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:
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: http://fclib.ca.sandia.gov