When dealing with problems that require checking the answer of some ranges inside a given array, the sliding window algorithm can be a very powerful technique. In this tutorial, we’ll explain the sliding window technique with both its variants, the fixed and flexible window sizes. Also, we’...
While presenting the data stream model in Chapter 1, we explained it using two examples. In one example, the input for the algorithm appeared on a magnetic tape which can be efficiently accessed only in a sequential manner. In this example, the reason that we got the input in the form ...
On the other hand, increasing c also has a positive impact on the accuracy of the method, since, the larger c is, the more our algorithm approaches the ideal algorithm explained in the previous subsection. However, larger values of c increase both the memory and CPU requirements of our ...
Algorithm 1 performs the sliding window exponentiation method, assuming that the exponent is given in a windowed form, in two steps: It first precomputes the values of\(b^1 \bmod p, b^3 \bmod p, \cdots , b^{2^w-1} \bmod p\)for odd powers ofb. Then, the algorithm scans the...
In this section, we propose an efficient stream mining algorithm, called Top-DSW (Top-k path traversal patterns of stream Damped Sliding Windows), to discover the set of top-k path traversal patterns over Web click streams within a damped sliding window. In the framework of Top-DSW, there ...
2.1. Computation of SSTD window-sizes 2.1.1. EMD decomposition EMD uses a sifting algorithm to decompose the original multicomponent time series into nearly orthogonal modes spanning narrow frequency bands (Patrick and Goncalves, 2004), which are named intrinsic mode functions (IMFs). Let y(t)∈...
We used LR as estimation algorithm and used the entire CPU utilization data of the selected VM as a time series. We identified the optimal observation window size using exhaustive search among window sizes 2 to 30 and chosen the window size which yields maximum accuracy as the optimal window ...
In Algorithm 1, we define a function SPLIT_TENSOR, which is used to handle tensor for the Local Attention Structure. In Algorithm 2, we define a function LOCAT_ATTENTION, which is used to output local tensor. In Algorithm 3, we construct the SLAM Framework by the function MAKE_SLAM. ...
which is the set of all Hamming distances from every N-bit sub-code to every other N-bit sub-code in the set to be computed, the optimum order for computing them all for a minimum effort may be determined by using a Viterbi algorithm to test all possible paths. It may turn out for...
In one or more embodiments, a method of processing a soft value sequence according to an iterative soft-input-soft-output (SISO) algorithm comprises carrying out sliding-window proc