Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Past, present and future of information and knowledge sharing in the construction industry: Towards semantic service-based e-construction

The paper reviews product data technology initiatives in the construction sector and provides a synthesis of related ICT industry needs. A comparison between (a) the data centric characteristics of Product Data Technology (PDT) and (b) ontology with a focus on semantics, is given, highlighting the pros and cons of each approach. The paper advocates the migration from data-centric application integration to ontology-based business process support, and proposes inter-enterprise collaboration architectures and frameworks based on semantic services, underpinned by ontology-based knowledge structures. The paper discusses the main reasons behind the low industry take up of product data technology, and proposes a preliminary roadmap for the wide industry diffusion of the proposed approach. In this respect, the paper stresses the value of adopting alliance-based modes of operation

A lightweight approach to research object data packaging

A Research Object (RO) provides a machine-readable mechanism to communicate the diverse set of digital and real-world resources that contribute to an item of research. The aim of an RO is to evolve from traditional academic publication as a static PDF, to rather provide a complete and structured archive of the items (such as people, organisations, funding, equipment, software etc) that contributed to the research outcome, including their identifiers, provenance, relations and annotations. This is of particular importance as all domains of research and science are increasingly relying on computational analysis, yet we are facing a reproducibility crisis because key components are often not sufficiently tracked, archived or reported. Here we propose Research Object Crate (or RO-Crate for short), an emerging lightweight approach to packaging research data with their structured metadata, rephrasing the Research Object model as schema.org annotations to formalize a JSON-LD format that can be used independently of infrastructure, e.g. in GitHub or Zenodo archives. RO-Crate can be extended for domain-specific descriptions, aiming at a wide variety of applications and repositories to encourage FAIR sharing of reproducible datasets and analytical methods.Abstract accepted for talk at Bioinformatics Open Source Conference (BOSC2019). Slides https://doi.org/10.7490/f1000research.1117129.1 Poster https://doi.org/10.7490/f1000research.1117130.1 Video recording https://www.youtube.com/watch?v=AociW94muL

Development of semantically rich 3D retrofit models

The use of Building Information Modeling (BIM) has gained considerable interest in new build projects. However, its use in existing assets has been limited to geometric models utilizing point cloud data (PCD) as the primary source of data. The inclusion of nongeometrical data from distributed sources in the geometric model to make it semantically rich has considerable challenges. This paper proposes an approach to provide a framework for generating semantically-rich parametric models for existing assets. Although the geometric information such as length, width, area, and volume can be extracted from PCD, nongeometric data may need to be appended to this to generate genuinely semantically rich models. The comma-separated values (CSV) format is used to represent the data that can be extracted from PCD. In addition, the nongeometric information derived from other sources are appended to the CSV file. Subsequently, the resource description framework (RDF) data are generated from the data in the CSV files. RDF is a commonly used Semantic Web technology for storing, sharing, and reusing information on the web. The RDF data then are used to create the Industry Foundation Classes (IFC) data model by translating RDF into IFC. The IFC file is used to generate three-dimensional (3D) BIM by importing it into any IFC compliant application. The proposed approach was validated on one part of the Edinburgh Castle, a relatively complex historical building. The choice of building for validating the approach was driven by technical as well as pragmatic reasons. Technically, the robustness of the approach would have been proven if it were shown to work for a complex rather than a relatively simple building. Pragmatically, the authors had access to data on Edinburgh Castle due to an ongoing partnership with Historic Environment Scotland (HES). However, as a result of the validation process, it is suggested that the proposed approach should be applicable to any existing building

Digital accessible knowledge: Mobilizing legacy data and the future of taxonomic publishing

No abstract available

DRIVER Technology Watch Report

This report is part of the Discovery Workpackage (WP4) and is the third report out of four deliverables. The objective of this report is to give an overview of the latest technical developments in the world of digital repositories, digital libraries and beyond, in order to serve as theoretical and practical input for the technical DRIVER developments, especially those focused on enhanced publications. This report consists of two main parts, one part focuses on interoperability standards for enhanced publications, the other part consists of three subchapters, which give a landscape picture of current and surfacing technologies and communities crucial to DRIVER. These three subchapters contain the GRID, CRIS and LTP communities and technologies. Every chapter contains a theoretical explanation, followed by case studies and the outcomes and opportunities for DRIVER in this field

Metadata enrichment for digital heritage: users as co-creators

This paper espouses the concept of metadata enrichment through an expert and user-focused approach to metadata creation and management. To this end, it is argued the Web 2.0 paradigm enables users to be proactive metadata creators. As Shirky (2008, p.47) argues Web 2.0’s social tools enable “action by loosely structured groups, operating without managerial direction and outside the profit motive”. Lagoze (2010, p. 37) advises, “the participatory nature of Web 2.0 should not be dismissed as just a popular phenomenon [or fad]”. Carletti (2016) proposes a participatory digital cultural heritage approach where Web 2.0 approaches such as crowdsourcing can be sued to enrich digital cultural objects. It is argued that “heritage crowdsourcing, community-centred projects or other forms of public participation”. On the other hand, the new collaborative approaches of Web 2.0 neither negate nor replace contemporary standards-based metadata approaches. Hence, this paper proposes a mixed metadata approach where user created metadata augments expert-created metadata and vice versa. The metadata creation process no longer remains to be the sole prerogative of the metadata expert. The Web 2.0 collaborative environment would now allow users to participate in both adding and re-using metadata. The case of expert-created (standards-based, top-down) and user-generated metadata (socially-constructed, bottom-up) approach to metadata are complementary rather than mutually-exclusive. The two approaches are often mistakenly considered as dichotomies, albeit incorrectly (Gruber, 2007; Wright, 2007) . This paper espouses the importance of enriching digital information objects with descriptions pertaining the about-ness of information objects. Such richness and diversity of description, it is argued, could chiefly be achieved by involving users in the metadata creation process. This paper presents the importance of the paradigm of metadata enriching and metadata filtering for the cultural heritage domain. Metadata enriching states that a priori metadata that is instantiated and granularly structured by metadata experts is continually enriched through socially-constructed (post-hoc) metadata, whereby users are pro-actively engaged in co-creating metadata. The principle also states that metadata that is enriched is also contextually and semantically linked and openly accessible. In addition, metadata filtering states that metadata resulting from implementing the principle of enriching should be displayed for users in line with their needs and convenience. In both enriching and filtering, users should be considered as prosumers, resulting in what is called collective metadata intelligence