http://www3.cs.stonybrook.edu/~emanzoor/streamspot/
- Anaconda for the entire
scikit-learnstack. - GCC 5.2+ to compile the C++11 code.
The following steps assume this repository has been cloned and all dependencies installed.
Convert the training data from CDM13/Avro to StreamSpot
For detailed instructions, see the sbustreamspot-cdm README.
For the purpose of instruction, infoleak_small_units.CDM13.avro is assumed to be the training data.
- Get the StreamSpot CDM translation code:
git clone https://github.com/sbustreamspot/sbustreamspot-cdm.git - Install its dependencies:
pip install -r requirements.txt - Convert CDM13/Avro training data to StreamSpot edges:
python translate_cdm_to_streamspot.py --url avro/infoleak_small_units.CDM13.avro --format avro --source file --concise > streamspot/infoleak_small_units.CDM13.ss
Convert the StreamSpot training graphs to shingle vectors
The graph-to-shingle-vector transformation code is in C++ to ensure high performance. It is a modified version of the streamspot-core code.
Build and run the code as follows;
cd graphs-to-shingle-vectors
make optimized
./streamspot --edges=../streamspot/infoleak_small_units.CDM13.ss --chunk-length 24 > ../shingles/infoleak_small_units.CDM13.sv
cd ..
Cluster the training graph shingle vectors
Ensure the dependencies have been installed: pip install -r requirements.txt
python create_seed_clusters.py --input shingles/infoleak_small_units.CDM13.sv > clusters/infoleak_small_units.CDM13.cl
The *.cl file can then be provided to streamspot-core.