Need of this Algorithm
The Flajolet-Martin algorithm, can be used to determine how many unique elements are there in a database. It is very helpful in situations where the size of the memory is large and it is difficult to process the complete dataset. The following are some of the main uses and benefits of the Flajolet-Martin algorithm:
- Scalability: This algorithm is considered scalable as it can be used to handle large datasets and can estimate the number of unique elements in the dataset without storing the entire dataset in the memory.
- Memory efficiency: This algorithm requires a small amount of memory to count the number of unique elements. This can be achieved by using bit manipulations and hash functions to create a small representation of the data.
- Speed: This algorithm is suited for real-time applications since it is computationally efficient and can quickly generate count of the number of unique items by using relatively less computing.
Flajolet Martin Algorithm
The Flajolet-Martin algorithm is also known as probabilistic algorithm which is mainly used to count the number of unique elements in a stream or database . This algorithm was invented by Philippe Flajolet and G. Nigel Martin in 1983 and since then it has been used in various applications such as , data mining and database management.
The basic idea to which Flajolet-Martin algorithm is based on is to use a hash function to map the elements in the given dataset to a binary string, and to make use of the length of the longest null sequence in the binary string as an estimator for the number of unique elements to use as a value element.
The steps for the Flajolet-Martin algorithm are:
- First step is to choose a hash function that can be used to map the elements in the database to fixed-length binary strings. The length of the binary string can be chosen based on the accuracy desired.
- Next step is to apply the hash function to each data item in the dataset to get its binary string representation.
- Next step includes determinig the position of the rightmost zero in each binary string.
- Next we compute the maximum position of the rightmost zero for all binary strings.
- Now we estimate the number of distinct elements in the dataset as 2 to the power of the maximum position of the rightmost zero which we calculated in previous step.
The accuracy of Flajolet Martin Algorithm is determined by the length of the binary strings and the number of hash functions it uses. Generally, with increse in the length of the binary strings or using more hash functions in algorithm can often increase the algorithm’s accuracy.
The Flajolet Martin Algorithm is especially used for big datasets that cannot be kept in memory or analysed with regular methods. This algorithm , by using good probabilistic techniques, can provide a precise estimate of the number of unique elements in the data set by using less computing.