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 registered analytic functions to it. PolicyCLOUD was built as an extensible framework toward the creation of an analytic ecosystem. As of now, we have developed several essential ingest and analytic functions that are built-in within the framework. They include data cleaning, enhanced interoperability, and sentiment analysis generic functions; in addition, a trend analysis function is being created as a new built-in function. PolicyCLOUD has also the ability to tap on the analytic capabilities of external tools; we demonstrate this with a social dynamics tool implemented in conjunction with PolicyCLOUD, and describe how this stand-alone tool can be integrated with the PolicyCLOUD platform to enrich it with policy modeling, design and simulation capabilities. Furthermore, PolicyCLOUD is supported by a tailor-made legal and ethical framework derived from privacy/data protection best practices and existing standards at the EU level, which regulates the usage and dissemination of datasets and analytic functions throughout its policy-relevant dataflows. The article describes and evaluates the application of PolicyCLOUD to four families of pilots that cover a wide range of policy scenarios.

Big data-driven public health policy making: Potential for the healthcare industry

“…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 ] …”

“…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.…”

“…These instances make it possible to analyze the use of big data, draw general conclusions and pinpoint prospective public health sector use of BDA-driven policy making [ 27 ]. Recently, many studies have been conducted in clinical settings and public health sectors by applying the concept of “big data” and “data analytics” [ 19 , 20 ]. Furthermore, government organizations use BDA more often to solve issues like pandemics and sustainability [ 30 ].…”

Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community

“…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].…”

Algorithm for community security risk assessment and influencing factors analysis by back propagation neural network

“…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.…”