Adaptive data dissemination schemes for location-aware mobile services

2006 ◽  
Vol 79 (5) ◽  
pp. 674-688 ◽  
Author(s):  
KwangJin Park ◽  
MoonBae Song ◽  
Chong-Sun Hwang
Keyword(s):  
Data Dissemination ◽  
Mobile Services ◽  
Location Aware

Efficient Processing of Continual Range Queries for Location-Aware Mobile Services

2005 ◽  
Vol 7 (4-5) ◽  
pp. 435-448 ◽  
Author(s):  
Kun-Lung Wu ◽  
Shyh-Kwei Chen ◽  
Philip S. Yu
Keyword(s):  
Mobile Services ◽  
Range Queries ◽  
Location Aware ◽  
Efficient Processing

scholarly journals From Reputation Perspective: A Hybrid Matrix Factorization for QoS Prediction in Location-Aware Mobile Service Recommendation System

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Shun Li ◽  
Junhao Wen ◽  
Xibin Wang
Keyword(s):  
Matrix Factorization ◽  
Recommendation System ◽  
Mobile Services ◽  
End Users ◽  
Mobile Service ◽  
Service Recommendation ◽  
Qos Prediction ◽  
Hybrid Matrix ◽  
Location Aware ◽  
The Impact

With the great development of mobile services, the Quality of Services (QoS) becomes an essential factor to meet end users’ personalized requirement on the nonfunctional performance of mobile services. However, most of the QoS values in real cases are unattainable because a service user would only invoke some specific mobile services. Therefore, how to predict the missing QoS values and recommend high-quality services to end users becomes a significant challenge in mobile service recommendation research. Previous QoS prediction researches demonstrate that the nonfunctional performance of mobile services is closely related to users’ location information. However, most location-aware QoS prediction methods ignore the premise that the obtainable QoS values observed by different users in same location region would probably be untrustworthy, which will lead to inaccurate and unreliable prediction results. To make credible location-aware QoS prediction, we propose a hybrid matrix factorization method integrated location and reputation information (LRMF) to predict the unattainable QoS values. Our approach firstly cluster users into different locational region based on their geographical distribution, and then we compute users’ reputation to identify untrustworthy users in every locational region. Finally, the unknown QoS values can be predicted by integrating locational cluster information and users’ reputation into a hybrid matrix factorization model. Comprehensive experiments are conducted on a public QoS dataset which contains sufficient real-world service invocation records. The evaluation results indicate that our LRMF method can effectively reduce the impact of unreliable users on QoS prediction and make credible mobile service recommendation.

Scalable Spatial Query Processing for Location-Aware Mobile Services

2005 ◽  
pp. 715-724
Author(s):  
KwangJin Park ◽  
MoonBae Song ◽  
Ki-Sik Kong ◽  
Chong-Sun Hwang ◽  
Kwang-Sik Chung ◽  
...  
Keyword(s):  
Query Processing ◽  
Mobile Services ◽  
Spatial Query ◽  
Spatial Query Processing ◽  
Location Aware
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