Conference Paper

SemAI: A Novel Approach for Achieving Enhanced Semantic Interoperability in Public Policies

One of the key elements in several application domains, such as policy making, addresses the scope of achieving and dealing with the very different formats, models and languages of data. The amount of data to be processed and analyzed in modern governments, organizations and businesses are staggering, thus Big Data analysis is the mean that helps organizations to harness their data and to identify new opportunities

Wednesday, 28 July, 2021 - 11:49
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Journal Paper

Parallel query processing in a polystore

In this paper, we address these points by: (i) using the polyglot approach of the CloudMdsQL query language that allows native queries to be expressed as inline scripts and combined with SQL statements for ad-hoc integration and (ii) incorporating the approach within the LeanXcale distributed query engine, thus allowing for native scripts to be processed in parallel at data store shards. In addition, (iii) efficient optimization techniques, such as bind join, can take place to improve the performance of selective joins. We evaluate the performance benefits of exploiting parallelism in combination with high expressivity and optimization through our experimental validation.

Wednesday, 3 February, 2021 - 07:36
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Conference Paper

An Evaluation of Neural Machine Translation and Pre-trained Word Embeddings in Multilingual Neural Sentiment Analysis

This paper investigates the impact of Neural Machine Translation in sentiment analysis, based on the translation of text for which neural sentiment analysis in English has been already applied and is applied again on the translated German and Greek texts. The latter is performed through the same Neural Network models that were used in the original language, in order to analyse the differences between the performed neural sentiment analysis on the source and the target languages.

Sunday, 20 December, 2020 - 07:23
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AIAI2020 Policy Cloud PowerPoint

Conference Paper

PolicyCLOUD: Analytics as a Service Facilitating Efficient Data-Driven Public Policy Management

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.

Sunday, 5 July, 2020 - 10:37
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