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VLog is a Datalog engine which combines a column-based memory layout with novel optimization methods that avoid redundant inferences at runtime, resulting in high efficiency in terms of memory usage and speed. Compared to traditional row stores, column-based approaches have shown superior performance on analytical workloads, but are deployed mostly in relational DBMSs, see for instance MonetDB. For VLog, the vertical storage leads to high memory efficiency and competitive runtimes, but also requires specific implementation strategies and data structures.
Its main features are:
- The engine has a small memory footprint, which allows the materialisation of large databases using commodity hardware
- the engine can interface with different data sources, like relational databases, CSV, etc.
- It contains an experimental implementation of a parallel evaluation of the rules.
- It supports the execution of existential rules (click here for more info)
We provide two small tutorials to illustrate how the system can be used. Click here for more info.
We also provide a detailed documentation of how various databases can be configured.
Below is a list of publications concerning VLog. If you need to cite our work, please cite the AAAI publication.
Varsha Ravichandra Mouli, Unmesh Joshi, Ceriel Jacobs, and Jacopo Urbani. Predicting the cost of online reasoning on knowledge graphs: Some heuristics. In 16th International Semantic Web Conference 2017 - Posters and Demos (ISWC2017P&D), Vienna, Austria, 2017.
Jacopo Urbani, Ceriel J.H. Jacobs, and Markus Krötzsch. Column-oriented Datalog materialization for large knowledge graphs. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA., pages 258--264, 2016, http.
Jacopo Urbani, Ceriel J.H. Jacobs, and Markus Krötzsch. VLog: A column-oriented Datalog reasoner. In The 39th German Conference on Artificial Intelligence (KI2016), Klagenfurt, Austria, 2016.
Jacopo Urbani, Ceriel J.H. Jacobs, and Markus Krötzsch. VLog: A column-oriented datalog system for large knowledge graphs. In 5th International Semantic Web Conference 2016 - Posters and Demos (ISWC2016P&D), Kobe, Japan, 2016.