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Project Execution
Published in Terra Vanzant Stern, Lean and Agile Project Management, 2020
A POC is designed to demonstrate the feasibility of a proposed idea or concept to solve a business need. POC may also be used during the project selection process. The disadvantage to using POC is that generally it is so specific to the particular product or service that in order to conduct a valid study, subject matter experts in that topic need to be available. Also, there is a great deal of variation when conducting a POC. Because the purpose of POC is to prove that a project or idea will work, several one-off strategies specific to that particular product or service may be employed.
Project Execution
Published in Terra Vanzant Stern, Lean and Agile Project Management, 2017
A POC is designed to demonstrate the feasibility of a proposed idea or concept to solve a business need. POC may also be used during the project selection process. The disadvantage to using POC is that generally it is so specific to the particular product or service that in order to conduct a valid study subject matter experts in that topic need to be available. Also there is a great deal of variation when conducting a POC. Because the purpose of POC is to prove that a project or idea will work, several one-off strategies specific to that particular product or service may be employed.
Proof of Concept
Published in Bahram Nassersharif, Engineering Capstone Design, 2022
Designers often use POCs and prototypes interchangeably. A POC shows whether the product or process can be built or not, while a prototype physically presents its essential functions. When design teams prove that the design solution is valid and sponsors agree and approve the proposal, they can build the product or create the process. The POC aims to validate the idea or assumption, and prototyping lets the design team realize the concept by creating an interactive working model of their proposed design solution.
Analysing dynamic work systems using DynEAST: a demonstration of concept
Published in Ergonomics, 2023
Matt Holman, Guy Walker, Terry Lansdown
DynEAST is an augmentation of ‘conventional’ EAST that combines features of temporal and multilayer networks to form dynamic models of work systems. The DynEAST approach involves constructing a series of multilayer EAST network ‘slices’ representing the agents and information involved in each subtask. The temporal ‘life’ of subtask network slices are delimited by temporal data relating to the time of subtask occurrence. These slices are then arranged in sequence along a timeline bound by the beginning and end of the observed system (see Figure 3). The result is a ‘superordinate dynamic multilayer EAST network’. This superordinate network is different to the static composite networks currently produced using EAST (Salmon et al. 2018). Rather than producing a static representation of the total work system, the dynamic superordinate network model reconfigures its constituent nodes and links according to the agents and information involved throughout each sub-task. Dynamic network metrics can be extracted from this superordinate network, and its constituent network slices, to generate traceable insights into the dynamism of the system under analysis. These concepts apply in practice. A typical use case would be to employ DynEAST (as per conventional EAST) to the analysis of a ‘real-world’ work system. Such a use-case forms the topic of a proof-of-concept demonstration described in the next section.
The impact factors on the competence of big data processing
Published in International Journal of Computers and Applications, 2022
Wei Li, William W. Guo, Michael Li
To make such an investigation, we do not inject any optimization into the models. Our purpose is to expose any potential problems that could impair MapReduce performance. If such impaired competence can be demonstrated as acceptable/promising, optimization can be added to alleviate the performance penalty from the dynamic, heterogeneous, and unreliable features of VC. The methodology of this paper consists of the following steps: Propose and justify impact factors, covering heterogeneity, communication cost, and churn to reflect the opportunistic environments of VC.Model impact by composing an ideal computing environment and injecting the impact factors into it. The compute-capacities of the impaired environments are also classified on the way of modeling.Model a DHT platform that is able to inject any single or combination of the impact factors into the running MapReduce.Implement a proof-of-concept prototype of the proposed models and algorithms.Evaluate the impaired performance of MapReduce and analyze the causes of each impact.Predict optimization potentials on the basis of result analysis.
Flood damage cost estimation in 3D based on an indicator modelling framework
Published in Geomatics, Natural Hazards and Risk, 2020
Mostafa Elfouly, Anna Labetski
A considerable number of related approaches for modelling indicators can be found in various publications. Barone et al. (2011) focus on composite indicators consisting of a hierarchy of indicators. They seek to link indicators to business objectives in order to achieve business intelligence. One such example is Balanced Scorecard, developed by Kaplan and Norton (1992), which is a very common technique for choosing indicators, mainly in management. During the course of its development, they tackled questions, such as how to transfer values across different hierarchies of indicators, and how to derive values for composite indicators. In order to determine the indicators, they relied on two relations: evaluates and measures. In addition, an Eclipse-based prototype was implemented as a proof of concept for their work. Although they follow clear and accurate semantics and syntax, they do not offer a UML representation of their model, which would allow for extendibility and integration within other frameworks. Moreover, although Balanced Scorecard does allow for financial and operational measures, it was not investigated whether it can be linked to the geospatial domain.