2022
DOI: 10.1017/dap.2022.32
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PolicyCLOUD: A prototype of a cloud serverless ecosystem for policy analytics

Abstract: We present PolicyCLOUD: a prototype for an extensible serverless cloud-based system that supports evidence-based elaboration and analysis of policies. PolicyCLOUD allows flexible exploitation and management of policy-relevant dataflows, by enabling the practitioner to register datasets and specify a sequence of transformations and/or information extraction through registered ingest functions. Once a possibly transformed dataset has been ingested, additional insights can be retrieved by further applying registe… Show more

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Cited by 13 publications
(9 citation statements)
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References 41 publications
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“…It is the most sophisticated kind of analytics, which goes beyond predicting what will happen in the future since it might help discover the most efficient risk-reduction tactics. [ 2 , 20 ] …”
Section: Resultsmentioning
confidence: 99%
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“…It is the most sophisticated kind of analytics, which goes beyond predicting what will happen in the future since it might help discover the most efficient risk-reduction tactics. [ 2 , 20 ] …”
Section: Resultsmentioning
confidence: 99%
“…Public health organizations may use large databases to spot epidemiological patterns, predict disease outbreaks, and allocate resources as efficiently as possible, improving public health responsiveness and readiness. For example, Biran et al [ 20 ] proposed PolicyCLOUD platform which facilitates the flexible exploitation and management of policy-relevant dataflows by allowing practitioners to register datasets and define a series of transformations and/or information extraction using registered ingest functions. Using a newly proposed type of electronic health records and their corresponding networks, Mavrogiorgou et al [ 21 ] proposed beHEALTHIER, which provides the capacity to generate health policies out of data of collective understanding through the effective management of healthcare data.…”
Section: Introductionmentioning
confidence: 99%
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“…To give an idea of the order of magnitude of the phenomenon, it it possible to observe that at the time of writing, more than 680,000 documents that include the words "machine" and "learning" or "deep" and "learning" have been indexed by SCOPUS after 2017, and more than 22,000 of them are represented by review and survey articles, with more than 2000 citations for the highest-cited ones (https://www.shorturl.at/lDPRT (accessed on 1 August 2023)). Among such reviews, it is possible to find applications of ML and DL methodologies to a set of very heterogeneous domains including, to cite some, environment and energy applications [3][4][5], materials science [6,7], agriculture [8,9], natural language processing and computer vision [10,11], industrial applications and IoT [12][13][14][15], and public health policies [16,17].…”
Section: Methodsmentioning
confidence: 99%
“…Through the method proposed by Biran et al (2022), inconsistencies and errors in the data are identified, such as incorrect timestamps or misspelled locations. Rule-based automated data cleaning processes, including standardizing naming conventions, formats, and data types, are applied [ 36 ]. Combining this with the research of Mavrogiorgos et al (2022), natural language processing tools are utilized for the analysis and preprocessing of textual data, such as extracting keywords and phrases, eliminating ambiguity, and removing duplicates.…”
Section: Optimization Of the Community Safety Risk Assessment Algorit...mentioning
confidence: 99%