Vehicular traffic flow intensity detection and prediction through mobile data usage
In this research we propose a novel approach, consisting of an ensemble of data- mining and machine learning techniques, to prove that it is possible to extract and predict vehicular traffic patterns from mobile usage data. An anonymized mobile phone usage dataset from a telecommunications provider in Malta was used to generate an origin-destination matrix that defines the top two activity hubs through clustering. The matrix was used to infer user trips over fastest routes between these top two locations across time. Spatial binning techniques were used to deduce the aggregated distribution of traffic load on the traffic network and a predictive model based on an artificial neural network (ANN) was trained with the whole network traffic flow load in a time series to predict traffic level for specific nodes.
Ontology Quality Visualisation (OntoQAV)
OntoQAV is a pipeline that integrates Luzzu , a generic Linked Data quality assessment framework, and WebVOWL , a widely used tool to represent RDF-based ontologies in a visual format. The rational behind this pipeline is to create an ecosystem with the aim of allowing stakeholders to assess ontologies using various quality metrics and to visualise any identified problems. The user has the possibility of assessing multiple ontologies at once and presenting a comparative visualisation and summary of the quality problems within the ontologies being assessed.
OntoQAV is downloadable from: https://github.com/silviomcgurk/OntoQAV
OntoQAV demo: http://ontoqav.publicvm.com/evaluation/preconf/
 Debattista, J., Auer, S., Lange, C.: Luzzu – A Methodology and Framework for Linked Data Quality Assessment. Journal of Data and Information Quality. (2016) https://github.com/eis-bonn/Luzzu
Lohmann, S., Negru, S., Haag, F., Ertl, T.: Visualizing ontologies with VOWL. Semantic Web. 7, 399-419 (2016) http://vowl.visualdataweb.org/webvowl.html
AcademiC analysEr (ACE)
The characteristics of scientific collaboration networks have been extensively analysed and found to be similar to other scale-free networks. Research has furthermore focused on investigating how collaboration patterns between authors evolved over time, by providing in- sights into different fields of research. Numerous bibliographic datasets, such as DBLP and Microsoft Academic Graph, provide the basis for investigations and analysis of such networks. ACE (Academic Collaboration analyzEr) is an interactive framework that uses big data technologies and allows for scientific collaboration patterns to be analysed and visualised. Through ACE it is possible to reveal the key authors in particular fields of research, the topological features of the collaboration network, the network trends over time and the relationships between authors and co-authors. Furthermore, ACE allows for the discovery of potentially new collaborations between authors in the same field of research as well as fields where scientists can conduct future joint-research work.
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