Using bloom filters and sparse matrix

Sparse matrix can be used as highly efficient data structure. A sparse matrix has more 0 values compared to actual values. For example, a 100 X 100 matrix may have 10,000 cells. Now, out of this 10,000 cells, only 100 have values; rest are 0. Other than the 100 values, remaining cells are occupied with the default value of 0, and they are taking same byte size to store the value 0 to indicate the empty cell. It is a huge waste of space, and we can reduce it using the sparse matrix. We can use different techniques to store the values to the sparse matrix in a separate matrix that will be very lean and will not take any unnecessary spaces. We can also use a linked list to represent the sparse matrix. Here is an example of the sparse matrix: