While the traditional way to analyze unstructured data was limited to a single computing node, the cloud model enables multiple computing nodes to each work on a portion of the data in parallel. As a result, agencies today can process large data sets quickly on cloud-based distributed systems. MapReduce makes generating statistics from both large amounts of data and unstructured data possible, in essence generating manageable structured data from unstructured data.
Isale of government data software#
MapReduce is a software framework launched by Google in 2004 that supports distributed computing on large sets of data for computer clusters. One of the most common tools utilized by government organizations is MapReduce. As a result, translation technologies are also a key component in effectively managing, sorting and distributing the types of unstructured data encountered in the public sector. Additionally, because government data is of a global nature, big data must take into account not only different types of data but also the multi-lingual nature of much of the data collected today. Since public agencies have not traditionally had enough human capital or computational capacity to manage and analyze all of their data, and given the shifting nature and exponentially growing amount of data, cloud computing-enabled big data tools are essential. The result is a movement called big data, which seeks to capture and process vast amounts of unstructured data. To understand what patterns exist in unstructured data, government agencies apply statistical models to large quantities of unstructured data. Much of that information is unstructured, meaning it does not fit into a pre-defined data model. Census to local municipalities, there are massive amounts of data in agency computer systems. From national intelligence to the IRS, the U.S. When it comes to managing data, government agencies have always had the same issue.
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