163,996 research outputs found
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
User Feedback-Informed Interface Design for Flow Management Data and Services (FMDS)
The transition to a microservices-based Flow Management Data and Services
(FMDS) architecture from the existing Traffic Flow Management System (TFMS) is
a critical enabler of the vision for an Information-Centric National Airspace
System (NAS). The need to design a user-centric interface for FMDS is a key
technical gap, as this interface connects NAS data and services to the traffic
management specialists within all stakeholder groups (e.g., FAA, airlines). We
provide a research-driven approach towards designing such a graphical user
interface (GUI) for FMDS. Major goals include unifying the more than 50
disparate traffic management services currently hosted on TFMS, as well as
streamlining the process of evaluating, modeling, and monitoring Traffic
Management Initiatives (TMIs). Motivated by this, we iteratively designed a GUI
leveraging human factors engineering and user experience design principles, as
well as user interviews. Through user testing and interviews, we identify
workflow benefits of our GUI (e.g., reduction in task completion time), along
with next steps for developing a live prototype.Comment: 8 pages, 8 figure
Proposing a holistic framework for the assessment and management of manufacturing complexity through data-centric and human-centric approaches
A multiplicity of factors including technological innovations, dynamic operating environments, and globalisation are all believed to contribute towards the ever-increasing complexity of manufacturing systems. Although complexity is necessary to meet functional needs, it is important to assess and monitor it to reduce life-cycle costs by simplifying designs and minimising failure modes. This research paper identifies and describes two key industrially relevant methods for assessing complexity, namely a data-centric approach using the information theoretic method and a human-centric approach based on surveys and questionnaires. The paper goes onto describe the benefits and shortcomings of each and contributes to the body of knowledge by proposing a holistic framework that combines both assessment methods
Proposing a Holistic Framework for the Assessment and Management of Manufacturing Complexity through Data-centric and Human-centric Approaches
A multiplicity of factors including technological innovations, dynamic operating environments, and globalisation are all believed to contribute towards the ever-increasing complexity of manufacturing systems. Although
complexity is necessary to meet functional needs, it is important to assess and monitor it to reduce life-cycle
costs by simplifying designs and minimising failure modes. This research paper identifies and describes two
key industrially relevant methods for assessing complexity, namely a data-centric approach using the information theoretic method and a human-centric approach based on surveys and questionnaires. The paper goes on
to describe the benefits and shortcomings of each and contributes to the body of knowledge by proposing a
holistic framework that combines both assessment methods
SHAMAN : Symbolic and Human-centric view of dAta MANagement
National audienc
G-Sense: a scalable architecture for global sensing and monitoring
The pervasiveness of cellular phones combined with Internet connectivity, GPS embedded chips, location information, and integrated sensors provide an excellent platform to collect data about the individual and its surrounding environment. As a result, new applications have recently appeared to address large-scale societal problems as well as improve the quality of life of the individual. However, these new applications, recently called location-based services, participatory sensing, and human-centric sensing, bring many new challenges, one of them being the management of the huge amount of traffic (data) they generate. This article presents G-Sense, for Global-Sense, an architecture that integrates mobile and static wireless sensor networks in support of location-based services, participatory sensing, and human-centric sensing applications. G-Sense includes specific mechanisms to control the amount of data generated by these applications while meeting the application requirements. Furthermore, it creates a network of servers organized in a peer-to-peer architecture to address scalability and reliability issues. An example prototype application is presented along with some basic results and open research issues
A Human-centric Perspective on Digital Consenting: The Case of GAFAM
According to different legal frameworks such as the European General Data Protection Regulation (GDPR), an end-user's consent constitutes one of the well-known legal bases for personal data processing. However, research has indicated that the majority of end-users have difficulty in understanding what they are consenting to in the digital world. Moreover, it has been demonstrated that marginalized people are confronted with even more difficulties when dealing with their own digital privacy. In this research, we use an enactivist perspective from cognitive science to develop a basic human-centric framework for digital consenting. We argue that the action of consenting is a sociocognitive action and includes cognitive, collective, and contextual aspects. Based on the developed theoretical framework, we present our qualitative evaluation of the consent-obtaining mechanisms implemented and used by the five big tech companies, i.e. Google, Amazon, Facebook, Apple, and Microsoft (GAFAM). The evaluation shows that these companies have failed in their efforts to empower end-users by considering the human-centric aspects of the action of consenting. We use this approach to argue that their consent-obtaining mechanisms violate principles of fairness, accountability and transparency. We then suggest that our approach may raise doubts about the lawfulness of the obtained consent—particularly considering the basic requirements of lawful consent within the legal framework of the GDPR
A First Approach on Modelling Staff Proactiveness in Retail Simulation Models
There has been a noticeable shift in the relative composition of the industry in the developed countries in recent years; manufacturing is decreasing while the service sector is becoming more important. However, currently most simulation models for investigating service systems are still built in the same way as manufacturing simulation models, using a process-oriented world view, i.e. they model the flow of passive entities through a system. These kinds of models allow studying aspects of operational management but are not well suited for studying the dynamics that appear in service systems due to human behaviour. For these kinds of studies we require tools that allow modelling the system and entities using an object-oriented world view, where intelligent objects serve as abstract \'actors\' that are goal directed and can behave proactively. In our work we combine process-oriented discrete event simulation modelling and object-oriented agent based simulation modelling to investigate the impact of people management practices on retail productivity. In this paper, we reveal in a series of experiments what impact considering proactivity can have on the output accuracy of simulation models of human centric systems. The model and data we use for this investigation are based on a case study in a UK department store. We show that considering proactivity positively influences the validity of these kinds of models and therefore allows analysts to make better recommendations regarding strategies to apply people management practices.Retail Performance, Management Practices, Proactive Behaviour, Service Experience, Agent-Based Modelling, Simulation
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