Cloud-Based Infrastructure for Data-Intensive e-Science Applications: Requirements and Architecture - Cloud Computing with e-Science Applications - page 20

The chapter is organized as follows: Section 2.2 looks into the definition
and nature of big data in e-science, industry, business, and social networks,
also analyzing the main drivers for big data technology development.
Section 2.3 gives an overview of the main research communities and sum-
marizes requirements for future SDI. Section 2.4 discusses challenges to data
management in big data science, including a discussion of SDLM. Section 2.5
introduces the proposed e-SDI architecture model that is intended to answer
the future big data challenges and requirements. Section 2.6 discusses SDI
implementation using cloud technologies. Section 2.6 discusses security and
trust-related issues in handling data and summarizes specific requirements
to access the control infrastructure for modern and future SDIs.
2.2 Big Data Definition
2.2.1 Big Data in e-Science, Industry, and Other Domains
Science traditionally has dealt with challenges to handle large volumes of
data in complex scientific research experiments. Scientific research typically
includes a collection of data in passive observation or active experiments
that aim to verify one or another scientific hypotheses. Scientific research
and discovery methods typically are based on the initial hypothesis and a
model that can be refined based on the collected data. The refined model
may lead to a new, more advanced and precise experiment or reevaluation of
the previous data. Another distinctive feature of modern scientific research
is that it suggests wide cooperation between researchers to challenge com-
plex problems and run complex scientific instruments.
In industry, private companies will not share data or expertise. When
dealing with data, companies will always intend to keep control over their
information assets. They may use shared third-party facilities, like clouds,
but special measures need to be taken to ensure data protection, including
data sanitization. Also, companies might use shared facilities only for proof
of concept and do production data processing at private facilities. In this
respect, we need to accept that science and industry cannot be done in the
same way; consequently, this will be reflected in how they can interact and
how the big data infrastructure and tools can be built.
With the proliferation of digital technologies into all aspects of business
activities and emerging big data technologies, the industry is entering a
new playing field when it needs to use scientific methods to benefit from
the possibility of collecting and mining data for desirable information, such
as market prediction, customer behavior predictions, social groups activity
predictions, and so on.