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Advances in Robotics & Mechanical Engineering

Short CommunicationOpen Access

Insight Bigdata Management Modeling for Business Virtualization Volume 1 - Issue 3

Sadique Shaikh* and Tanvir Begum

  • Department of Management & Science (IMS), MS, India

Received: October 18, 2018;   Published: October 25, 2018

*Corresponding author: Md Sadique Shaikh, KYDSC Trust’s Institute of Management & science, Sakegaon-Bhusawal, MS, India


Abstract PDF

Abstract

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].

Keywords: Mirror Neurons; Neuroscience; Feeling Leadership; Biological Leadership; Personable Leadership

Modeling

Bigdata Design Line Model

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.

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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.

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Conclusion

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.

Acknowledgement

I would like to credit this work to my loving wife Safeena Khan, my angels Md Nameer Shaikh, Md Shadaan Shaikh and my close friend Tanveer Sayyed.

References

  1. h t t p : / / w w w. c i s c o . c o m / c / e n / u s / p ro d u c t s / c l o u d ‐ sys te m s ‐ management/data‐analytics/index.
  2. http://www.techtarget.com/contributor/Rick‐Van‐Der‐Lans.
  3. http://www.b‐eye‐network.com/channels/5087/articles/
  4. http://www.b‐eye‐network.com/channels/5087/view/12495
  5. RF Van Der Lans (2007) Introduction to SQL; Mastering the Relational Database Language, 4th edn); Addison‐Wesley.
  6. RF Van Der Lans (2012) Data Virtualization for Business Intelligence Systems. Morgan Kaufmann Publishers.
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