Rich and diverse information sources of static or stream structured data do exist. However these are heterogeneous in various aspects: On the ways information is formed, on the ways information is described and shaped, on the ways different aspects of (abstract or concrete) entities are conceptualized and represented.
Computing accurate semantic descriptions of heterogeneous and disparate data sources, and managing their heterogeneity via scalable semantic alignment solutions is challenge. We need algorithms that – among others- are able to cope with big data sources, take advantage of background knowledge and consult humans to compute semantic agreements.
In addition to that, considering the existence of many diverse information sources in an open environment; each source having its own way of structuring, describing data and representing the world, we can easily see the necessity of distributed algorithms for these sources to reach semantic agreements towards shaping information jointly.
Big Data Analytics for Time Critical Mobility Forecasting: From raw data to trajectory-oriented mobility analytics in the aviation and maritime domains Book Forthcoming
SPARTAN: Semantic Integration of Big Spatio-temporal Data from Streaming and Archival Sources Journal Article Forthcoming
Future Generation Computer Systems, to appear , Forthcoming.
Fundamenta Informaticae, 172 , pp. 299-325, 2020, ISSN: 0169-2968 (P).
The Knowledge Engineering Review, 35 , 2020.
ARGO: A Big Data Framework for Online Trajectory Prediction Conference
SSTD 2019, 2019.