Grouperfish is built to perform text clustering for Firefox Input. Due to its generic nature, it also serves as a testbed to prototype machine learning algorithms.
How does it work?¶
Grouperfish is a document transformation system, for high throughput applications.
- users put documents into Grouperfish using a REST interface
- transformations are performed on one or several subsets of these documents.
- results can be retrieved by users over the REST interface
- all components are distributed for high volume applications
What can be done?¶
Assume a scenario where a steady stream of documents is generated. For example:
- user feedback
- software crash reports
- twitter messages
Now, these documents can be processed to make them more useful. For example:
- clustering (grouping related documents together, detecting common topics)
- classification (associating documents with predefined categories including spam)
- trending (identifying new topics over time).
Grouperfish users can assume one of three roles (or any combination thereof):
- Document Producer
- Some user (usually another piece of software) that will put documents into the System.
- Result Consumer
- Some user/software that gets the generated results.
- A user who configures which subsets of documents to transform, but also how and when to do that.