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• W2563280597 abstract "Cardinality estimation is one of the fundamental problems in large-scale radio frequency identification systems. While many efforts have been made to achieve faster approximate counting, the accuracy of estimates itself has not received enough attention. Specifically, most state-of-the-art schemes share a two-phase paradigm implicitly or explicitly, which needs a rough estimate first and then refines it to a final estimate meeting the desired accuracy; we observe that the final estimate can largely deviate from the expectation due to the skewed rough estimate, i.e., the accuracy of final estimates is not rigorously bounded. This negative impact is hidden because former solutions either assume perfect rough estimates or rough estimates that can be produced by uniform random data or perfect hash functions that can turn any data into uniform random data. Unfortunately, both of them are hard to meet in practice. To address the above issues, we propose a novel scheme, namely, “rigorous and practical cardinality (RPC)” estimation. RPC adopts the two-phase paradigm, in which the rough estimate is derived in the first phase using pairwise-independent hashing. In the second phase, we employ t-wise-independent hashing to reinforce the rough estimate to meet arbitrary accuracy requirements. We validate the effectiveness and performance of RPC through theoretical analysis and extensive simulations. The results show that the RPC can meet the desired accuracy all the time with diverse practical settings while previous designs fail with non-uniform data." @default.
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• W2563280597 date "2017-06-01" @default.
• W2563280597 modified "2023-10-14" @default.
• W2563280597 title "Toward More Rigorous and Practical Cardinality Estimation for Large-Scale RFID Systems" @default.
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• W2563280597 doi "https://doi.org/10.1109/tnet.2016.2634551" @default.
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