This paper applies the perspective of business ecosystem to mobile communications industry,trying to help mobile network operators improve their strategies in the era of the third generation mobile communications(3G)....This paper applies the perspective of business ecosystem to mobile communications industry,trying to help mobile network operators improve their strategies in the era of the third generation mobile communications(3G).According to the definition of the business ecosystem,the ecosystem structure of mobile network operators is analyzed.As an important hub in the ecosystem,mobile network operators are advised to take a keystone strategy.The key points of the strategy are summarized.Finally,suggestions for Chinese mobile network operators are given based on the analysis.展开更多
Recently,various mobile apps have included more features to improve user convenience.Mobile operating systems load as many apps into memory for faster app launching and execution.The least recently used(LRU)-based ter...Recently,various mobile apps have included more features to improve user convenience.Mobile operating systems load as many apps into memory for faster app launching and execution.The least recently used(LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low.However,the LRUbased cached app termination does not distinguish between frequently or infrequently used apps.The app launch performance degrades if LRU terminates frequently used apps.Recent studies have suggested the potential of using users’app usage patterns to predict the next app launch and address the limitations of the current least recently used(LRU)approach.However,existing methods only focus on predicting the probability of the next launch and do not consider how soon the app will launch again.In this paper,we present a new approach for predicting future app launches by utilizing the relaunch distance.We define the relaunch distance as the interval between two consecutive launches of an app and propose a memory management based on app relaunch prediction(M2ARP).M2ARP utilizes past app usage patterns to predict the relaunch distance.It uses the predicted relaunch distance to determine which apps are least likely to be launched soon and terminate them to improve the efficiency of the main memory.展开更多
文摘This paper applies the perspective of business ecosystem to mobile communications industry,trying to help mobile network operators improve their strategies in the era of the third generation mobile communications(3G).According to the definition of the business ecosystem,the ecosystem structure of mobile network operators is analyzed.As an important hub in the ecosystem,mobile network operators are advised to take a keystone strategy.The key points of the strategy are summarized.Finally,suggestions for Chinese mobile network operators are given based on the analysis.
基金This work was supported in part by the National Research Foundation of Korea(NRF)Grant funded by the Korea Government(MSIT)under Grant 2020R1A2C100526513in part by the R&D Program for Forest Science Technology(Project No.2021338C10-2323-CD02)provided by Korea Forest Service(Korea Forestry Promotion Institute).
文摘Recently,various mobile apps have included more features to improve user convenience.Mobile operating systems load as many apps into memory for faster app launching and execution.The least recently used(LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low.However,the LRUbased cached app termination does not distinguish between frequently or infrequently used apps.The app launch performance degrades if LRU terminates frequently used apps.Recent studies have suggested the potential of using users’app usage patterns to predict the next app launch and address the limitations of the current least recently used(LRU)approach.However,existing methods only focus on predicting the probability of the next launch and do not consider how soon the app will launch again.In this paper,we present a new approach for predicting future app launches by utilizing the relaunch distance.We define the relaunch distance as the interval between two consecutive launches of an app and propose a memory management based on app relaunch prediction(M2ARP).M2ARP utilizes past app usage patterns to predict the relaunch distance.It uses the predicted relaunch distance to determine which apps are least likely to be launched soon and terminate them to improve the efficiency of the main memory.
