Real-Time Data Analytics in Internet of Things Systems

2020 ◽  
pp. 1-28
Author(s):  
Tianqi Yu ◽  
Xianbin Wang
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Data Analytics ◽  
Time Data ◽  
Real Time Data

Real-time data analytics in healthcare using the Internet of Things

2020 ◽  
pp. 37-50
Author(s):  
Satya Narayan Sahu ◽  
Maheswata Moharana ◽  
Purna Chandra Prusti ◽  
Shanta Chakrabarty ◽  
Fahmida Khan ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Data Analytics ◽  
The Internet ◽  
Time Data ◽  
Real Time Data ◽  
The Internet Of Things

Improving latency in Internet-of-Things and cloud computing for real-time data transmission: a systematic literature review (SLR)

2021 ◽  
Author(s):  
Saurabh Shukla ◽  
Mohd. Fadzil Hassan ◽  
Duc Chung Tran ◽  
Rehan Akbar ◽  
Irving Vitra Paputungan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Literature Review ◽  
Real Time ◽  
Systematic Literature Review ◽  
Data Transmission ◽  
Time Data ◽  
Real Time Data

Move Real-Time Data Analytics to the Cloud: A Case Study on Heron to Dataflow Migration

2021 ◽  
Author(s):  
Huijun Wu ◽  
Xiaoyao Qian ◽  
Aleks Shulman ◽  
Kanishk Karanawat ◽  
Tushar Singh ◽  
...  
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Time Data ◽  
Real Time Data

A Real-Time Data Analytics-Based Crop Diseases Recognition System

2019 ◽  
pp. 245-256
Author(s):  
Chiranji Lal Chowdhary ◽  
Rachit Bhalla ◽  
Esha Kumar ◽  
Gurpreet Singh ◽  
K. Bhagyashree ◽  
...  
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Recognition System ◽  
Time Data ◽  
Real Time Data ◽  
Crop Diseases

Development of Failure Prediction Models for Subsea Blowout Preventers Using Data Analytics and AI

2021 ◽  
Author(s):  
Rodrigo Chamusca Machado ◽  
Fabbio Leite ◽  
Cristiano Xavier ◽  
Alberto Albuquerque ◽  
Samuel Lima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Analytics ◽  
Historical Data ◽  
Learning Algorithms ◽  
Failure Prediction ◽  
Machine Learning Algorithms ◽  
Time Data ◽  
Real Time Data ◽  
Operational Data

Objectives/Scope This paper presents how a brazilian Drilling Contractor and a startup built a partnership to optimize the maintenance window of subsea blowout preventers (BOPs) using condition-based maintenance (CBM). It showcases examples of insights about the operational conditions of its components, which were obtained by applying machine learning techniques in real time and historic, structured or unstructured, data. Methods, Procedures, Process From unstructured and structured historical data, which are generated daily from BOP operations, a knowledge bank was built and used to develop normal functioning models. This has been possible even without real-time data, as it has been tested with large sets of operational data collected from event log text files. Software retrieves the data from Event Loggers and creates structured database, comprising analog variables, warnings, alarms and system information. Using machine learning algorithms, the historical data is then used to develop normal behavior modeling for the target components. Thereby, it is possible to use the event logger or real time data to identify abnormal operation moments and detect failure patterns. Critical situations are immediately transmitted to the RTOC (Real-time Operations Center) and management team, while less critical alerts are recorded in the system for further investigation. Results, Observations, Conclusions During the implementation period, Drilling Contractor was able to identify a BOP failure using the detection algorithms and used 100% of the information generated by the system and reports to efficiently plan for equipment maintenance. The system has also been intensively used for incident investigation, helping to identify root causes through data analytics and retro-feeding the machine learning algorithms for future automated failure predictions. This development is expected to significantly reduce the risk of BOP retrieval during the operation for corrective maintenance, increased staff efficiency in maintenance activities, reducing the risk of downtime and improving the scope of maintenance during operational windows, and finally reduction in the cost of spare parts replacementduring maintenance without impact on operational safety. Novel/Additive Information For the near future, the plan is to integrate the system with the Computerized Maintenance Management System (CMMS), checking for historical maintenance, overdue maintenance, certifications, at the same place and time that we are getting real-time operational data and insights. Using real-time data as input, we expect to expand the failure prediction application for other BOP parts (such as regulators, shuttle valves, SPMs (Submounted Plate valves), etc) and increase the applicability for other critical equipment on the rig.

Trends and Technologies in Big Data Processing

2020 ◽  
pp. 17-42
Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Computing Technology ◽  
Time Data ◽  
Big Data Processing ◽  
Real Time Data ◽  
Real Time Data Processing

Data type and amount in human society is growing at an amazing speed, which is caused by emerging new services such as cloud computing, internet of things, and location-based services. The era of big data has arrived. As data has been a fundamental resource, how to manage and utilize big data better has attracted much attention. Especially with the development of the internet of things, how to process a large amount of real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention to high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. In this chapter, various big data processing techniques are discussed.

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