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Streamed Data Analysis Using Adaptable Bloom Filter
2018
Computing and informatics
With the coming up of plethora of web applications and technologies like sensors, IoT, cloud computing, etc., the data generation resources have increased exponentially. Stream processing requires real time analytics of data in motion and that too in a single pass. This paper proposes a framework for hourly analysis of streamed data using Bloom filter, a probabilistic data structure where hashing is done by using a combination of double hashing and partition hashing; leading to less inter-hash
doi:10.4149/cai_2018_3_693
fatcat:dpnom4ugurd5nhvm4vgmmz7qhm