COrRect: Connection-Oriented Resource Matching for Vehicular Clouds

Author(s):  
Rahul Kumar ◽  
Abubakar Saad ◽  
Robson E. De Grande
Keyword(s):  
Author(s):  
Sotiris A. Papantonopoulos ◽  
Gavriel Salvendy

Cognitive task allocation employs task analysis to identify the performance and operational requirements of task functions; and demand/resource matching to match the identified requirements and the human and computer resources available for implementation. The current methodologies of cognitive task allocation are either too aggregate to provide adequate resolution of performance requirements or domain-specific and thus of limited applicability. The paper introduces a formal, quantitative, and domain-independent model of cognitive task allocation aimed at reducing the limitations inherent in the currently practiced methodologies. Demand/resource matching is modeled as an Analytic Hierarchy Process. The Analytic Hierarchy Process of Demand/Resource Matching is defined as a mapping process along a four-level Analytic Hierarchy. By means of the Analytic Hierarchy Process, a task function (Level 1 of the Analytic Hierarchy) is analyzed into its cognitive processes (Level 2); performance criteria are set for each cognitive process (Level 3) by means of which the capacities of the human, computer, or interactive human/computer controller (Level 4) are evaluated and compared. The Analytic Hierarchy Process then integrates judgements of human and computer abilities and limitations into a weighted average indicating the relative capacity of human and computer to perform this function. This assessment of relative merit of performance can hence be integrated with work design, economic, and other contextual factors towards the final allocation design. The Analytic Hierarchy Process was applied and evaluated in the design of task allocation in production planing and control of a flexible manufacturing system by comparing the allocation designs of two groups of subjects. One group was supported by the decision model, the other received no decision support. The observed differences between the two groups indicated that the decision model can effectively support detailed task analysis and an adequate resolution of performance requirements; the identification of the design, trade-offs between human allocation and automation; and provide the computational resources to reduce decision bias.


2009 ◽  
Vol 18 (5) ◽  
pp. 623 ◽  
Author(s):  
Mark Borchert ◽  
Claudia M. Tyler

For many geophytes living in Mediterranean ecosystems, the passage of fire can produce bursts of flowering, seed production, vegetative growth, and seedling recruitment. In the present study, we investigated patterns of flowering and fruit production of the chaparral geophyte Chlorogalum pomeridianum (common soap plant) at two sites: one burned in a prescribed fire and one in nearby unburned chaparral. Both sites burned in a wildfire 2 years later, and we continued monitoring marked plants for an additional 6 years, enabling us to observe the effects of recent reburning on reproduction and growth. We found that flowering was stimulated by fire but was not strictly fire-dependent. There was a positive relationship between bulb size and leaf area, as well as between these two characteristics and flower and fruit production. Flower stalk initiation occurred when plants reached a minimum leaf area of ~1000 cm2, indicating that a minimum bulb size must be reached before reproductive effort is initiated. Direct herbivory of flowering stalks reduced fruiting and leaf herbivory indirectly prevented the initiation of flowering stalks. In the first several years after fire, flower and fruit production could be explained by resource matching but in subsequent years, resource matching was replaced by resource switching.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fuli Zhou ◽  
Yandong He ◽  
Panpan Ma ◽  
Raj V. Mahto

PurposeThe booming of the Internet of things (IoT) and artificial intelligence (AI) techniques contributes to knowledge adoption and management innovation for the healthcare industry. It is of great significance to transport the medical resources to required places in an efficient way. However, it is difficult to exactly discover matched transportation resources and deliver to its destination due to the heterogeneity. This paper studies the medical transportation resource discovery mechanism, leading to efficiency improvement and operational innovation.Design/methodology/approachTo solve the transportation resource semantic discovery problem under the novel cloud environment, the ontology modelling approach is used for both transportation resources and tasks information modes. Besides, medical transportation resource discovery mechanism is proposed, and resource matching rules are designed including three stages: filtering reasoning, QoS-based matching and user preferences-based rank to satisfy personalized demands of users. Furthermore, description logic rules are built to express the developed matching rules.FindingsAn organizational transportation case is taken as an example to describe the medical transportation logistics resource semantic discovery process under cloud medical service scenario. Results derived from the proposed semantic discovery mechanism could assist operators to find the most suitable resources.Research limitations/implicationsThe case study validates the effectiveness of the developed transportation resource semantic discovery mechanism, contributing to knowledge management innovation for the medical logistics industry.Originality/valueTo improve task-resource matching accuracy under cloud scenario, this study develops a transportation resource semantic discovery procedure from the viewpoint of knowledge management. The novel knowledge management practice contributes to operational management of the cloud medical logistics service by introducing ontology modelling and creative management.


1997 ◽  
Vol 24 (3) ◽  
pp. 295-304 ◽  
Author(s):  
Punam Anand Keller ◽  
Lauren G. Block
Keyword(s):  

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