Service Development as Action Design Research: Reporting on a Servitized E-Recruiting Portal

In this paper we reflect retrospectively on an e-recruiting service design and development project action design research. The project itself pre-dated the publication of the Action Design Research Method by Sein, Henfridsson et al., (2011). When viewed as action design research, we find that many of the principles of ADR, such as defining the problem as an instance of a class of problem, practice inspired research, mutually influential roles and guided emergence are not only synergistic with service design, but in fact, the effective design of services embeds and requires a similar approach. To this extent, we considered ADR to be an appropriate choice for services research, development and implementation at the nexus of theory and practice. We further identified some extensions and elaborations to the ADR method in a service development context. In particular, we posit that guided emergence occurs between the theoretical foundations of a service project and the artefact development, as well as between the artefact development and the organizational context. We find that in a multi-disciplinary project, theoretical contributions may be emergent, and multiple theoretical contributions are possible using a range of different lenses. We also identify some practical difficulties with reporting the learning from service development projects. Overall, we found that ADR was likely to be a highly appropriate approach for framing and deriving learning from innovative service design projects, but may require further enhancement

Pedagogically-driven Ontology Network for Conceptualizing the e-Learning Assessment Domain

The use of ontologies as tools to guide the generation, organization and personalization of e-learning content, including e-assessment, has drawn attention of the researchers because ontologies can represent the knowledge of a given domain and researchers use the ontology to reason about it. Although the use of these semantic technologies tends to enhance technology-based educational processes, the lack of validation to improve the quality of learning in their use makes the educator feel reluctant to use them. This paper presents progress in the development of an ontology network, called AONet, that conceptualizes the e-assessment domain with the aim of supporting the semi-automatic generation of assessment, taking into account not only technical aspects but also pedagogical ones.Fil: Romero, Lucila. Universidad Nacional del Litoral; ArgentinaFil: North, Matthew. The college of Idabo; Estados UnidosFil: Gutierrez, Milagros. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; ArgentinaFil: Caliusco, Maria Laura. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentin

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

Combined AI Capabilities for Enhancing Maritime Safety in a Common Information Sharing Environment

The complexity of maritime traffic operations indicates an unprecedented necessity for joint introduction and exploitation of artificial intelligence (AI) technologies, that take advantage of the vast amount of vessels’ data, offered by disparate surveillance systems to face challenges at sea. This paper reviews the recent Big Data and AI technology implementations for enhancing the maritime safety level in the common information sharing environment (CISE) of the maritime agencies, including vessel behavior and anomaly monitoring, and ship collision risk assessment. Specifically, the trajectory fusion implemented with InSyTo module for soft information fusion and management toolbox, and the Early Notification module for Vessel Collision are presented within EFFECTOR Project. The focus is to elaborate technical architecture features of these modules and combined AI capabilities for achieving the desired interoperability and complementarity between maritime systems, aiming to provide better decision support and proper information to be distributed among CISE maritime safety stakeholders

A framework for constructing a common knowledge base for human-machine system to perform maintenance tasks

A reliable and comprehensive maintenance is important to promise the system running in a normal state, but it is skill-intensive and heavily dependent on human labor. With the development of predictive maintenance in industry, an optimized solution can be posed for maintaining assets with less downtime and cost. However, most of current research on this topic is limited on a top-level algorithm design for prediction, but few consider how to perform the maintenance tasks according to the prediction results at a particular occasion and condition. Besides, the complexity of system is exploded, and it may take people much effort to cover every detail to achieve a credible maintenance result. Thus, machine is introduced to collaborate with human by undertaking some work and suggesting actions to take in order to reduce human physical and mental workload. This paper aims to present a framework to integrate human knowledge and machine learning into a common knowledge base to enable human and machine can contribute to shift the final maintenance decision from planning to performing. The proposed framework is based on a knowledge graph generated by ontology and machine learning, which can be conveniently retrieved by human via questions answering system or visualization platform and efficiently computed by machine via graph representation learning. Consequently, domain knowledge can be formally represented, systematically managed and easily reused by human-machine teaming to attack domain-specific problems. In a long term, the evolving knowledge based, with an accumulation on samples and information, can guide the team to draw a reasonable and delicate strategy for overhaul and recondition, moreover, ensure the next generation of maintenance: prescriptive maintenance.DMG Mor

Tools for enterprises collaboration in virtual enterprises

Virtual Enterprise (VE) is an organizational collaboration concept which provides a competitive edge in the globalized business environment. The life cycle of a VE consists of four stages i.e. opportunity identification (Pre-Creation), partner selection (Creation), operation and dissolution. The success of VEs depends upon the efficient execution of their VE-lifecycles along with knowledge enhancement for the partner enterprises to facilitate the future formation of efficient VEs. This research aims to study the different issues which occur in the VE lifecycle and provides a platform for the formation of high performance enterprises and VEs. In the pre-creation stage, enterprises look for suitable partners to create their VE and to exploit a market opportunity. This phase requires explicit and implicit information extraction from enterprise data bases (ECOS-ontology) for the identification of suitable partners. A description logic (DL) based query system is developed to extract explicit and implicit information and to identify potential partners for the creation of the VE. In the creation phase, the identified partners are analysed using different risks paradigms and a cooperative game theoretic approach is used to develop a revenue sharing mechanism based on enterprises inputs and risk minimization for optimal partner selection. In the operation phases, interoperability remains a key issue for seamless transfer of knowledge information and data. DL-based ontology mapping is applied in this research to provide interoperability in the VE between enterprises with different domains of expertise. In the dissolution stage, knowledge acquired in the VE lifecycle needs to be disseminated among the enterprises to enhance their competitiveness. A DL-based ontology merging approach is provided to accommodate new knowledge with existing data bases with logical consistency. Finally, the proposed methodologies are validated using the case study. The results obtained in the case study illustrate the applicability and effectiveness of proposed methodologies in each stage of the VE life cycle

Analysis Of Aircraft Arrival Delay And Airport On-time Performance

While existing grid environments cater to specific needs of a particular user community, we need to go beyond them and consider general-purpose large-scale distributed systems consisting of large collections of heterogeneous computers and communication systems shared by a large user population with very diverse requirements. Coordination, matchmaking, and resource allocation are among the essential functions of large-scale distributed systems. Although deterministic approaches for coordination, matchmaking, and resource allocation have been well studied, they are not suitable for large-scale distributed systems due to the large-scale, the autonomy, and the dynamics of the systems. We have to seek for nondeterministic solutions for large-scale distributed systems. In this dissertation we describe our work on a coordination service, a matchmaking service, and a macro-economic resource allocation model for large-scale distributed systems. The coordination service coordinates the execution of complex tasks in a dynamic environment, the matchmaking service supports finding the appropriate resources for users, and the macro-economic resource allocation model allows a broker to mediate resource providers who want to maximize their revenues and resource consumers who want to get the best resources at the lowest possible price, with some global objectives, e.g., to maximize the resource utilization of the system