Frontiers in Massive Data Analysis
2013

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Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data.

Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale—terabytes and petabytes—is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge—from computer science, statistics, machine learning, and application disciplines—that must be brought to bear to make useful inferences from massive data.

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190 pages · 6 x 9 · paperback
ISBN Paperback: 0-309-28778-2
ISBN Ebook: 0-309-28779-0
DOI: https://doi.org/10.17226/18374

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National Research Council. 2013. Frontiers in Massive Data Analysis. Washington, DC: The National Academies Press.

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Front Matter i-xiv
Summary 1-10
1 Introduction 11-21
2 Massive Data in Science, Technology, Commerce, National Defense, Telecommunications, and Other Endeavors 22-40
3 Scaling the Infrastructure for Data Management 41-57
4 Temporal Data and Real-Time Algorithms 58-65
5 Large-Scale Data Representations 66-81
6 Resources, Trade-offs, and Limitations 82-92
7 Building Models from Massive Data 93-119
8 Sampling and Massive Data 120-132
9 Human Interaction with Data 133-145
10 The Seven Computational Giants of Massive Data Analysis 146-160
11 Conclusions 161-166
Appendixes 167-168
Appendix A: Acronyms 169-170
Appendix B: Biographical Sketches of Committee Members 171-176

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ABC	approximate Bayesian computation
AMT	Amazon Mechanical Turk
API	application programming interface

CCD	charge-coupled device
CPU	central processing unit
CQL	Contextual Query Language

DAG	directed-acyclic-graph
DBMS	database management system
DNA	deoxyribonucleic acid
DSMS	data stream management system

FDR	false discovery rate
FFTW	Fastest Fourier Transform in the West
FPGA	field programmable gate array
FTRL	follow the regularized leader

GB	gigabyte
GFS	Google’s File System
GPU	graphics processing unit

HDFS	Hadoop distributed file system

I/O	input/output

LAPACK	Linear Algebra PACKage library
LP	linear programming

MCMC	Markov Chain Monte Carlo
MLE	maximum likelihood estimation

NP	non-deterministic polynomial time

OCR	optical character recognition

PB	petabyte
PCA	principal components analysis

QFS	Quick File System
QP	quadratic programming

RAM	random access memory
RDS	respondent-driven sampling

S3	Simple Storage Service
SQL	Structured Query Language

TB	terabyte
TCAM	ternary content addressable memory

Frontiers in Massive Data Analysis (2013)

Chapter: Appendix A: Acronyms

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Frontiers in Massive Data Analysis (2013)

Chapter: Appendix A: Acronyms