Recognizing Information Feature Variation: Message Importance Transfer Measure and Its Applications in Big Data

Fan

2019

Self Cite

Different probabilities of events attract different attention in many scenarios such as anomaly detection and security systems. To characterize the events’ importance from a probabilistic perspective, the message importance measure (MIM) is proposed as a kind of semantics analysis tool. Similar to Shannon entropy, the MIM has its special function in information representation, in which the parameter of MIM plays a vital role. Actually, the parameter dominates the properties of MIM, based on which the MIM has three work regions where this measure can be used flexibly for different goals. When the parameter is positive but not large enough, the MIM not only provides a new viewpoint for information processing but also has some similarities with Shannon entropy in the information compression and transmission. In this regard, this paper first constructs a system model with message importance measure and proposes the message importance loss to enrich the information processing strategies. Moreover, the message importance loss capacity is proposed to measure the information importance harvest in a transmission. Furthermore, the message importance distortion function is discussed to give an upper bound of information compression based on the MIM. Additionally, the bitrate transmission constrained by the message importance loss is investigated to broaden the scope for Shannon information theory.

Section: Bitrate Transmission Constrained By Message Importancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Attention to the Variation of Probabilistic Events: Information Processing with Message Importance Measure

Sheng

Fan

2019

Self Cite

“…It is the same with recommendation value since lim ̟→+∞ u min e ̟(1−u min ) N i=1 p i e ̟(1−u i ) = 1 (cf. (36)). Furthermore, if ̟ is not a very large positive number, the form of f (̟, x, P ) (1 < x < N) is similar with Shannon entropy, which is discussed in [39].…”

Section: B Optimal Recommendation and Mimmentioning

confidence: 99%

Matching Users’ Preference under Target Revenue Constraints in Data Recommendation Systems

Dong

Fan

et al. 2019

Self Cite

This paper focuses on the problem of finding a particular data recommendation strategy based on the user preferences and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a certain revenue constraint. In fact, the optimal recommendation distribution is the one that is the closest to the utility distribution in the sense of relative entropy and satisfies expected revenue. We show that the optimal recommendation distribution follows the same form as the message importance measure (MIM) if the target revenue is reasonable, i.e., neither too small nor too large. Therefore, the optimal recommendation distribution can be regarded as the normalized MIM, where the parameter, called importance coefficient, presents the concern of the system and switches the attention of the system over data sets with different occurring probability. By adjusting the importance coefficient, our MIM based framework of data recommendation can then be applied to system with various system requirements and data distributions. Therefore, the obtained results illustrate the physical meaning of MIM from the data recommendation perspective and validate the rationality of MIM in one aspect.

“…In order to depict the message importance in small-probability event scenarios, message importance measure (MIM) was proposed in Reference [ 30 ]. Furthermore, MIM is fairly effective in many applications of big data, such as IoT [ 31 ], mobile edge computing [ 32 ]. In addition, Reference [ 33 ] expanded MIM to the general case, and it presented that MIM can be adopted as a special weight in designing the recommendation system.…”

Section: Introductionmentioning

confidence: 99%

Storage Space Allocation Strategy for Digital Data with Message Importance

Sheng

Zhu

et al. 2020

Self Cite

This paper mainly focuses on the problem of lossy compression storage based on the data value that represents the subjective assessment of users when the storage size is still not enough after the conventional lossless data compression. To this end, we transform this problem to an optimization, which pursues the least importance-weighted reconstruction error in data reconstruction within limited total storage size, where the importance is adopted to characterize the data value from the viewpoint of users. Based on it, this paper puts forward an optimal allocation strategy in the storage of digital data by the exponential distortion measurement, which can make rational use of all the storage space. In fact, the theoretical results show that it is a kind of restrictive water-filling. It also characterizes the trade-off between the relative weighted reconstruction error and the available storage size. Consequently, if a relatively small part of total data value is allowed to lose, this strategy will improve the performance of data compression. Furthermore, this paper also presents that both the users’ preferences and the special characteristics of data distribution can trigger the small-probability event scenarios where only a fraction of data can cover the vast majority of users’ interests. Whether it is for one of the reasons above, the data with highly clustered message importance is beneficial to compression storage. In contrast, from the perspective of optimal storage space allocation based on data value, the data with a uniform information distribution is incompressible, which is consistent with that in the information theory.