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FCLib, a data analysis toolkit, was constructed to meet the needs of data discovery in large-scale, spatio-temporal data. FCLib is a C library toolkit of building blocks that can be used to assemble analyses for data discovery. Important features of FCLib include the following:

  1. Support for feature-based analysis
  2. Minimization of low-level processing
  3. Ease of use
  4. Applications in a wide variety of science domains

Data discovery is the iterative process of exploring data to extract information. As data increase in size and complexity, current data analysis methods become more cumbersome, slower, and more error-prone since these methods rely on analysts to examine each piece of data and move data between tools. FCLib was designed to automate as much low-leveling processing as possible, while still allowing analysts the freedom to flexibly compose their own chains of analysis. Instead of worrying about low-level details, users of FCLib can compose data analyses at a higher level.

Most data analysis tools lack a key capability useful for analyzing large data: the native ability to manipulate small regions of interest known as “features”. Features are usually coherent structures that persist over some period of time. Examples include vertex tubes in fluid-dynamical systems, failure zones in mechanical systems, and hot spots in chemical systems. Although feature-based analysis is a common approach, most data analysis tools do not directly support this approach. Instead analysts typically hand-select regions of interest and then export these regions to other tools for further analysis. In contrast to other data analysis tools, FCLib provides a native data structure for features, as well as analysis building blocks that are feature-aware.

FCLib is available for free download at: