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.
Integration of Mobility Data with Weather Information. Conference
EDBT/ICDT Workshops 2019, 2019.
stLD: towards a spatio-temporal link discovery framework. Conference
SBD@SIGMOD 2019, 2019.
Big Data Analytics for Time Critical Mobility Forecasting: Recent Progress and Research Challenges Proceeding
FAIMUSS: Flexible Data Transformation to RDF from Multiple Streaming Sources Proceeding