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’...
The sliding window algorithm is an algorithm for more introductory topics. It is generally the optimal solution to some regular array problems. That is to say, if an array problem can be solved by dynamic programming, but it can be solved by sliding window, then the efficiency of sliding win...
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 ...
In Table 1, we show a similar analysis as carried out in [22], but extending the comparison to other window based programs and different prediction sets for the CpGcluster algorithm. When considering CpGcluster islands with p-value≤ 1E-5 (the original relaxed set), the CGI fraction ...
duration in this case. To detect this kind of attack, one has to aggregate more the traffic in time. For the sliding HyperLogLog algorithm, a larger time windowW′=5 min can be added. The algorithm is performed independently and in parallel for the two time scales:W=60 s andW′=5 ...
Benjamin Van Roy, A Short Proof of Optimality for the MIN Cache Replacement Algorithm, Dec. 2, 2010, pp. 1-3. Benson et al., Disco: Efficient Distributed Window Aggregation, Proceedings of the 23rd International Conference on Extending Database Technology (EDBT), Mar. 30-Apr. 2, 2020. ...
Fig. 6. Epicentral map of the earthquakes listed in Table 1, located using a 3D velocity model with SIMULPS algorithm. (a) Distribution of the seismic and GNSS stations in the Etna region; (b) Fault plane solution of the ML 3.6, 2009 mainshock. In about a month, twenty-five earthquake...
In the sliding-window model, the computation is further restricted to the latest w elements in the stream, and an algorithm is required to find good solutions in sublinear time and space w.r.t. the window size. However, the only known algorithms [17,20] for fair max–min diversity ...
The fundamental parameters of the method are explained below: Figure 1. Representation of the sliding window method. Position of the window, which is identified according to the position of its center, which, taking into account that the rows will be identified as i, and the columns as j...
The rough matching procedure is an incremental algorithm. When the trajectory input by the user produces an inflection point, or when staying time that the user stay on a position exceeds a value, or when the user finishes the input of trajectory and lifes the hand, the present invention ...