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Cottontail DB is a column store for multimedia retrieval and multimedia analysis. It allows for boolean retrieval, traditionally supported by classical database systems, as well as vector-space retrieval in the form of nearest-neighbour search (NNS), as used in similarity search. Most importantly, however, Cottontail DB allows for a seamless combination of the two retrieval modes in a unified data and query model.

This Wiki is supposed to be a collection of all information surrounding Cottontail DB both for developers as well as end-users. Currently, not all relevant parts are covered. However, we're working on it, If some information is missing for you, please open an issue.

Credits

Cottontail DB is based on the ideas presented in the following papers:

• Ivan Giangreco and Heiko Schuldt (2016): ADAMpro: Database Support for Big Multimedia Retrieval. Datenbank-Spektrum. http://link.springer.com/article/10.1007/s13222-015-0209-y
• Ivan Giangreco (2018): Database support for large-scale multimedia retrieval. PhD Thesis. https://edoc.unibas.ch/64751/

Furthermore, the current release of Cottontail DB relies on the following libraries (big shoutout here):

• MapDB for internal data organization and storage.
• Lucene for text retrieval and text matching.
• gRPC for communication with the outside world.
• FastUtil for fast in-memory data structures.
• Clikt for the CLI functionality.
• Picnic for table rendering in the CLI.

Citation

We kindly ask you to refer to the following paper in publications mentioning or employing Cottontail DB:

Ralph Gasser, Luca Rossetto, Silvan Heller, Heiko Schuldt. Cottontail DB: An Open Source Database System for Multimedia Retrieval and Analysis. In Proceedings of 28th ACM International Conference on Multimedia (ACM MM 2020), Seattle, USA, 2020

Link: https://doi.org/10.1145/3394171.3414538

Bibtex:

@inproceedings{10.1145/3394171.3414538,
    author = {Gasser, Ralph and Rossetto, Luca and Heller, Silvan and Schuldt, Heiko},
    title = {Cottontail DB: An Open Source Database System for Multimedia Retrieval and Analysis},
    year = {2020},
    isbn = {9781450379885},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    doi = {10.1145/3394171.3414538},
    booktitle = {Proceedings of the 28th ACM International Conference on Multimedia},
    pages = {4465–4468},
    numpages = {4},
    keywords = {open source, multimedia retrieval, database, multimedia indexing, data management system},
    location = {Seattle, WA, USA},
    series = {MM '20}
}