iterative-top-k

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Synthesis data: First, set the value for parameters: alpha - skewness of global distribution, n - number of distinct items, m - number of nodes, dis - how scores of items are partitioned among nodes. Then, run functions in utils/common.py to generate data. Last, call functions from utils/method.py to run top-k queries. An example is shown in main.py.
HIGGS data: download the dataset from https://archive.ics.uci.edu/dataset/280/higgs. Then, mploying functions in utils/common.py to increase the dimension of the dataset. Finally, directly call functions from utils/method.py to run top-k queries. An example is shown in mainhiggs.py.

Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
machine		machine
results		results
utils		utils
README.md		README.md
main.py		main.py
mainhiggs.py		mainhiggs.py