People, places, things: web presence for the real world
Mobile Networks and Applications
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
LANDMARC: indoor location sensing using active RFID
Wireless Networks - Special issue: Pervasive computing and communications
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Random walk with restart: fast solutions and applications
Knowledge and Information Systems
Web page classification: Features and algorithms
ACM Computing Surveys (CSUR)
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Find me if you can: improving geographical prediction with social and spatial proximity
Proceedings of the 19th international conference on World wide web
Ubiquitous RFID: Where are we?
Information Systems Frontiers
On the semantic annotation of places in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
20 Years Past Weiser: What's Next?
IEEE Pervasive Computing
Facilitating Efficient Object Tracking in Large-Scale Traceability Networks
The Computer Journal
| Hi-index | 0.00 |
With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web services, physical things are becoming an integral part of the emerging ubiquitous Web. While this integration offers many exciting opportunities such as efficient supply chains and improved environmental monitoring, it also presents many significant challenges. One such challenge lies in how to classify, discover, and manage ubiquitous things, which is critical for efficient and effective object search, recommendation, and composition. In this paper, we focus on automatically classifying ubiquitous things into manageable semantic category labels by exploiting the information hidden in interactions between users and ubiquitous things. We develop a novel approach to extract latent relevance by building a relational network of ubiquitous things (RNUbiT) where similar things are linked via virtual edges according to their latent relevance. A discriminative learning algorithm is also developed to automatically determine category labels for ubiquitous things. We conducted experiments using real-world data and the experimental results demonstrate the feasibility and validity of our proposed approach.