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Author Kepner, Jeremy V., 1969- author
Title Mathematics of big data : spreadsheets, databases, matrices, and graphs / Jeremy Kepner and Hayden Jananthan
Imprint Cambridge, Massachusetts : The MIT Press, [2018]
book jacket
LOCATION CALL # STATUS OPACMSG BARCODE
 Euro-Am 3F Western Mat.  005.7 K445 2018    AVAILABLE  -  30500101554262
 Inform. Sci. Books  H2.8 K38    AVAILABLE    30330000233952
Descript xxi, 418 pages ; 24 cm
text txt rdacontent
unmediated n rdamedia
volume nc rdacarrier
Series MIT Lincoln laboratory series
MIT Lincoln Laboratory series
Note Includes bibliographical references and index
I : Applications and practice. Introduction and overview -- Perspectives on data -- Dynamic distributed dimensional data model -- Associative arrays and musical metadata -- Associataive arrays and abstract art -- Manipulating graphs with matrices -- Graph analysis and machine learning systems -- II : Mathematical foundations. Visualizing the algebra of associative arrays -- Defining the algebra of associative arrays -- Structural properties of associative arrays -- Graph construction and graphical patterns -- III : Linear systems. Survey of common transformations -- Maps and bases -- Linearity of associative arrays -- Eigenvalues and Eigenvectors -- Higher dimensions
Today, the volume, velocity, and variety of data are increasing rapidly across a range of fields, including Internet search, healthcare, finance, social media, wireless devices, and cybersecurity. Indeed, these data are growing at a rate beyond our capacity to analyze them. The tools-including spreadsheets, databases, matrices, and graphs-developed to address this challenge all reflect the need to store and operate on data as whole sets rather than as individual elements. This book presents the common mathematical foundations of these data sets that apply across many applications and technologies. Associative arrays unify and simplify data, allowing readers to look past the differences among the various tools and leverage their mathematical similarities in order to solve the hardest big data challenges
Subject Big data -- Graphic methods
Alt Author Jananthan, Hayden, author
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