A Graph API for Complex Business Network Query and Traversal

Author(s):  
Daniel Ritter ◽  
Christoph Herrmann
Keyword(s):  
Author(s):  
Henry M. Franken ◽  
Rene Bal ◽  
Harmen van den Berg ◽  
Wil Janssen ◽  
Henny de Vos

Author(s):  
Hamsa Maan Mohammed Hamsa Maan Mohammed

Any project consists of a set of interconnected and interrelated activities in a specific order that are carried out at a specific time. The size of the projects, their high costs, and the complexity of their activities made it necessary to make a careful and prior planning. From here came the idea of ​​business networks, where scientific analysis was adopted for project planning, scheduling and reviewing by representing these projects with a network that shows the sequence of their activities at appropriate costs and times. Since such networks need time and effort to implement, the researcher used one of the smart techniques (the weed algorithm) and applied it to some business network issues that require great time and effort that increase as the size of the project increases. By applying the algorithm to some of these issues, it succeeded in achieving the required results in a record time (a few minutes) and according to the size of the issue, and hardly a little effort, in the first issue, the results (the expected time for each activity, determining the critical path, calculating the time needed to complete the critical path) achieved results in approximately one minute, and the second issue took less than two minutes. As for the third issue, the results were given in approximately two and a half minutes. Thus this smart technology has achieved the desired results in the least possible time and effort. Such a technique can be used and implemented on problems in different fields due to its accuracy, efficiency and speed in solving problems.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Di Qu ◽  
Dianya Deng

Currently, the development of sharing economy and interconnection also has a profound impact on community life services. This study is based on the deep neural network theory, combined with the evolution mechanism of the commercial network of the community life service industry, link prediction theory, and the latest deep neural network algorithm, referring to the evolution model of merger and stripping, and the network structure is optimized on this basis. Through simulation experiments and result analysis, the model is used to deeply study the evolution trend and dynamics of the community life service business network from the perspective of quantitative analysis. Then the business network structure is optimized and development is promoted at the same time. At the same time, it can also upgrade those old scattered industries and provide theoretical and decision-making guidance for the future transformation and upgrading of the innovative community life service industry.


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