CRUSH: Data collection and analysis framework for power capped data intensive computing

In the event of natural or man-made disasters, geospatial video analytics is valuable to provide situational awareness that can be extremely helpful for first responders. However, geospatial video analytics demands massive imagery/video data 'collection' from Internet-of-Things (IoT) and their seamless 'computation/consumption' within a geo-distributed (edge/core) cloud infrastructure in order to cater to user Quality of Experience (QoE) expectations. Thus, the edge computing needs to be designed with a reliable performance while interfacing with the core cloud to run computer vision algorithms. This is because infrastructure edges near locations generating imagery/video content are rarely equipped with high-performance computation capabilities. This thesis addresses challenges of interfacing edge and core cloud computing within the geo-distributed infrastructure as a novel 'function-centric computing' paradigm that brings new insights to computer vision, edge routing and network virtualization areas. Specifically, we detail the state-of-the-art techniques and illustrate our new/improved solution approaches based on function-centric computing for the two problems of: (i) high-throughput data collection from IoT devices at the wireless edge, and (ii) seamless data computation/consumption within the geo-distributed (edge/core) cloud infrastructure. To address (i), we present a novel deep learning-augmented geographic edge routing that relies on physical area knowledge obtained from satellite imagery. To address (ii), we describe a novel reliable service chain orchestration framework that builds upon microservices and utilizes a novel 'metapath composite variable' approach supported by a constrained-shortest path finder. Finally, we show both analytically and empirically, how our geographic routing, constrained shortest path finder and reliable service chain orchestration approaches that compose our function-centric computing framework are superior than many traditional and state-of-the-art techniques. As a result, we can significantly speedup (up to 4 times) data-intensive computing at infrastructure edges fostering effective disaster relief coordination to save lives.

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Applied tensional analysis: Engaging practitioners and the constitutive shift

Management Learning ◽

10.1177/1350507619849604 ◽

2019 ◽

Vol 50 (4) ◽

pp. 409-426 ◽

Cited By ~ 1

Author(s):

Jennifer J Mease

Keyword(s):

Data Collection ◽

Methodological Approach ◽

Organizational Knowledge ◽

Methodological Framework ◽

Analysis Framework ◽

Data Collection And Analysis ◽

The Everyday ◽

Organizational Experiences

This article introduces applied tensional analysis as a methodological framework that integrates constitutive ontologies (that depict organizations as processes in constant states of emerging or becoming) with the applied need for practitioners to understand and navigate the everyday exigencies of their organizational experiences. Applied tensional analysis centers analysis on tensions as the key to understanding organizational becoming in contrast to approaches that assume organizations are stable entities and consequently focus on patterns, themes, or laws. The applied tensional analysis framework offers four analytical foci (context, tensions, enacted responses, and repertoires) organized into two loops (analytical and change) as guides for data collection and analysis. While the analytical loop orients scholars to the current and past configurations of an organization’s emergence, the change loop emphasizes the multitude of available responses to a particular tension and the constitutive implications of those responses for organizational becoming. As a new methodological approach, applied tensional analysis suggests that organizational knowledge requires more than awareness of what an organization is and includes awareness of organizational potential and what an organization might become.

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