TY - GEN
T1 - Graph lenses over any data
T2 - 40th IEEE International Conference on Data Engineering Workshops, ICDEW 2024
AU - Balalau, Oana
AU - Barret, Nelly
AU - Ebel, Simon
AU - Galizzi, Theo
AU - Manolescu, Ioana
AU - Mohanty, Madhulika
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Data integration is decades-old problem that takes many shapes, depending on the model of the integrated data sources, the integration model (if a single model is used), the expressivity of the features supported from each model, etc.Over several years, we have worked to integrate very heterogeneous data, aiming to address the needs Non-Technical Users (NTUs), notably journalists. The choice we make is to integrate data of any model by migrating (transforming) it into a graph, consisting simply of labeled nodes and edges. Such graphs are much simpler than Property Graphs and more basic even than RDF graphs, since we do not require URI labels on internal nodes. This experience paper gives an overview of our research efforts towards querying, understanding, and exploring the resulting graphs. The contributions we brought are in the areas of data integration, graph exploration, graph querying, and go towards managing semistructured data lakes.
AB - Data integration is decades-old problem that takes many shapes, depending on the model of the integrated data sources, the integration model (if a single model is used), the expressivity of the features supported from each model, etc.Over several years, we have worked to integrate very heterogeneous data, aiming to address the needs Non-Technical Users (NTUs), notably journalists. The choice we make is to integrate data of any model by migrating (transforming) it into a graph, consisting simply of labeled nodes and edges. Such graphs are much simpler than Property Graphs and more basic even than RDF graphs, since we do not require URI labels on internal nodes. This experience paper gives an overview of our research efforts towards querying, understanding, and exploring the resulting graphs. The contributions we brought are in the areas of data integration, graph exploration, graph querying, and go towards managing semistructured data lakes.
KW - data exploration
KW - data integration
KW - graph databases
KW - graph querying
KW - semistructured data
U2 - 10.1109/ICDEW61823.2024.00056
DO - 10.1109/ICDEW61823.2024.00056
M3 - Conference contribution
AN - SCOPUS:85197358522
T3 - Proceedings - 2024 IEEE 40th International Conference on Data Engineering Workshops, ICDEW 2024
SP - 370
EP - 374
BT - Proceedings - 2024 IEEE 40th International Conference on Data Engineering Workshops, ICDEW 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 May 2024 through 16 May 2024
ER -