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production management, business operation and decision making, etc. of modern
enterprises from the management perspective. What's more, the application of
big data to specific fields needs the participation of interdisciplinary talents.
Visualization
: In many human-computer interaction scenarios, the principle of
What You See Is What You Get is followed, e.g., text and image editors. In big
data applications, mixed data may not be is very useful for decision making.
Only when the analytical results are friendly displayed, it may be accepted and
utilized by users. Reports, histograms, pie charts, and regression curves, etc.,
are frequently used to visualize results of data analysis. New presentation forms
will occur in the future, e.g., Microsoft Renlifang, a social search engine, utilizes
relational diagrams to express interpersonal relationship.
Data-Oriented
: It is well-known that programs are consisted of data structures
and algorithms. In the history of program design, it is observed that the role of
data is becoming increasingly more significant. In the small scale data era, in
which logic is more complex than data, program design is mainly focused on
processes. As business data is becoming more complex, object-oriented design
methods are developed. The complexity of business data has far surpassed
business logic and programs gradually transform from algorithm-intensive to
data-intensive. It is anticipated data-oriented program design methods are certain
to emerge, which will have far-reaching influence on the development of IT in
software engineering, architecture, and model design, among others.
Big Data Causes the Revolution of Thinking
: In the big data era, data collection,
acquisition, and analysis are more rapidly accomplished and the massive data
will profoundly influence our ways of thinking. In [
2
], the authors summarizes
the thinking revolution caused by big data as follows:
-
During data analysis, we will try to utilize all data other than only analyzing a
little sample data.
-
Compared with accurate data, we would like to accept numerous and compli-
cated data.
-
We shall pay greater attention to correlations between things other than
exploring causal relationship.
-
The simple algorithms of big data are more effective than complex algorithms
of small data.
-
Analytical results of big data will reduce hasty and subjective factors during
decision making and data scientists will replace “experts.”
Managing Large-scale FlowTable for Software-Defined Networking with Big
Data Techniques
: In the past few years, software-defined networking (SDN) has
been the buzz of the networking world. It was originally proposed to accelerate
networking innovations in legacy campus networks called OpenFlow, which
comprises a number of closed networking boxes with diverse functionalities
(such as routing, switching, firewall, etc.) [
3
]. It is observed that, plenty of
emerging networking problems appeared in the era when cloud computing meets
big data applications, and SDN seems to be extremely suitable for solving those
problems in respect of network efficiency, scalability, flexibility, agility, as well
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