todays, in many modern applications, we search for frequent and repeating patterns in the analyzed data sets. in this search, we look for patterns that frequently appear in data set and mark them as frequent patterns to enable users to make decisions based on these discoveries. most algorithms presented in the context of data stream mining and frequent pattern detection, work either on uncertai...

Sliding windows of data stream have rich semantics, which results all kinds of window semantics of different data stream, so join semantics between the different types of windows becomes very complicated. The basic join semantic of data streams, the join semantic of tuple-based sliding window and the join semantic of time-based sliding window have partly solved the semantics of stream joins, bu...

Todays, in many modern applications, we search for frequent and repeating patterns in the analyzed data sets. In this search, we look for patterns that frequently appear in data set and mark them as frequent patterns to enable users to make decisions based on these discoveries. Most algorithms presented in the context of data stream mining and frequent pattern detection, work either on uncertai...

Online mining of frequent itemsets over a stream sliding window is one of the most important problems in stream data mining with broad applications. It is also a difficult issue since the streaming data possess some challenging characteristics, such as unknown or unbound size, possibly a very fast arrival rate, inability to backtrack over previously arrived transactions, and a lack of system co...

The sliding window algorithm is widely used in many standard network protocols. It can ensure a correct data transfer over unreliable channels where packets may be duplicated, lost, or re-ordered. By now only few papers have presented the performance of the sliding window protocol. A number of parameters affecting the overall performance of the system still need to be investigated systematicall...

Sliding window is an interesting model to solve frequent pattern mining problem since it does not need entire history of received transactions and can handle concept change by considering recent data. However, in the previous sliding window algorithms, required amount of memory and processing time with respect to limited number of transactions within window is very large. To overcome this short...

Constrained coding is used widely in digital communication and storage systems. In this article, we study a generalized sliding window constraint called the skip-sliding window. A (SSW) code defined terms of length L window, skip J, cost E each Each valid codeword + kJ determined by k+1 windows where i starts at (iJ 1)th symbol for all non-negative integers such that ≤ k; must be satisfied. SSW...

The short-time Fourier transform of a discrete-time signal, which is the Fourier transform of a “windowed” version of the signal, is interpreted as a sliding-window spectrum. This sliding-window spectrum is a function of two variables: a discrete time index, which represents the position of the window, and a continuous frequency variable. It is shown that the signal can be reconstructed from th...

We consider time-space tradeoffs for exactly computing frequency moments and order statistics over sliding windows [16]. Given an input of length 2n− 1, the task is to output the function of each window of length n, giving n outputs in total. Computations over sliding windows are related to direct sum problems except that inputs to instances almost completely overlap. • We show an average case ...

Existing digital logic simulators typically depend on a discrete-time model of circuit behaviour. Whilst this approach is sufficient in many cases for the validation of the behaviour of synchronous circuits, it is not good at identifying glitches that are narrower than the available time resolution. Moreover, it is not generally feasible to accommodate uncertainty in delay time in such a way as...