Recognizing Information Feature Variation: Message Importance Transfer Measure and Its Applications in Big Data
Information transfer that characterizes the information feature variation can have a crucial impact on big data analytics and processing. Actually, the measure for information transfer can reflect the system change from the statistics by using the variable distributions, similar to Kullback-Leibler...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-05-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/20/6/401 |
| _version_ | 1818015533092569088 |
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| author | Rui She Shanyun Liu Pingyi Fan |
| author_facet | Rui She Shanyun Liu Pingyi Fan |
| author_sort | Rui She |
| collection | DOAJ |
| description | Information transfer that characterizes the information feature variation can have a crucial impact on big data analytics and processing. Actually, the measure for information transfer can reflect the system change from the statistics by using the variable distributions, similar to Kullback-Leibler (KL) divergence and Renyi divergence. Furthermore, to some degree, small probability events may carry the most important part of the total message in an information transfer of big data. Therefore, it is significant to propose an information transfer measure with respect to the message importance from the viewpoint of small probability events. In this paper, we present the message importance transfer measure (MITM) and analyze its performance and applications in three aspects. First, we discuss the robustness of MITM by using it to measuring information distance. Then, we present a message importance transfer capacity by resorting to the MITM and give an upper bound for the information transfer process with disturbance. Finally, we apply the MITM to discuss the queue length selection, which is the fundamental problem of caching operation on mobile edge computing. |
| first_indexed | 2024-04-14T06:58:30Z |
| format | Article |
| id | doaj.art-c962f54094ce4553874321469efe53e5 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-14T06:58:30Z |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-c962f54094ce4553874321469efe53e52022-12-22T02:06:49ZengMDPI AGEntropy1099-43002018-05-0120640110.3390/e20060401e20060401Recognizing Information Feature Variation: Message Importance Transfer Measure and Its Applications in Big DataRui She0Shanyun Liu1Pingyi Fan2Department of Electronic Engineering, Tsinghua University, Beijing 30332, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 30332, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 30332, ChinaInformation transfer that characterizes the information feature variation can have a crucial impact on big data analytics and processing. Actually, the measure for information transfer can reflect the system change from the statistics by using the variable distributions, similar to Kullback-Leibler (KL) divergence and Renyi divergence. Furthermore, to some degree, small probability events may carry the most important part of the total message in an information transfer of big data. Therefore, it is significant to propose an information transfer measure with respect to the message importance from the viewpoint of small probability events. In this paper, we present the message importance transfer measure (MITM) and analyze its performance and applications in three aspects. First, we discuss the robustness of MITM by using it to measuring information distance. Then, we present a message importance transfer capacity by resorting to the MITM and give an upper bound for the information transfer process with disturbance. Finally, we apply the MITM to discuss the queue length selection, which is the fundamental problem of caching operation on mobile edge computing.http://www.mdpi.com/1099-4300/20/6/401information transfer measuresmall probability eventsbig data analysis and processingmobile edge computing (MEC)queue theory |
| spellingShingle | Rui She Shanyun Liu Pingyi Fan Recognizing Information Feature Variation: Message Importance Transfer Measure and Its Applications in Big Data Entropy information transfer measure small probability events big data analysis and processing mobile edge computing (MEC) queue theory |
| title | Recognizing Information Feature Variation: Message Importance Transfer Measure and Its Applications in Big Data |
| title_full | Recognizing Information Feature Variation: Message Importance Transfer Measure and Its Applications in Big Data |
| title_fullStr | Recognizing Information Feature Variation: Message Importance Transfer Measure and Its Applications in Big Data |
| title_full_unstemmed | Recognizing Information Feature Variation: Message Importance Transfer Measure and Its Applications in Big Data |
| title_short | Recognizing Information Feature Variation: Message Importance Transfer Measure and Its Applications in Big Data |
| title_sort | recognizing information feature variation message importance transfer measure and its applications in big data |
| topic | information transfer measure small probability events big data analysis and processing mobile edge computing (MEC) queue theory |
| url | http://www.mdpi.com/1099-4300/20/6/401 |
| work_keys_str_mv | AT ruishe recognizinginformationfeaturevariationmessageimportancetransfermeasureanditsapplicationsinbigdata AT shanyunliu recognizinginformationfeaturevariationmessageimportancetransfermeasureanditsapplicationsinbigdata AT pingyifan recognizinginformationfeaturevariationmessageimportancetransfermeasureanditsapplicationsinbigdata |