Intelligent bridge management via big data knowledge engineering

2022 ◽  
Vol 135 ◽  
pp. 104118
Author(s):  
Jianxi Yang ◽  
Fangyue Xiang ◽  
Ren Li ◽  
Luyi Zhang ◽  
Xiaoxia Yang ◽  
...  
Author(s):  
Piyush Kumar Shukla ◽  
Madhuvan Dixit

In this chapter, Big Data provide large-volume, complex structure, heterogeneous and irregular growing data sets include multiple and autonomous different resources. In this chapter, With the growing improvement of networking sites, image information storing capacity become big issue too, Big Data concept are most growing expanding in all technical area and knowledge engineering domains, including physical, medical and paramedical sciences. Here a data-driven method consist demand-driven aggregation of information and knowledge mining and analysis, user interest prototyping, security and privacy aspects has been presented.


Author(s):  
Robert Laurini

For millennia, spatial planning has been based on human knowledge about the context and its environment together with some objectives of development. Now, with artificial intelligence and especially knowledge engineering, practices of spatial planning can be renovated. Presently, novel practices can be designed. In addition to human collective knowledge, some new chunks of knowledge can be introduced, coming from physical laws, administrative regulations, standards, data mining, and best practices. By big data analytics, some regularities and patterns can be discovered, which again will lead to new actions towards cities: in other words, there is a virtuous circle linking smart territories and big data that can be the basis for novel spatial planning. The role of this chapter will be to analyze those new chunks of knowledge and to explain how human knowledge, possibly coming from different stakeholders, can be harmonized with machine-processable knowledge as to be the basis for territorial intelligence.


2016 ◽  
Vol 4 (1) ◽  
Author(s):  
M. Taimoor Khan ◽  
Mehr Durrani ◽  
Shehzad Khalid ◽  
Furqan Aziz

Clustering is one of the relevant knowledge engineering methods of data analysis. The clustering method will automatically directly affect the result dataset. The proposed work aims at developing an Extended Advanced Method of Clustering (EAMC) to address numerous types of issues associated to large and high dimensional dataset. The proposed Extended Advance Method of clustering will repetitively avoid computational time between each data cluster object contained by the cluster that saves execution time in term. For each iteration EAMC needs a data structure to store data that can be utilized for the next iteration. We have gained outcomes from the proposed method, which demonstrates that there is an improvement in effectiveness and pace of clustering and precision generation, which will decrease the convolution of computing over the old algorithms like SOM, HAC, and K-means. This paper includes EAMC and the investigational outcomes done using academic datasets


IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 12696-12701 ◽  
Author(s):  
Xindong Wu ◽  
Huanhuan Chen ◽  
Jun Liu ◽  
Gongqing Wu ◽  
Ruqian Lu ◽  
...  

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