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Crowd-Sourced Centralized Thermal Imaging for Isolation and Quarantine
Published in Chhabi Rani Panigrahi, Bibudhendu Pati, Mamata Rath, Rajkumar Buyya, Computational Modeling and Data Analysis in COVID-19 Research, 2021
Sudershan Kumar, Prabuddha Sinha, Sujata Pal
For real-time systems, data synchronization is a very important issue. In a real-time system, the data must be consistent having a low error rate and high integrity. Irregularity in data might cause some false inference which may lead to drastic circumstances. Data synchronization updates the data with regular intervals of time and maintains the consistency between two or more devices. It will become challenging when synchronization needs to be done in a remote or mobile network. The main challenge that may occur is the accuracy of data by losing bits in the mobile network, the time interval of syncing of the data security, and data integrity in mobile networks. Data synchronizations mainly deal with updates of the data in all the network nodes and make a single and recently updated copy of the data in the overall system. The real-time analysis helps us to give accurate results in the proposed model.
Mobility Solutions for the Financial Service Industry
Published in Jithesh Sathyan, Anoop Narayanan, Navin Narayan, K V Shibu, A Comprehensive Guide to Enterprise Mobility, 2016
Jithesh Sathyan, Anoop Narayanan, Navin Narayan, K V Shibu
In scenarios that include field operations where there is no real-time connectivity with the enterprise network, the thick client mobile applications can store the data locally on the device and can be synchronized later with the enterprise database when a network connection is available. A custom-developed or off-the-shelf data synchronization middleware can be used for syncing locally stored data with the enterprise system.
Cloud-based fleet management for prefabrication transportation
Published in Enterprise Information Systems, 2019
Gangyan Xu, Ming Li, Lizi Luo, Chun-Hsien Chen, George Q. Huang
To well address the above issues, this paper proposed a cloud-based fleet management platform for prefabrication transportation through integrating IoT and cloud technology. By visualizing all the transportation resources into the cloud, this solution enables the pay-per-use mode that transportation companies could dynamically access and configure the services to satisfy their own management activities and fulfill the diverse project requirements. Meanwhile, to further lower the implementation cost on data collection while ensuring sufficient real-time information during the transportation processes, relation-based data extraction method is proposed. Furthermore, a data synchronization mechanism is worked out to guarantee the data consistency among stakeholders under different network connection environments.
Contextual self-organizing of manufacturing process for mass individualization: a cyber-physical-social system approach
Published in Enterprise Information Systems, 2020
Jiewu Leng, Pingyu Jiang, Chao Liu, Chuang Wang
From the implementation perspective, this approach stores the key machining information on the SW data-tag in a legible format, which can eliminate the process of data retrieving procedure and ensure the production to run smoothly in case of server failing. The decentralized data-tag makes the workpiece become a smart one which proactively communicates with SMs to make local decision, which avoids the data synchronization between the tag and the centralized database by on-tag index of further manufacturing information, and thus can simplify the up-level management.
Systematic Framework toward a Highly Reliable Approach in Metal Accounting
Published in Mineral Processing and Extractive Metallurgy Review, 2022
Yousef Ghorbani, Glen T. Nwaila, Munyar Chirisa
Metal accounting processes are applied variably by mining companies for the purpose of quantifying metal inventories and financial reporting statements. Comparison of the various existing metal accounting procedures, combined with a case study of a gold mine, reveals that the current practices provide a number of important metal accounting procedures. However, a fundamental change in the approach toward data integrated and auditable metal accounting system is long overdue. This change is reflected in our proposal for a more accurate and reliable systematic framework of the metal accounting of salable metals. Our proposed metal accounting framework includes various mass balance-risk drivers, aspects of operational measurements and data reliability, effects of plant configuration, data integration and security, corporate governance dynamics, and reporting of salable metals. The proposed approach considers the use of the currently available and future-oriented development in technological development (both hardware and software) in mineral processing and extractive metallurgy. Implementation will lead to better plant process control, transparent financial reporting, and an adequate understanding of interrelationships between different process activities. In addition, quantification of metal inventories can be more accurately constrained, and risks such as material misstatement can be identified with the aid of metallurgical and metal accounting information management system. We have recognized the need for a proactive design where metal accounting sampling has to be factored during the plant equipment design stages. This, in turn, will help ensure that metal accounting sampling is done as per the first principles that are based on the nature and composition of the ore. The use of MMAIMS will eliminate data security risks associated with multiple-user environment spreadsheets, create consistency in data used for decision-making and operation data synchronization. End to end metal accounting process visibility across the entire process will improve the audibility and transparency of metal accounting from mine to product, and facilitate good corporate governance and financial reporting of salable metal production and sales. Naturally, with any system, there are uncertainties in the data acquired which could affect the precision of the analyses. Additional delimiting factors may include conditional biases, adequate measures, such as data cross-referencing/replication and routine metal accounting system audits can help to ensure the quality of the data and ultimately assist in better reporting of salable metals.