The project provides a Scala implementation of the Lossy Counting and Sticky Sampling algorithms for efficient counting on data streams. You can find a description of the algorithms at this post.
We want to know which items exceed a certain frequency and identify events and patterns. Answers to such questions in real-time over a continuous data stream is not an easy task when serving millions of hits due to the following challenges:
- Single Pass
- Limited memory
- Volume of data in real-time
The above impose a smart counting algorithm. Data stream mining to identify events & patterns can be performed by applying the following algorithms: Lossy Counting and Sticky Sampling.
How to run
Using sbt to build and run:
sbt "run-main frequencycount.lossycounting.LossyCountingModel"
sbt "run-main frequencycount.stickysampling.StickySamplingModel"
Run the tests using
Have you found any issues? Want to contribute?
Help me finish the distributed implementation on Spark (see branch).
Please contact me at firstname.lastname@example.org or create a new Issue. Pull requests are always welcome.