1.
Introduction
Tim O’Reilly, by declaring in his famous
article “What is Web 2.0” that “data is the next Intel Inside” made indeed a
visionary declaration for data revolution. However creating intelligent values
from Big Data cannot be accomplished by just aggregating large amounts of data
or performing analysis. The necessity to make sense and maximize utilization of
such vast amounts of data for knowledge discovery and decision-making is
crucial to scientific advancement. This has led to some recent initiatives in
both theory and practice in order to find some techniques to handle Big Data
challenges.
2.
Research
direction
Some
of the active areas of research under Big Data are:
§ Text- and data-mining of historical and
archival material.
§ Social media analysis, including sentiment
analysis
§ Knowledge Mapping from Big Data Sources
§ Crowd-sourcing and big data
§ Privacy Preserving Big Data Collection /
Analytics
§ Relationship between ‘small data’ and big
data
§ NoSQL databases and their application
§ Big data and the construction of memory and
identity
§ Big data and archival practice
§ Construction of big data
§ Big data in Heritage
§ Etc…
3.
Challenges
As pointed out in [1, 2, 3], applying Big
Data analytics to the field of development faces several challenges. Some challenges
relate to the data including its acquisition and sharing and overarching
concern over privacy, and others pertain to its analysis. Privacy is the most sensitive issue, with conceptual, legal, and technological
implications. Access and sharing are
not the least given the reluctance of private companies and other institutions
to share data about their clients and users, as well as about their own
operations. Obstacles may include legal or reputational considerations, a need
to protect their competitiveness, a culture of secrecy, and, more broadly, the
absence of the right incentive and information structures. There are also
institutional and technical challenges—when data is stored in places and ways
that make it difficult to be accessed, transferred, etc. Another key challenge is the analysis itself. Working with new data sources brings about a
number of analytical challenges. The relevance and severity of those challenges
will vary depending on the type of analysis being conducted, and on the type of
decisions that the data might eventually inform. The analysis challenge can be
splitted into three distinct categories: (1) getting the picture right, i.e.
summarizing the data (2) interpreting, or making sense of the data through
inferences, and (3) defining and detecting anomalies.
4.
Applications
Big
Data holds a tremendous wealth of information and, like nanotechnology and
quantum computing, it will shape the twenty-first century with highly promising
applications. As presented in [1, 3], Big Data if properly analyzed can offer
the opportunity for an improved understanding of human behavior that can
support the field of global development in three main ways:
a) Early warning: early detection of anomalies in how
populations use digital devices and services can enable faster response in
times of crisis;
b) Real-time awareness: Big Data can paint a
fine-grained and current representation of reality which can inform the design
and targeting of programs and policies;
c) Real-time feedback: the ability to monitor a
population in real time makes it possible to understand where policies and
programs are failing and make the necessary adjustments.
5.
Conclusion
Despite
the overwhelming challenges Big Data present potential growing applications and
concerns ranging from government, industries to academia as illustrated in [2, 3].
Therefore the aim of this article has
been to present some potential research directives on Big Data as well as its
challenges and potential applications.
References:
[1]
“Big Data for Development: Challenges and Opportunities”, Global Pulse, May
2012
[2]
“Big data in canada: challenging complacency for competitive advantage”. Nigel
Wallis, December 2012.
[3] "Algorithm and approaches to handle
large Data- A Survey", Chanchal Yadav, Shuliang Wang ,Manoj Kumar, IJCSN,
Vol 2, Issue 3, 2013.
