Run time scanner data analysis for HVM lithography process monitoring and stability control

2014 ◽  
Author(s):  
Woong Jae Chung ◽  
Young Ki Kim ◽  
John Tristan ◽  
Jeong Soo Kim ◽  
Lokesh Subramany ◽  
...  
Keyword(s):  
Data Analysis ◽  
Process Monitoring ◽  
Stability Control ◽  
Scanner Data ◽  
Lithography Process ◽  
Run Time

When Are Stockpiled Products Consumed Faster? A Convenience–Salience Framework of Postpurchase Consumption Incidence and Quantity

2002 ◽  
Vol 39 (3) ◽  
pp. 321-335 ◽  
Author(s):  
Pierre Chandon ◽  
Brian Wansink
Keyword(s):  
Data Analysis ◽  
Field Study ◽  
Research Methods ◽  
Scanner Data ◽  
Laboratory Studies ◽  
Consumption Decisions

When people stockpile products, how do they decide when and how much they will consume? To answer this question, the authors develop a framework that shows how the salience and convenience of products influence postpurchase consumption incidence and quantity. Multiple research methods—including scanner data analysis, a field study, and two laboratory studies—show that stockpiling increases product salience and triggers consumption incidence among high-convenience products. However, when the decision is made to consume a product, stockpiling increases the consumption quantity for both high- and low-convenience products. In addition to providing new insights on how consumers make postpurchase consumption decisions, these results have implications for the debate on the value of promotions that induce stockpiling.

scholarly journals MiCADO-Edge: Towards an Application-level Orchestrator for the Cloud-to-Edge Computing Continuum

2021 ◽  
Vol 19 (4) ◽  
Author(s):  
Amjad Ullah ◽  
Huseyin Dagdeviren ◽  
Resmi C. Ariyattu ◽  
James DesLauriers ◽  
Tamas Kiss ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Analysis ◽  
Video Processing ◽  
Time Management ◽  
Realistic Case ◽  
Healthcare Data ◽  
Computing Environments ◽  
Management Policies ◽  
Run Time ◽  
Monitoring Information

AbstractAutomated deployment and run-time management of microservices-based applications in cloud computing environments is relatively well studied with several mature solutions. However, managing such applications and tasks in the cloud-to-edge continuum is far from trivial, with no robust, production-level solutions currently available. This paper presents our first attempt to extend an application-level cloud orchestration framework called MiCADO to utilise edge and fog worker nodes. The paper illustrates how MiCADO-Edge can automatically deploy complex sets of interconnected microservices in such multi-layered cloud-to-edge environments. Additionally, it shows how monitoring information can be collected from such services and how complex, user- defined run-time management policies can be enforced on application components running at any layer of the architecture. The implemented solution is demonstrated and evaluated using two realistic case studies from the areas of video processing and secure healthcare data analysis.

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