MSIT capstone
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Assignment-3.docxNETWORK AND MANAGEMENT OF BIG DATA 2
Data Analysis and Prediction for Future Maintenance
MIT-681
Prof. Mark O'Connell
IGlobal University
Running head: NETWORK AND MANAGEMENT OF BIG DATA 1
Before discussing the benefit to my firm, it is sensible to describe network services which are provided cloud based. Networking which is available cloud based is a type of facilitating that totally exists and works in a web accessible framework/environment. The cloud network management, assets, resources, infrastructure, and other operational procedures are performed from the cloud. (Technology Trends, 2018). The important objective behind cloud-based networking is to give network connectivity among resources and applications present on a cloud. For example, interconnectivity between virtual machines created within a similar cloud environment is achieved through cloud-based networking (Technology Trends, 2018).
Ongoing improvement of distributed cloud environments needs cutting-edge network infrastructure to enable network automation, superior data exchange/transfer, virtualization, and verified access of start to finish resources over provincial limits. So as to meet these creative cloud networking requirements, software-defined wide area network (SD-WAN) is mainly requested to merge distributed cloud resources (e.g., virtual machines) in a programmable and intelligent manner over distant connectivity. (Kim D., Kim Y., Kim K. & Gil J., 2017). Furthermore, WAN network is moving to the cloud, and that implies that IT and business directors need to discard hardware devices for the WAN. The new model will be a cloud administration that the client can organize and request on the Web with software, or at the base utilize just a small box which is overseen by the service provider (Raynovich, 2015).
To enhance quicker with our big data produced from IoT, our startup firm will utilize cloud-based connecting/networking services to deliver numerous advantages for startup firm. Sensors which is installed on manufacturing equipment will make forecasting against that information, to stay away from breakdowns, bottlenecks and other delays as well. what's more, it matches with parallel activity that originally happened in the telecommunication industry quite a while in the past. By recognizing performance shortcomings, communications service providers (CSPs) started to proactively happen, and subsequently to fix or networking equipment’s piece before it malfunctioned or defected and intermittent communication services. This behavior makes speed paramount i.e., speed of analysis of the data, and speed of distribution of our analytical results to our perceived customer.
Big Data is not only a matter of volume increased of the collected data, but also
includes the evolution of data peculiarities, namely data variety and data velocity.
The intrinsic pattern of comprehensive data becomes the major driver for compa-
Big Data Analytics for Predictive Maintenance Strategies
53
nies to investigate the use of big data analytics. The features of big data are broadly
recognized as “4Vs” – i.e. volume, velocity, variety and value (Xin & Ling, 2013).
Big Data is not only a matter of volume increased of the collected data, but also
includes the evolution of data peculiarities, namely data variety and data velocity.
The intrinsic pattern of comprehensive data becomes the major driver for compa-
Big Data Analytics for Predictive Maintenance Strategies
53
nies to investigate the use of big data analytics. The features of big data are broadly
recognized as “4Vs” – i.e. volume, velocity, variety and value (Xin & Ling, 2013).
Big Data is not only a matter of volume increased of the collected data, but also includes the evolution of data peculiarities, namely data variety and data velocity. The intrinsic pattern of comprehensive data becomes the major driver for our startup firm to investigate the use of big data analytics. The features of big data are broadly recognized as “4Vs” – i.e. volume, velocity, variety and value (Xin & Ling, 2013).
The acquisition and processing of big data largely improves the transparency
along the supply chains providing accurate and timely information for managerial
decision making. Companies and organizations operate on the huge amounts of
data by classifying trends and identifying patterns to produce invaluable knowledge.
Meanwhile the flood of big data with high speed and many variations has chal-
lenged the limited storage and conventional data mining methods. Challenges are
also from processing and analyzing the large amount of unstructured data which are
the major components in the big data acquired. Technologies have been advancing
towards better performance in the big data context regarding integrated platforms,
predictive analytics, and visualization (Lee, Kao, & Yang, 2014). Big data and
predictive analysis are strongly interconnected. Without proper analytics, big data
is just a deluge of data, while without big data, predictive analytics, the strength
of statistics, modeling, and data mining tools for analyzing current and historical
conditions will be undermined.
