Recognition of the Relevance of International Scientific Data: Poços de Caldas Geopark Project
Monteiro Vinícius Arcanjo
Issue:
Volume 7, Issue 2, June 2021
Pages:
23-31
Received:
7 January 2021
Accepted:
20 January 2021
Published:
23 June 2021
Abstract: The scientific knowledge of geological sites is a model to recognize the scores of stablish geoconservation and education/touristic proposes sites in geoparks territories. The alkaline massif of Poços de Caldas is a structure of igneous alkaline origin (with volcanic and hypabissal deposits), formed, roughly, by breccias and tufts, effusive and hypabissal rocks and plutonic rocks. The purpose of this article is to recognize the international scientific relevance of geological sites in the Alkaline Massif of Poços de Caldas (Minas Gerais State) as a requirement to be a UNESCO Global Geopark. Poços de Caldas is located in the Southern region of Brazil and were know in the literature as the second biggest alkaline massif in the globe. We did a scientific database review in Scopus and manual review to recognize a science base for the issue of International publications of Poços de Caldas. The publication of bibliographic stuffs mentioned in the text formed a database for knowledge of international sites and its relevance. The themes of volcanism, hydrogeology, geomorphology, rare minerals and samples are contextualizing the geossites arranged in the article. The significance of largest igneous alkaline massif in the globe, rare minerals founded in Poços de Caldas, the uranium thematic in Brazil, the International Earth Science Olympiad and the hydrogeology are the main international relevant geossits in the region studied.
Abstract: The scientific knowledge of geological sites is a model to recognize the scores of stablish geoconservation and education/touristic proposes sites in geoparks territories. The alkaline massif of Poços de Caldas is a structure of igneous alkaline origin (with volcanic and hypabissal deposits), formed, roughly, by breccias and tufts, effusive and hyp...
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Kognitor: Big Data Real-Time Reasoning and Probabilistic Programming
Arinze Anikwue,
Boniface Kabaso
Issue:
Volume 7, Issue 2, June 2021
Pages:
32-39
Received:
10 June 2021
Accepted:
9 July 2021
Published:
2 August 2021
Abstract: There is a huge increase in the amount of generated data since the explosion of the Internet. This generated data which is usually collected in different formats and from multiple sources is popularly termed Big Data. Big data contains uncertainty. To handle uncertainty in big data, probabilistic reasoning is used to develop probabilistic models that specify generic knowledge in different topics. These models are used in conjunction with an inference algorithm to enable decision makers especially during uncertain situations. Extensive knowledge in fields such as statistics, machine learning and probability theories are employed in the development of these probabilistic models. Thus, it is usually a difficult undertaking. Probabilistic programming was introduced to simplify and enable development of complex models. Again, decision makers often need to use knowledge from historic data as well as current data to make cogent decisions. Thus, the necessity to unify processing of historic and real-time data with low latency. The Lambda architecture was introduced for this purpose. This paper presents a framework called Kognitor that simplifies the design and development of difficult models using probabilistic programming and Lambda architecture. Evaluation of this framework is also presented in this paper using a case study to highlight the crucial potential of probabilistic programming to achieve simplification of model development and enable real-time reasoning on big data. Thus, demonstrating the effectiveness of the framework. Finally, results of this evaluation are presented in this paper. The Kognitor framework can be used to steer effective and easier implementation of complicated real-life situations as probabilistic models. This will be beneficial in the big data processing domain and for decision makers. Kognitor ensures cost-effectiveness using contemporary big data tools and technology on commodity hardware. Kognitor framework will also be beneficial in academia with respect to the use of probabilistic programming.
Abstract: There is a huge increase in the amount of generated data since the explosion of the Internet. This generated data which is usually collected in different formats and from multiple sources is popularly termed Big Data. Big data contains uncertainty. To handle uncertainty in big data, probabilistic reasoning is used to develop probabilistic models th...
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