and like O&M it is semistructured. Semi-structured models allow for variance within implementations for different use-cases, which is both necessary and detrimental to systems interoperability. In this paper we propose that the SensorThings API data model should be defined as a set of archetypes, used to capture extensible domain concepts using a two-level modeling IoT systems design approach. Extending two-level modeling to the IoT using the SensorThings API as a base for domain concepts definition allows for a powerful framework to manage variance within systems implementation and maintaining semantic interoperability within systems-of-IoT-systems across diverse use-cases

Interoperable Ocean Observing Using Archetypes: A Use-case Based Evaluation

This paper presents a use-case based evaluation of the impact of two-level modeling on the automatic federation of ocean observational data. The goal of the work is to increase the interoperability and data quality of aggregated ocean observations to support convenient discovery and consumption by applications. An assessment of the interoperability of served data flows from publicly available ocean observing spatial data infrastructures was performed. Barriers to consumption of existing standards-compliant ocean-observing data streams were examined, including the impact of adherence to agreed data standards. Historical data flows were mapped to a set of archetypes and a backward integration experiment was performed to assess the incremental benefit of using two level models to federate data streams. The outcome of the evaluation demonstrates the feasibility of building a two-level model based ocean observing system using a combination of existing open source components, the adaptation of existing standards and the development of new software tools. The automatic integration of data flows becomes possible. This technique also allows real-time applications to automatically discover and federate newly discovered data flows and observations

Design and Implementation of an Archetype Based Interoperable Knowledge Eco-system for Data Buoys

This paper describes the ongoing work of the authors in translating two-level system design techniques used in Health Informatics to the Earth Systems Science domain. Health informaticians have developed a sophisticated two-level systems design approach for electronic health documentation over many years, and with the use of archetypes, have shown how knowledge interoperability among heterogeneous systems can be achieved. Translating two-level modelling techniques to a new domain is a complex task. A proof-of-concept archetype enabled data buoy eco-system is presented. The concept of operational templates-as-a service is proposed. Design recommendations and implementation experiences of re-working the proposed architecture to run on ultra-resource constrained data buoy platforms using templates-as-service are described

Applying Two-Level Modelling to Remote Sensor Systems Design to Enable Future Knowledge Generation

Geographical Information Scientists have a need to combine data from many sources and in various ways to synthesize new understanding, producing new knowle­dge [1]. Remote sensor deployments, monitoring environmental phenomena, are a huge provider of valuable data. Often, observation systems are built in isolation, and the data representations are not adequately designed for re-use and higher order knowledge generation. There are many standards that allow syntactic interoperability and sharing of remote sensor systems observational data, such as the OGC’s suite of standards [2]. However, semantic interoperability remains a work in progress [3] [4]. This presentation describes how system design techniques used in the health informatics domain [5] to tackle similar problems of how data, information and knowledge concepts are modelled and managed can be applied to remote sensing applications. Much like the health domain, remotely sensed data is traditionally modelled from a computer science perspective. Traditional object-oriented techniques typically used to model complex data are insufficient in a geographical data context, as they are too stringent during the early stages of knowledge acquisition. Standards such as O&M on their own precipitate a codifying effect as systems are developed, constraining rapidly evolving information [6]. The authors have investigated the OGC’s O&M standard as a reference model to underpin a two-level modelling approach. An augmented O&M model has been developed and is presented along with a worked example of how a two-level modelling approach using O&M as the reference model can be applied to modelling a marine data buoy. [1] M. Gahegan and W. Pike, A situated knowledge representation of geographical information, Transactions in GIS, vol. 10, pp. 727-749, 2006. [2] M. Botts, G. Percivall, C. Reed and J. Davidson, OGC® sensor web enablement: Overview and high level architecture, in GeoSensor Networks Springer, 2008, pp. 175-190. [3] S. Cox, An explicit OWL representation of ISO/OGC observations and measurements. in Ssn@ Iswc, 2013, pp. 1-18. [4] A. M. Leadbetter, R. K. Lowry and D. O. Clements, Putting meaning into NETMAR–the open service network for marine environmental data, International Journal of Digital Earth, pp. 1-18, 2013. [5] T. Beale, Archetypes: Constraint-based domain models for future-proof information systems, in OOPSLA 2002 Workshop on Behavioural Semantics, 2002. [6] M. F. Goodchild, GIScience ten years after Ground Truth, Transactions in GIS, vol. 10, pp. 687-692, 2006

Clinical coverage of an archetype repository over SNOMED-CT

AbstractClinical archetypes provide a means for health professionals to design what should be communicated as part of an Electronic Health Record (EHR). An ever-growing number of archetype definitions follow this health information modelling approach, and this international archetype resource will eventually cover a large number of clinical concepts. On the other hand, clinical terminology systems that can be referenced by archetypes also have a wide coverage over many types of health-care information.No existing work measures the clinical content coverage of archetypes using terminology systems as a metric. Archetype authors require guidance to identify under-covered clinical areas that may need to be the focus of further modelling effort according to this paradigm.This paper develops a first map of SNOMED-CT concepts covered by archetypes in a repository by creating a so-called terminological Shadow. This is achieved by mapping appropriate SNOMED-CT concepts from all nodes that contain archetype terms, finding the top two category levels of the mapped concepts in the SNOMED-CT hierarchy, and calculating the coverage of each category. A quantitative study of the results compares the coverage of different categories to identify relatively under-covered as well as well-covered areas. The results show that the coverage of the well-known National Health Service (NHS) Connecting for Health (CfH) archetype repository on all categories of SNOMED-CT is not equally balanced. Categories worth investigating emerged at different points on the coverage spectrum, including well-covered categories such as Attributes, Qualifier value, under-covered categories such as Microorganism, Kingdom animalia, and categories that are not covered at all such as Cardiovascular drug (product)

Towards an ontology for soft robots: What is soft?

The advent of soft robotics represents a profound change in the forms robots will take in the future. However, this revolutionary change has already yielded such a diverse collection of robots that attempts at defining this group do not reflect many existing ‘soft’ robots. This paper aims to address this issue by scrutinising a number of descriptions of soft robots arising from a literature review with the intention of determining a coherent meaning for soft. We also present a classification of existing soft robots to initiate the development of a soft robotic ontology. Finally, discrepancies in prescribed ranges of Young’s modulus, a frequently used criterion for the selection of soft materials, are explained and discussed. A detailed visual comparison of these ranges and supporting data is also presented

Detection of Pause in a Pedestrian’s Movement on a Linear Walkway using Bluetooth Low Energy Received Signal Strength Indicator

In recent years, Bluetooth Low Energy (BLE) has amassed significant attention in several applications. Its potential, however, remains largely unexplored for understanding pedestrian behaviour. This study focuses on investigating the potential of BLE in identifying pedestrian activity in an outdoor linear walkway. We specifically examine the likelihood of detecting pauses in the movement of pedestrians on a linear walkway using the strength of the signals obtained from a BLE device carried by the pedestrian. To accomplish this, a volunteer pedestrian intentionally pauses at selected points on the chosen walkway for varying predetermined intervals. The obtained data was conditioned using a polynomial curve to reduce the impact of anomalous data and was subsequently used to detect flatness in the trend of the signals to identify a pause. This flatness was identified using a sliding window standard deviation (SD) calculation over the curve obtained through polynomial fitting. Our results indicate a strong likelihood of detecting long pauses in a pedestrian’s journey