Sensor data is structured and generally lacks of meaning by itself, but life-logging data (time, location, etc.) out of sensor data can be utilized to create lots of meaningful information combined with social data from social networks like Facebook and Twitter. There have been many platforms to produce meaningful information and support human behavior and context-awareness through integrating diverse mobile, social, and sensing input streams. The problem is that these platforms don’t guarantee the performance in terms of the processing time and even let the accuracy of output data be addressed by new studies in each area where the platform is applied. Thus, this study proposes an improved platform which builds a knowledge base for context awareness by applying distributed and parallel computing approach considering the characteristics of sensor data that is collected and processed in real-time, and compares the proposed platform with existing platforms in terms of performance. The experiment shows the proposed platform is an advanced platform in terms of processing time. We reduce the processing time by 40% compared with existing platform. The proposed platform also guarantees the accuracy compared with existing platform.
Sensor data; Context awareness; Knowledge extraction; Distributed and parallel computing; Machine learning; Knowledge base
International journal of distributed sensor networks