Sliding-window analysis has widely been used to uncover synonymous (silent, d(S)) and nonsynonymous (replacement, d(N)) rate variation along the protein sequence and to detect regions of a protein under selective constraint (indicated by d(N)d(S)). The approach compares two or more protein-coding genes and plots estimates d(/\)(S) and d(/\)(N) fro...

While traditional data-management systems focus on evaluating single, adhoc queries over static data sets in a centralized setting, several emerging applications require (possibly, continuous) answers to queries on dynamic data that is widely distributed and constantly updated. Furthermore, such query answers often need to discount data that is “stale”, and operate solely on a sliding window of...

This paper proposes implementations of the well-known traffic policing mechanisms for high speed networks. The implementations are based on a high performance development environment and are described through commented block diagrams. The considered mechanisms are Leaky Bucket with variants, Jumping Window, Triggered Jumping Window, Continuous Sliding Window, Discretized Sliding Window and Time...

While vehicle license plate recognition (VLPR) is usually done with a sliding window approach, it can have limited performance on datasets with characters that are of variable width. This can be solved by hand-crafting algorithms to prescale the characters. While this approach can work fairly well, the recognizer is only aware of the pixels within each detector window, and fails to account for ...

We consider the problem of maintaining aggregates over recent elements of a massive data stream. Motivated by applications involving network data, we consider asynchronous data streams, where the observed order of data may be different from the order in which the data was generated. The set of recent elements is modeled as a sliding timestamp window of the stream, whose elements are changing co...

• Standard stream model: m elements from universe of size n, come one by one. Goal: compute a function of stream. Constraints: (1) Limited space (working memory), sublinear in n and m. (2) Access data sequentially. (3) Process each element quickly. • Graph stream: elements of the stream are edges. Let n be the number of vertices. We have m = O(n 2). To be able to handle interesting functions, w...

MAX and MIN are two important aggregates offered by the original SQL specification. In the paper, we propose a novel mechanism, i.e. the exemplary sketch, to evaluate MAX and MIN over sliding windows with various size in the data stream environment. Performance analysis shows that evaluating MAX or MIN over w sliding windows with various size using the exemplary sketch takes O(ln n) expected am...

This paper presents a fast algorithm for the computation of sliding conjugate symmetric sequency-ordered complex Hadamard transform (CS-SCHT). The algorithm calculates the values of window from those of window , one lengthWalsh Hadamard transform (WHT) and one lengthModified WHT (MWHT). The proposed algorithm requires arithmetic operations, which is more efficient than the block-based algorithm...

A data stream is a real-time, continuous, ordered sequence of items generated by sources such as sensor networks, Internet traffic flow, credit card transaction logs, and on-line financial tickers. Processing continuous queries over data streams introduces a number of research problems, one of which concerns evaluating queries over sliding windows defined on the inputs. In this paper, we descri...

This note describes a Sliding Window Filter that is an on-line constanttime approximation to the feature-based 6-degree-of-freedom full Batch Least Squares Simultaneous Localization and Mapping (SLAM) problem. We contend that for SLAM to be useful in large environments and over extensive run-times, its computational time complexity must be constant, and its memory requirements should be at most...