China 
Africa 
Explore chapters and articles related to this topic
Data-to-decision framework for monitoring railroad bridges
Published in Khaled M. Mahmoud, Asset Management of Bridges, 2017
S. Alampalli, S. Alampalli, M. Ettouney, J.P. Lynch
In this project, given the heterogeneity of data, many alternative designs were considered to store information with the key requirements for the system being scalability, consistency, and usability. Finally, Microsoft SQL Server—a relational database—has been used as a reliable data repository. The relational database links structural analytical models to physical bridge components; sensors to physical bridge components; and results to related components. The created database schema is structured and normalized to ensure that data are stored efficiently and optimized for highest performance. The design provides the capability to disclose knowledge and reliable hidden patterns; crosscheck various datasets; and validate and uncover relationships within data. For example, visual inspection constitutes a major basis for decisions regarding the performance of bridges. So, the visual inspection data is linked with sensor data in order to provide information regarding limit states of bridges below failure through different ratings.
Postulate satisfaction for inconsistency measures in monotonic logics and databases
Published in Journal of Applied Non-Classical Logics, 2023
Let be an MIS bigraph where and . We construct a database schema DS and a database D for which . We need only a single relation symbol R. Let . We specify the arity of R as . D and C are constructed together. R will have m tuples, written , where corresponds to for . The entry of , that is, the value for attribute j is written as . Without loss of generality we assume that V is given in such a way that the elements of (if any) are written at the end of the list. These represent the MISs of size 1.
Smart waste management system for decision makers by using smart bins - a case study for an Australian municipality
Published in Australian Journal of Civil Engineering, 2022
Yinan An, Jing Qiu, Z. Y. Dong
Database schema of this system: id INT(11) is the unique serial number of the bin.lat DOUBLE is the Latitude of the bin. By using DOUBLE as the date type will give a 3.5 nm precision.lng DOUBLE is the Longitude of the bin.desc VARCHAR(255) is the description of the bin.address VARCHAR(100) is the physical address of the bin.age_threshold INT(11) is the age threshold of the bin.fullness_threshold INT(11) is the indicator when it becomes full and need action.timestamp DATE records the date of the data generated.status ENUM(‘0ʹ, ‘1ʹ) is the status of the bin.
A computational framework for social-media-based business analytics and knowledge creation: empirical studies of CyTraSS
Published in Enterprise Information Systems, 2021
Wingyan Chung, Elizabeth Mustaine, Daniel Zeng
The framework consists of the steps of intelligence gathering, sentiment and network analytics, and temporal analysis and visualization. Figure 1 shows these steps and the process of gathering intelligence from social media, extracting emotion and sentiment, identifying networks, and temporal analysis and visualization of user activities and networks. Social media data are collected from publicly available SM sites, application programming interfaces provided by these sites, or commercial services that supply SM data. The framework includes a database schema and a pipeline that can be used to pre-process and store the SM data into a structured format (e.g. relational database). The pre-processing includes filtering SM posts that do not match the selected query terms (to be provided by system developer) or user-defined filtering rules. The following describes these steps in the context of building CyTraSS (Cyber-trafficking Surveillance System).