scholarly journals Overseer: A Mobile Context-Aware Collaboration and Task Management System for Disaster Response

2010 ◽  
Author(s):  
Faisal Luqman ◽  
Martin Griss
Keyword(s):  
Disaster Response ◽  
Management System ◽  
Context Aware ◽  
Task Management ◽  
Mobile Context ◽  
And Task

XAC Project

2010 ◽  
pp. 214-231
Author(s):  
Kenji Tei ◽  
Shunichiro Suenaga ◽  
Yoshiyuki Nakamura ◽  
Yuichi Sei ◽  
Hikotoshi Nakazato ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Pervasive Computing ◽  
Management System ◽  
Wireless Sensor ◽  
Context Aware ◽  
Task Management ◽  
Development Cost ◽  
Description Language ◽  
Context Management ◽  
Research Issues

In pervasive computing environment (Satyanarayanan, 2001), common context management system, that make context of the real world be shared among the context-aware applications, is required to reduce development cost of each context-aware applications. A wireless sensor network (WSN) will be a key infrastructure for the context management system. Towards pervasive computing, a WSN integrated into context management system should be open infrastructure. In an open WSN should (1)handle various kinds of tasks, (2)manage tasks at runtime, (3)save resource consumption, and (4)adapt to changes of environments. To develop such an open WSN, middleware supports are needed, and our XAC project tries to develop a middleware for the open WSN. The XAC project is a research project to develop a middleware for open WSN. In this chapter, the auhors show research issues related to open WSN from the viewpoints of task description language, runtime task management, self-adaptability, and security.

Grounded Task Prioritization with Context-Aware Sequential Ranking

2022 ◽  
Vol 40 (4) ◽  
pp. 1-28
Author(s):  
Chuxu Zhang ◽  
Julia Kiseleva ◽  
Sujay Kumar Jauhar ◽  
Ryen W. White
Keyword(s):  
Management System ◽  
Large Scale ◽  
Digital Tools ◽  
Context Aware ◽  
Task Management ◽  
Ranking Models ◽  
Extensive Analysis ◽  
Management Application ◽  
History Of ◽  
Task Prioritization

People rely on task management applications and digital assistants to capture and track their tasks, and help with executing them. The burden of organizing and scheduling time for tasks continues to reside with users of these systems, despite the high cognitive load associated with these activities. Users stand to benefit greatly from a task management system capable of prioritizing their pending tasks, thus saving them time and effort. In this article, we make three main contributions. First, we propose the problem of task prioritization, formulating it as a ranking over a user’s pending tasks given a history of previous interactions with a task management system. Second, we perform an extensive analysis on the large-scale anonymized, de-identified logs of a popular task management application, deriving a dataset of grounded, real-world tasks from which to learn and evaluate our proposed system. We also identify patterns in how people record tasks as complete, which vary consistently with the nature of the task. Third, we propose a novel contextual deep learning solution capable of performing personalized task prioritization. In a battery of tests, we show that this approach outperforms several operational baselines and other sequential ranking models from previous work. Our findings have implications for understanding the ways people prioritize and manage tasks with digital tools, and in the design of support for users of task management applications.

A computer‐based task management system for junior medical officers during after‐hours shifts

2014 ◽  
Vol 200 (2) ◽  
pp. 79-80
Author(s):  
Nicholas J Whitehead ◽  
O'Neil N Maharaj ◽  
Michael Agrez
Keyword(s):  
Management System ◽  
Task Management ◽  
Computer Based ◽  
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