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In this paper we try to propose a strategy based on string or pattern matching to handle data streams.
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Therefore a set of metric techniques based on string filtration were developed.
We combine the proposed feature extractors with an approach for classification of pose sequences based on string kernels.
The presented strategy is based on string matching parameter instead of distance to handle the four challenges of data streams.
Our model presents an alternative to solutions of the fine tuning problem based on string landscape scenarios.
Many scholars of innovation have implemented ad-hoc disambiguation methods based on string similarity thresholds and string comparison matching; such methods have been shown to be vulnerable to a number of problems that can adversely affect research results.
In most realistic models of physics based on string theory, this is accomplished by a process called compactification, in which the extra dimensions are assumed to "close up" on themselves to form circles.
The extraction of the relevant abstracts based on string matching can not capture the underlying semantics.
We developed a distributed genome assembler based on string graphs and MapReduce framework, known as the CloudBrush.
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