The entire Big Data framework requires human intelligence and expert opinion in
the design stage. It is difficult for the manufacturer to manage and, control big data
and select relevant information for MDSS. The availability of sensors and techno-
logical advancement enable explorative research of Big Data Analytics, and allows
organizations to expand their capability to enhance the data transparency of machine
status for manufacturers. The speedy data flow and collection of abundant data
through WSNs enhance the potential of analytical performance. The adoption of Big
Data in MDSS state a significant step in machine health condition diagnosis. The
proposed approach helps to mitigate machine failure during production and uncover
the hidden patterns through Big Data Analytics.
The entire Big Data framework requires human intelligence and expert opinion in the design stage. It is difficult for the manufacturer to manage and, control big data and select relevant information for maintenance decision support system (MDSS). That’s why our firm will use the availability of sensors and technological advancement enable explorative research of Big Data Analytics and allows us to expand its capability to enhance the data transparency of machine status for manufacturers. The speedy data flow and collection of abundant data through wireless sense networks (WSN) enhance the potential of analytical performance. The adoption of Big Data in MDSS state a significant step in machine health condition diagnosis. The proposed approach helps to mitigate machine failure during production and uncover the hidden patterns through Big Data Analytics. (Margaret Rouse, 2012)
Various industries noticed that the size of data has been exponentially increasing
and accelerating due to the comprehensive use of sensor network. The transition from
conventional database to non-relational database is not only upgrading the storage
Various industries noticed that the size of data has been exponentially increasing and accelerating due to the comprehensive use of sensor network. The transition from conventional database to non-relational database is not only upgrading the storage capacity, but also requiring an infrastructure and expertise to process, and handle structured and unstructured data. Handling and understanding on the petabytes or even exabyte of data has become a challenge for IT teams. My startup firm will take advantage of advanced capability of data warehouses and network connection which expedites real time data processing. Due to the enormous data booming via WSN, a cohesive platform for processing structure and unstructured data becomes an essential element for our startup firm. The major purpose of Big Data Infrastructure is to resolve the problem of incompatible data formats and non-aligned data structures. The impact on inconsistency of unstructured data requires pre-processing of data input to enhance the performance of Big Data Mining. Investigating the unstructured data by our firm in manufacturing not only create the value for the production engineer but also support MDSS with more vigorous and sophisticated Big Data Analytics.
Several research papers mentioned that analyzing the unstructured data is the first priority in decision-making and prediction (Li, Bagheri, Goote, Hasan, & Hazard, 2013; Muhtaroglu, Demir, Obali, & Girgin, 2013; Wielki, 2013). However, not all the unstructured data can be beneficial to knowledge development and decision-making process. The relevant machine data must be fit for purpose of maintenance policy selection. Proper domain experts in place are critical to interpret the sensory information for predictive maintenance during the design stage of Big Data Analytics. Furthermore, enhancing data quality by the adoption of suitable sensors in the machine is also an importance for the company. Big Data offers tremendous insight to the diagnostics and prognostics of the machine status. Nonetheless, the information reliability from predictive maintenance is only available with appropriate sensors selection and adoption.
In today’s Big Data Analytics, research focus has been shifted from Volume of data to quality data. Regarding the complexity of Big Data Analytics in MDSS, our startup firm will involve to have enough breadth and depth of domain knowledge to design an appropriate Big Data Analytics for maintenance strategies. The proactive approach to predict machine failure provides a high-level reliability for excellence in maintenance management. Further benefit can be summarized as reducing the frequency of corrective maintenance, increasing machine performance and enhancing overall production reliability.
References
Kim, D., Kim, Y., Ki-Hyun, K., & Joon-Min, G. (2017). Cloud-centric and logically isolated virtual network environment based on software-defined wide area network. Sustainability, 9(12), 2382.
Savaram R. (2017). Understanding the relationship between IoT and Big Data retrieved from https://jaxenter.com/relationship-between-iot-big-data-138220.html
Jeff C., Frost, & Sullivan. Bigdata in Manufacturing retrieved from https://bigdata.cioreview.com/cxoinsight/big-data-in-manufacturing--it-s-not-just-for-predictive-maintenance-anymore--nid-24364-cid-15.html
Big Data, Mobile and IoT retrieved from https://www.accruent.com/resources/blog-posts/big-data-mobile-iot-predictive-maintenance
Kx Insights: IIOT and Big Data retrieved from https://kx.com/blog/kx-insights-iiot-and-big-data/
