Project: Discovery and maintenance of heavy hitters over sliding windows, in a distributed environment.
Description
Synopses are extensively used for summarizing high-frequency streaming data, e.g., input from sensors, network packets, financial transactions. Some examples include Count-Min sketches, Bloom filters, AMS sketches, samples, and histogram. This project will focus on designing, developing, and evaluating synopses for the discovery of heavy hitters over sliding windows, in a distributed environment, and over high-frequency streams.
This project may include collaboration with international partners.
Prerequisites: ability to write efficient code in *Java or Scala*, comfortable with mathematical proofs, ability to read and understand scientific literature (conference papers and journal articles), successful completion of 2AMD15 with a high grade.
Reading material:
Introduction to synopses: https://dl.acm.org/doi/10.1561/1900000004
Space saving algorithm and extensions: https://vldb.org/pvldb/vol15/p1215-zhao.pdf
Memento algorithm https://arxiv.org/pdf/1810.02899.pdf
ECM sketches https://www.win.tue.nl/~opapapetrou/papers/vldbj15.pdf
Details
- Supervisor
-
Odysseas Papapetrou
- Interested?
- Get in contact