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Data interpolation is a fundamental data processing tool in scientific studies and engineering applications.
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In the area of reducing energy consumption in buildings, high-priority research topics include information technology infrastructure for fundamental data gathering, processing and management, whole system and process integration for design and operation of smart buildings, and high-performance building components and sub-systems.
Gene regulatory networks perform fundamental information processing and control mechanisms in the cell.
A suffix tree, which provides us with a linear space full-text index of a given string, is a fundamental data structure for string processing and information retrieval.
The fundamental components of IoT include sensing/acquiring data, heterogeneous connectivity, data processing, as well as applications and services.
Automation of the workflow can greatly accelerate the pace of discovery, and allows researchers to focus on the fundamental scientific questions rather than mastery of specialized software and data processing techniques.
The most fundamental of these are explained in detail: biases introduced during data processing, lack of correction for multiple testing, and an incorrect use of clustering for gene discovery.
The fundamental idea of distributed estimation is to share the task of data processing among the whole network.
Yet, for all of the SQL-like familiarity, these abstraction layers ignore one fundamental reality – MapReduce (and thereby Hadoop) is purpose-built for organized data processing (read: running jobs, or "workflows").
The data processing and filtration of microarray results is of fundamental importance when attempting analysis of amplification reactions from sub-nanogram input amounts of total RNA.
In most cases, however, its accurate determination requires extensive data processing and/or extrapolations, thus rendering the measurement of this fundamental flame property indirect.
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