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Data-Intensive Computing

The world is awash with digital data from social networks, blogs, business, science, and

engineering. Data-intensive computing facilitates understanding of complex problems

that must process massive amounts of data. Through the development of new classes of

software, algorithms, and hardware, data-intensive applications can provide timely and

meaningful analytical results in response to exponentially growing data complexity and

associated analysis requirements. This emerging area brings many challenges that are

different from traditional high-performance computing.

This reference for computing professionals and researchers describes the dimensions

of the ﬁeld, the key challenges, the state of the art, and the characteristics of likely

approaches that future data-intensive problems will require. Chapters cover general

principles and methods for designing such systems and for managing and analyzing

the big data sets of today that live in the cloud, and describe example applications in

bioinformatics and cyber-security that illustrate these principles in practice.

ian gorton

is a Laboratory Fellow in Computational Sciences and Math at Paciﬁc

Northwest National Laboratory (PNNL), where he manages the Data Intensive Scientiﬁc

Computing group and was the Chief Architect for PNNL’s Data Intensive Computing

Initiative. Gorton is a Senior Member of the IEEE Computer Society and a Fellow of

the Australian Computer Society.

deborah k. gracio

joined Paciﬁc Northwest National Laboratory in 1990 and is

currently the Director of the Computational and Statistical Analytics Division and of

the Data Intensive Computing Research Initiative. Since joining the laboratory, she has

led the research, development, and management of multiple cross-disciplinary, multi-

laboratory projects focused in the basic sciences and national security sectors.

Data-Intensive Computing

Architectures, Algorithms, and Applications

Edited by

IAN GORTON

Paciﬁc Northwest National Laboratory

DEBORAH K. GRACIO

Paciﬁc Northwest National Laboratory