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AbstractBig Data concerns large volume, complex, growing data set

AbstractBig Data concerns large volume, complex, growing data set

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Abstract:

Big Data concerns large-volume, complex, growing data

sets with multiple, autonomous sources. With the fast

development of networking, data storage, and the data

collection capacity, Big Data is now rapidly expanding in

all science and engineering domains, including physical,

biological and biomedical sciences. This article presents a

HACE theorem that characterizes the features of the Big

Data revolution, and proposes a Big Data processing

model, from the data mining perspective. This data-driven

model involves demand-driven aggregation of information

sources, mining and analysis, user interest modeling, and

security and privacy considerations. We analyze the

challenging issues in the data-driven model and also in the

Big Data revolution.

Introduction

Dr. Yan Mo won the 2012 Nobel Prize in Literature. This is

probably the most controversial Nobel prize of this

category, as Mo speaks Chinese, lives in a socialist

country, and has the Chinese government’s support.

Searching on Google with “Yan Mo Nobel Prize”, we get

1,050,000 web pointers on the Internet (as of January 3,

2013). “For all praises as well as criticisms,” said Mo

recently, “I am grateful.” What types of praises and

criticisms has Mo actually received over his 31-year writing

career? As comments keep coming on the Internet and in

various news media, can we summarize all types of

opinions in different media in a real-time fashion, including

updated, cross-referenced discussions by critics? This

type of summarization program is an excellent example for

Big Data processing, as the information comes from

multiple, heterogeneous, autonomous sources with

complex and evolving relationships, and keeps growing.

Along with the above example, the era of Big Data has

arrived (Nature Editorial 2008; Mervis J. 2012; Labrinidis

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and Jagadish 2012). Every day, 2.5 quintillion bytes of

data are created and 90% of the 2 data in the world today

were produced within the past two years (IBM 2012). Our

capability for data generation has never been so powerful

and enormous ever since the invention of the Information

Technology in the early 19th century. As another example,

on October 4, 2012, the first presidential debate between

President Barack Obama and Governor Mitt Romney

triggered more than 10 million tweets within two hours

(Twitter Blog 2012). Among all these tweets, the specific

moments that generated the most discussions actually

revealed the public interests, such as the discussions

about Medicare and vouchers. Such online discussions

provide a new means to sense the public interests and

generate feedback in real-time, and are mostly appealing

compared to generic media, such as radio or TV

broadcasting. Another example is Flickr, a public picture

sharing site, which received 1.8 million photos per day, on

average, from February to March 2012 (Michel F. 2012).

Assuming the size of each photo is 2 megabytes (MB), this

resulted in 3.6 terabytes (TB) storage every single day. As

“a picture is worth a thousand words”, the billions of

pictures on Flicker are a treasure tank for us to explore the

human society, social events, public affairs, disasters etc.,

only if we have the power to harness the enormous amount

of data. The above examples demonstrate the rise of Big

Data applications where data collection has grown

tremendously and is beyond the ability of commonly used

software tools to capture, manage, and process within a

“tolerable elapsed time”. The most fundamental challenge

for the Big Data applications is to explore the large

volumes of data and extract useful information or

knowledge for future actions (Rajaraman and Ullman,

2011). In many situations, the knowledge extraction

process has to be very efficient and close to real-time

because storing all observed data is nearly infeasible. For

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Abstract: Big Data concerns large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data is now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This article presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This d ata-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution. Introduction Dr. Yan Mo won the 2012 Nobel Prize in Literature. This is probably the most controversial Nobel prize of this category, as Mo speaks Chinese, lives in a socialist country, and has the Chinese government’s support. Searching on Google with “Yan Mo Nobel Prize”, we get 1,050,000 web pointers on the Internet (as of January 3, 2013). “For all praises as well as criticisms,” said Mo recently, “I am grateful.” What types of praises and criticisms has Mo actually received over his 31 -year writing career? As comments keep coming on the Internet and in various news media, can we summarize all types of opinions in different media in a real -time fashion, including updated, cross-referenced discussions by critic ...
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