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BIM-based model checking in a business process management environment
Published in Jan Karlshøj, Raimar Scherer, eWork and eBusiness in Architecture, Engineering and Construction, 2018
P.N. Gade, R. Hansen, K. Svidt
This web service makes it possible for Bizagi Studio to request data contained in the BIM server-hosted IFC-models. We chose to implement a piece of middleware because the structure of the data exposed from BIMservers own web service implementation was unfit for direct integration with Bizagi Studio. Therefore, the custom web service acts as a data scrubbing middleware, which solves the structural mapping issues between Bizagi Studio and BIMerver data structures. The data scrubbing web service was implemented using Microsoft ASP.NET Web API 2.0, written in C#. The actual scrubbing of the JSON formatted BIM data consumed from BIMserver was done manually by querying BIMservers’ API and using Newtonsoft.Json (de)serialization library. The main work of the web service is the querying for BIMserver data, deserializing the data and reconstructing it in an easily readable format, which is then serialized again, and exposed as JSON to Bizagi Studio.
Medicine and Pharmaceuticals Biomanufacturing – Industry 5.0
Published in Pau Loke Show, Kit Wayne Chew, Tau Chuan Ling, The Prospect of Industry 5.0 in Biomanufacturing, 2021
Zahra Nashath, Doris Ying Ying Tang, Kit Wayne Chew, Pau Loke Show
Despite the impact and benefits of Industry 5.0 to the pharmaceutical industry, this evolution brings with it several challenges, primarily ethical issues, as technology becomes more intertwined with human lives. Additionally, the use of AI technology in data processing requires further development into synchronized storage systems, data security and automated data scrubbing systems. In short, while there are several challenges and issues still to be resolved before the transition to Industry 5.0, it is clear that it will lead to the advancement of the medical and pharmaceutical industry, particularly in the manufacturing stages, bringing more benefits to humans through machine interactions and AI and in the creation of optimized production lines.
Identifying Valuable Candidates for Project Lessons Learned
Published in Mel Bost, Project Management Lessons Learned, 2018
These were as follows: Old data cleanupData scrubbingData translationData compatibility with new application
DNS rule-based schema to botnet detection
Published in Enterprise Information Systems, 2021
Kamal Alieyan, Ammar Almomani, Mohammed Anbar, Mohammad Alauthman, Rosni Abdullah, B. B. Gupta
Data Cleansing: This process is also called data scrubbing. It may be also called data cleaning. This stage aims at detecting and removing the errors lying within the datasets. It also aims at discarding the conflicting instances inside the datasets.