Attack-tolerant Unequal Probability Sampling Methods over Sliding Window for Distributed Streams
Yann Busnel, Yves Tillé · 2020
Distributed systems increasingly require the processing of large amounts of data, for metrology, safety or security purposes. The online processing of these large data streams requires the development of algorithms to efficiently calculate parameters. If elegant solutions have been proposed recently, their approximation is commonly calculated from the inception of the data stream. In a distributed execution context, it would be preferable to collect information only on the recent past (for resource saving or relevancy of most recent information). We therefore consider here the sliding window model.