I was started my research only with one thinking “How we feel and understand sadness or happiness of others, why our eyes
sometime filled with tears, when we see others crying or sad, why we cheer up when we see others happy, why we bless others,
why we care for others, why we become sad when we watch sad seen in movies, why we motivate when we watch something
exciting and meaningful in movies, why, why and why?” these are the big questions front of us. My common answer which support
to all these questions is “when situation is common between two or more than two people they completely understand each other,
because their brains neurons handling same situation. Some time may be feelings for other because of past common situation of us
is the present situation of someone or may be some time we think if that situation on me what I would do. Hence common situation
either good or bad doesn’t matter but common situation people show strong feelings about each other with respecting emotions
and feelings of each other’s and this is because “common situation setup brain-to-brain link between people through which they
understand feelings and emotions of each other [1].
This is our first developed model related to Bigdata and
Business virtualization. This model has expansion through design
line from Bigdata Analysis to Bigdata Designing and Bigdata
Designing to Bigdata Development with their respective designing
essentials and functions. At stage one Bigdata analysis fundamental
considerations are Corporate Opportunities, Environments,
function, data needs. At stage two Bigdata designing is Advance
Bigdata Engineering tools to fulfill requirements using Hadoop or
other one and at the stage three Bigdata development we need to
Advance Business Interface, data streaming, quick response time
for Business Virtualization (VB). These are the few which I covered
likewise several facts and figures need to gather and revised timely
to keep update in Bigdata technologies and Business virtualization,
but most common for all covered on model (Figure 1).
Figure 1: Design line.
3B Models of Bigdata Engineering
This is second important model which purely discussed
to Bigdata engineering requirements considering three most
important factors which are Bigdata Organization, Bigdata Storage
Technologies and Bigdata Link. The first one important factor
Bigdata Organization concern to innovative data structures,
storage technologies-media and procedures to retrieve engineering
whereas Second parameter Bigdata Storage Technologies engage
to design long-lasting Storage, Advanced Backup & Recovery
Mechanisms. The third important criterion is Bigdata Link where
engineering must need to carry on High data streaming and Quick
response time (Figure 2).
Figure 2: Bigdata Organization, Bigdata Storage Technologies and Bigdata Link.
We had developed two models for better understanding with
Bigdata and Business Virtualization labeled as Bigdata Design Line
Model and 3B Model of Bigdata Engineering. Through these models
we focused on engineering aspects of Bigdata as well as discuss
essential of its like to compete in this environment, broadband
operators are trying to implement Big Data projects that give them
new insights into their network and subscribers. Operators have
many disparate systems that send bits of information into the
Telco’s data mart, but the data is fragmented and not very granular.
Operators often have probes deployed in their networks, but
most focus on signaling information and they lack KPIs on the
actual broadband experience delivered to the subscriber - resulting
in customer complaints of a slow network and the operator
responding that their network is up, so it can’t be their problem.
These systems are not integrated into the OSS/BSS and do not
perform subscriber correlation in real-time, losing opportunities
to proactively deliver targeted offers and rectify problems before a
subscriber realizes that there is an issue on the network. Operators
are also looking to monetize their subscriber usage data externally,
but they often lack the granularity and correlation of that data to
maximize the value to external audiences like enterprises, retail, or
advertising companies.
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