You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review for Weighted MinHash Algorithms 
Chapter Title:  Practical Consistent Weighted Sampling 
1. Abstract of Cited Paper (BIB001):  Min-Hash, which is widely used for efficiently estimating similarities of bag-of-words represented data, plays an increasingly important role in the era of big data. It has been extended to deal with real-value weighted sets -- Improved Consistent Weighted Sampling (ICWS) is considered as the state-of-the-art for this problem. In this paper, we propose a Practical CWS (PCWS) algorithm. We first transform the original form of ICWS into an equivalent expression, based on which we find some interesting properties that inspire us to make the ICWS algorithm simpler and more efficient in both space and time complexities. PCWS is not only mathematically equivalent to ICWS and preserves the same theoretical properties, but also saves 20% memory footprint and substantial computational cost compared to ICWS. The experimental results on a number of real-world text data sets demonstrate that PCWS obtains the same (even better) classification and retrieval performance as ICWS with 1/5~1/3 reduced empirical runtime.

Literature Review:
ICWS employs three random variables (i.e., r k , β k , c k ) for each element. Due to the fact that random variables are derived from the uniform random ones in programming languages, it essentially generates five uniform random variables for each element, i.e., , where u k1 , u k2 , v k1 , v k2 ∼ Uniform(0, 1). Inspired by this, Wu et. al. proposed a more efficient algorithm, the Practical Consistent Weighted Sampling (PCWS) algorithm BIB002 , by transforming the original form Eq. (11) for sampling a k in ICWS into an equivalent version and then simplifying it. Taking into consideration r k = − ln(u k1 u k2 ) ∼ Gamma(2, 1) and c k = − ln(v k1 v k2 ) ∼ Gamma(2, 1), where u k1 , u k2 , v k1 , v k2 ∼ Uniform(0, 1), Eq. (11) can be rewritten as In BIB002 , Wu et. al. has proved that and whereŜ k is an unbiased estimator for S k , andŜ k = y k u −1 k1 . Consequently, Eq. (16) can be simplified as where x k ∼ Uinform(0, 1), and Also, Eq. (7) can be rewritten as The only difference between ICWS and PCWS lies in the specific implementation of uniformity, although they both employ the property of exponential distribution. The former adopts Eq. (15), which is equivalent to Eq. (9), while the latter uses Eq. BIB001 . We know from Eqs. BIB001 and (20) that PCWS just employs four uniform random variables for each element, i.e., u k1 , u k2 , β k and x k . Obviously, the time and space complexity of PCWS are both O(4nD), while the time and space complexity of ICWS are essentially O(5nD). Also, the authors shows that PCWS is more efficient empirically than ICWS.