Introducér rho i introduktionen

[Snailed]

bachelor-project/introduction/main.tex

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Many improvements have since been presented to both improve processing time, query time and space efficiency. Notable mentions includes (but are not limited to) the use of \textit{tensoring}\cite{andoni2006efficient}, \textit{b-bit minwise hashing}\cite{ping2011theory}, \textit{fast similarity sketching}\cite{dahlgaard2017fast}. Simple applications of these techniques leads to efficient querying with a constant error probability. If one wishes to achieve an even better error probability such as $\varepsilon = o(1)$, it is standard practice within the field to use $O(\log_2(1/\varepsilon))$ independent data structures and return the best result, resulting in a query time of $O(\frac{1}{\epsilon} (n^\rho + |Q|))$. Recent advances by \citet{fast-similarity-search} show that it is possible to achieve an even better query time by sampling these data structures from one large sketch. The similarity between these sub-sketches and a query set needs to be evaluated efficiently when querying, which requires efficient computation of the cardinality of a bit-string. To do this, \citet{fast-similarity-search} presents a general parallel bit-counting algorithm that computes the cardinality of a list of bit-strings in sub-linear time amortized due to word-parallism. This brings the query time down to $O((\frac{n\log_2 w}{w})^\rho \log(1/\varepsilon) + |Q|)$.\\