Abstract: While several application domains are exploiting the added-value of analytics over various datasets to obtain actionable insights and drive decision making, the public policy management domain has not yet taken advantage of the full potential of the aforementioned analytics and data models. Diverse and heterogeneous datasets are being generated from various sources, which could be utilized across the complete policies lifecycle (i.e. modelling, creation, evaluation and optimization) to realize efficient policy management. To this end, in this paper we present an overall architecture of a cloud-based environment that facilitates data retrieval and analytics, as well as policy modelling, creation and optimization. The environment enables data collection from heterogeneous sources, linking and aggregation, complemented with data cleaning and interoperability techniques in order to make the data ready for use. An innovative approach for analytics as a service is introduced and linked with a policy development toolkit, which is an integrated web-based environment to fulfil the requirements of the public policy ecosystem stakeholders.

Authors: Dimosthenis Kyriazis (UPRC), Ofer Biran IBM Research), Thanassis Bouras, Klaus Brisch (DWF), Armend Duzha (Maggioli), Rafael del Hoyo (ITA Innova), Athanasios Kiourtis, Pavlos Kranas (LeanXcale), Ilias Maglogiannis (UPRC), George Manias, Marc Meerkamp (DWF), Konstantinos Moutselos, Argyro Mavrogiorgou, Panayiotis Michael (ICCS), Ricard Munné (Atos), Giuseppe La Rocca (EGI), Kostas Nasias, Tomas Pariente Lobo (Atos), Vega Rodrigálvarez (ITA Innova), Nikitas M. Sgouros, Konstantinos Theodosiou, Panayiotis Tsanakas

DOI: https://doi.org/10.1007/978-3-030-49161-1_13

Cite this paper as: Kyriazis D. et al. (2020) PolicyCLOUD: Analytics as a Service Facilitating Efficient Data-Driven Public Policy Management. In: Maglogiannis I., Iliadis L., Pimenidis E. (eds) Artificial Intelligence Applications and Innovations. AIAI 2020. IFIP Advances in Information and Communication Technology, vol 583. Springer, Cham

Download paperhttps://link.springer.com/chapter/10.1007%2F978-3-030-49161-1_13

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