Lifelog Data Model and Management: Study on Research Challenges

Abstract

Utilizing a computer to manage an enormous amount of information like lifelogs needs concrete digitized data models on information sources and their connections. For lifelogging, we need to model one’s life in a way that a computer can translate and manage information where many research efforts are still needed to close the gap between real life models and computerized data models. This work studies a fundamental lifelog data modeling method from a digitized information perspective that translates real life events into a composition of digitized and timestamped data streams. It should be noted that a variety of events occurred in one’s real life can’t be fully captured by limited numbers and types of sensors. It is also impractical to ask a user to manually tag entire events and their minute detail relations. Thus we aim to develop the lifelog management system architecture and service structures for people to facilitate mapping a sequence of sensor streams with real life activities. Technically we focus on time series data modeling and management as the first step toward lifelog data fusion and complex event detection.