Abstract: An example topology construction method for satisfying partition tolerance, comprising: combining the consortium blockchain consensus mechanism with the network topology structure to make the consortium blockchain consensus satisfy the partition tolerance in probability; abstracting the partition tolerance of a system into a class of convergent Markov process and computing a steady-state probability of the system; estimating the probability and the average minimum repair time of failing to meet consistency or availability in the event of a partition failure with a given number of failure channels, and a partition tolerance probability and an average minimum repair time of the system are obtained; and analyzing the resource overhead and the partition tolerance under different network topologies according to the obtained partition tolerance probability and the average minimum repair time, and constructing the network topology structure with suitable scale and high partition tolerance for the consortium blockcha
Type:
Application
Filed:
March 21, 2019
Publication date:
July 18, 2019
Applicants:
PEKING UNIVERSITY SHENZHEN GRADUATE SCHOOL, CHINA NATIONAL DIGITAL SWITCHING SYSTEM ENGINEERING & TECHNOLOGICAL R&D CENTER, SHENZHEN CESTBON TECHNOLOGY CO. LIMITED
Abstract: An example method includes: identifying message labels for electronic messages; identifying, based on a classification model specific to a first user, a first and a second message labels for a first and a second electronic messages; detecting a user action by the first user on the second electronic message to indicate the first message label is descriptive of the second electronic message; responsive to the user action, re-training the classification model based on tokens produced from the second electronic message to produce an updated classification model specific to the first user; after re-training is completed, detecting an incoming electronic message having a timestamp later in time than timestamps for the first and the second electronic messages; determining that the incoming electronic message shares a predefined number of tokens with the second electronic message; and assigning, based on the updated classification model, the first message label to the incoming electronic message.