Object-oriented application frameworks
Communications of the ACM
The computer for the 21st century
ACM SIGMOBILE Mobile Computing and Communications Review - Special issue dedicated to Mark Weiser
Data Warehousing, Data Mining, and Olap
Data Warehousing, Data Mining, and Olap
RHist: adaptive summarization over continuous data streams
Proceedings of the eleventh international conference on Information and knowledge management
Challenges in Ubiquitous Data Management
Informatics - 10 Years Back. 10 Years Ahead.
An architecture for privacy-sensitive ubiquitous computing
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Human-Computer Interaction
Context knowledge discovery in ubiquitous computing
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems
Towards summarized representation of time series data in pervasive computing systems
UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
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Typical ubiquitous computing environments contain a large number of data sources, in the form of sensors and infrastructure elements, emitting a huge amount of contextual data (called context) continuously that need to be processed and stored in some context repository. Usually, this data is for software system’s internal use to provide proactive services. Hence, it makes sense not to store this entire huge amount of data but to identify and remove some irrelevant data (garbage collecting context), summarize the left over and only store this summarized and more meaningful data. We believe that such a summarization will result in improved performance in query processing, data retrieval, knowledge reasoning and machine learning. Besides, it will also save the storage space required to store context repository. In this paper, we will present the idea and motivation behind context summarization and garbage collecting context and some possible techniques to achieve this.