Overcoming the challenge of variety: big data abstraction, the next evolution of data management for AAL communication systems

Mao, Rui; Xu, Honglong; Wu, Wenbo; Li, Jianqiang; Li, Yan; Lu, Minhua

doi:10.1109/mcom.2015.7010514

Cited by 63 publications

(29 citation statements)

References 6 publications

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“…(iii) Update the reputation of each user according to (3) and (4) for IR, (5)- (7) for CR, (8) for IARR methods, and (10) and (8) for IARR2, respectively. (iv) Continue the iteration process according to (ii) and (iii) until the change of the quality estimates ∑ ∈ ( ( ) − ( − 1)) is less than a threshold , and then terminate the iteration.…”

Section: Iterative Ranking Frameworkmentioning

confidence: 99%

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Eliminating the Effect of Rating Bias on Reputation Systems

Ren

et al. 2018

Complexity

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The ongoing rapid development of the e-commercial and interest-base websites makes it more pressing to evaluate objects' accurate quality before recommendation. The objects' quality is often calculated based on their historical information, such as selected records or rating scores. Usually high quality products obtain higher average ratings than low quality products regardless of rating biases or errors. However, many empirical cases demonstrate that consumers may be misled by rating scores added by unreliable users or deliberate tampering. In this case, users' reputation, that is, the ability to rate trustily and precisely, makes a big difference during the evaluation process. Thus, one of the main challenges in designing reputation systems is eliminating the effects of users' rating bias. To give an objective evaluation of each user's reputation and uncover an object's intrinsic quality, we propose an iterative balance (IB) method to correct users' rating biases. Experiments on two datasets show that the IB method is a highly self-consistent and robust algorithm and it can accurately quantify movies' actual quality and users' stability of rating. Compared with existing methods, the IB method has higher ability to find the "dark horses," that is, not so popular yet good movies, in the Academy Awards.

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Section: Iterative Ranking Frameworkmentioning

confidence: 99%

“…The fast development of the Internet and related infrastructures has created vast opportunities for people to date, read, shop, and enjoy entertainment online [1][2][3]. As people come to rely more and more on the Internet, they place themselves at additional risk.…”

Section: Introductionmentioning

confidence: 99%

Eliminating the Effect of Rating Bias on Reputation Systems

Ren

et al. 2018

Complexity

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“…Semantic region estimation approach is associated with last state, because the model of particle fi lter is based on last collection according to (4). Therefore, according to (9), the sum of probabilities in last semantic region would be larger than that in just entering semantic region.…”

Section: Kidnapped Robot Experimentsmentioning

confidence: 99%

“…For AAL (Ambient Assisted Living) [4], spatial semantic in known maps has drawn much attention for assistant robot. However, spatial semantic for the function of rooms like "dining or living room" are inadequate, since the tasks of those robots generally need specifstored data and are considered more reasonable in the region recognition.…”

Section: Introductionmentioning

confidence: 99%

Semantic region estimation of assistant robot for the elderly long-term operation in indoor environment

Huo

Zhao

et al. 2016

China Commun.

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“…In stead, this paper focuses on the algorithms for healthcare-related applications. Many works have been done on health sensing [53] [49] [32], a common feature of these algorithms is that they generally involve a data processing, feature extraction and a judgement process. They use the inertial sensor sequence as input, and output the diagnosis result or alarms of emergency.…”

Section: Characteristics Of Mems Inertial Sensorsmentioning

confidence: 99%