Fiscal Knowledge discovery in Municipalities of Athens and Thessaloniki via Linked Open Data

Koupidis, Kleanthis; Bratsas, Charalampos; Karampatakis, Sotiris; Martzopoulou, Anastasia; Antoniou, Ioannis

doi:10.1109/smap.2016.7753405

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…The transformation of the NSRF data to knowledge graph, is done by using the UnifiedViews 14 ETL tool. The main advantage of this tool is that it can extract data straight from relational databases and then transform it to RDF triples [27][28][29]. After the transformation process, the RDF files were uploaded to an OpenLink Virtuoso Server 15 .…”

Section: Nsrf Knowledge Graph and Data Retrievalmentioning

confidence: 99%

“…Supervised approaches are used when we have prior knowledge of what the output values for our samples should be. Therefore, unsupervised learning is appropriate to act on data without categorization [29,32]. Partitioning and hierarchical clustering algorithms are more effective on compact and well separated clusters, however in the presence of noise and outliers in the data, these methods are not very effective [33][34][35].…”

Section: Density Based Clusteringmentioning

confidence: 99%

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The Use of National Strategic Reference Framework Data in Knowledge Graphs and Data Mining to Identify Red Flags

Bratsas

Chondrokostas

Koupidis

et al. 2021

Data

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Red Flags in fiscal projects are warning signs that may indicate underlying problems with their implementation. In this paper, we present how National Strategic Reference Framework Open Data can be used to take full advantage of semantic web technologies and data mining techniques to build a knowledge-based system that identifies Red Flags. We collected the data from the Open Data API provided by the Greek Ministry of Economy and Finance. Data modeling consist of two ontologies; the Vocabulary of Fiscal Projects, describing the fiscal projects and the National Strategic Reference Framework Greece Vocabulary, illustrating the Greek National Strategic Reference Framework data. We transformed the data into RDF triples and uploaded them onto an OpenLink Virtuoso Server, so that we could retrieve them via SPARQL queries. Performance indicators were defined to assess the state of the project and Density-Based Spatial Clustering of Applications with Noise, (DBSCAN) was used to identify Red Flags. User’s demands is that rejected projects should raise Red Flags, to avoid project failure and assist the auditor to organize the monitoring process efficiently, by avoiding to examine most of the non-problematic projects. We performed a use case scenario in which an auditor has to examine NSRF projects, approximately 12 months before the end of the programming period. The system retrieved the fiscal information, calculated the performance indicators and identified the Red Flags. The last update of the projects status after the end of the programming period was retrieved and extracted the number of rejected projects, to test whether the user requirements are satisfied. Rejected projects consist of 3.8% of the total projects. The results of the use case scenario show that RedFlags platform is more likely to identify project failures and not raise Red Flags on not rejected projects. Therefore, the RedFlags platform using open data, assists the auditor to organize the monitoring process better.

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Section: Nsrf Knowledge Graph and Data Retrievalmentioning

confidence: 99%

Section: Density Based Clusteringmentioning

confidence: 99%

The Use of National Strategic Reference Framework Data in Knowledge Graphs and Data Mining to Identify Red Flags

Bratsas

Chondrokostas

Koupidis

et al. 2021

Data

Self Cite

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A Knowledge Discovery Method for Characteristics Extraction of Power Consumption Based on Cluster Analysis

Geng

Xia

2019

2019 Chinese Automation Congress (CAC)

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OpΕnergy: An Intelligent System for Monitoring EU Energy Strategy Using EU Open Data

2022

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In this paper, the basic structure of an ICT platform of energy indicators, Openergy, is analytically presented, leveraging energy open data to help address the energy crisis more democratically. More specifically, its applicability as a dynamic tool for the management of climate, environmental, and socioeconomic information is described, and its efficiency in helping uncover insights for optimal data-driven decisions is depicted. Openergy uses data from the official portal for European data and the Eurostat site. Its database consists of data related to six energy categories, EU 2020 energy targets, energy balance, electricity production, transport fuels, heat production, and gas emissions, and each one includes its own indicators for EU countries. The platform includes visualizations of these data as well as time series modeling and forecasting, and the results are depicted at Openergy platform. The time series modeling provides forecasts with confidence intervals of each indicator until 2020 in each energy category. Empirical validation, RMSE, and MAE values showed that in almost all cases and estimations, the predicted values are in very good agreement with the observed values.

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Fiscal Knowledge discovery in Municipalities of Athens and Thessaloniki via Linked Open Data

Cited by 3 publications

References 4 publications

The Use of National Strategic Reference Framework Data in Knowledge Graphs and Data Mining to Identify Red Flags

The Use of National Strategic Reference Framework Data in Knowledge Graphs and Data Mining to Identify Red Flags

A Knowledge Discovery Method for Characteristics Extraction of Power Consumption Based on Cluster Analysis

OpΕnergy: An Intelligent System for Monitoring EU Energy Strategy Using EU Open Data

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