Introduction - Big Data: Related Technologies, Challenges and Future Prospects - page 7

projects. Taking IBM as an example, since 2005, IBM has invested USD 16 billion
on 30 acquisitions related to big data. In academia, big data was also under the
spotlight. In 2008, Nature published the big data special issue. In 2011, Science also
launched a special issue on the key technologies of “data processing” in big data. In
2012, European Research Consortium for Informatics and Mathematics (ERCIM)
News published a special issue on big data. In the beginning of 2012, a report titled
Big Data, Big Impact presented at the Davos Forum in Switzerland, announced that
big data has become a new kind of economic assets, just like currency or gold.
Gartner, an international research agency, issued Hype Cycles from 2012 to 2013 ,
which classified big data computing, social analysis, and stored data analysis into
48 emerging technologies that deserve most attention.
Many national governments such as the U.S. also paid great attention to big
data. In March 2012, the Obama Administration announced a USD 200 million
investment to launch the Big Data Research and Development Initiative ,which
was a second major scientific and technological development initiative after the
Information Highway Initiative in 1993. In July 2012, the Japan's ICT project
issued by Ministry of Internal Affairs and Communications indicated that the big
data development should be a national strategy and application technologies should
be the focus. In July 2012, the United Nations issued Big Data for Development
report, which summarized how governments utilized big data to better serve and
protect their people.

1.5 Challenges of Big Data

The sharply increasing data deluge in the big data era brings huge challenges on
data acquisition, storage, management and analysis. Traditional data management
and analytics systems are based on the relational database management system
(RDBMS). However, such RDBMSs only apply to structured data, other than semi-
structured or unstructured data. In addition, RDBMSs are increasingly utilizing
more and more expensive hardware. It is apparently that the traditional RDBMSs
cannot handle the huge volume and heterogeneity of big data. The research
community has proposed some solutions from different perspectives. For example,
cloud computing is utilized to meet the requirements on infrastructure for big data,
e.g., cost efficiency, elasticity, and smooth upgrading/downgrading. For solutions of
permanent storage and management of large-scale disordered datasets, distributed
file systems [ 24 ]andNoSQL[ 25 ] databases are good choices. Such programming
frameworks have achieved great success in processing clustered tasks, especially
for webpage ranking. Various big data applications can be developed based on these
innovative technologies or platforms. Moreover, it is non-trivial to deploy the big
data analytics systems.